Release

.dockerignore

@@ -1,18 +0,0 @@
-bin/
-lib/
-share/
-etc/
-include/
-pyvenv.cfg
-.idea/
-.vscode/
-__pycache__/
-*.pyc
-.git/
-runs/
-dataset/
-NFS/
-*.pth
-*.pkl
-*.npy
-.env

Dockerfile

@@ -1,25 +0,0 @@
-FROM pytorch/pytorch:2.2.1-cuda12.1-cudnn8-runtime
-
-ENV PYTHONDONTWRITEBYTECODE=1
-ENV PYTHONUNBUFFERED=1
-
-WORKDIR /app
-
-# System dependencies for OpenCV and image processing
-RUN apt-get update && apt-get install -y \
-    libglib2.0-0 \
-    libsm6 \
-    libxext6 \
-    libxrender-dev \
-    && rm -rf /var/lib/apt/lists/*
-
-# Install python packages
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
-
-# Copy source code
-COPY src/ /app/src/
-
-EXPOSE 8080
-
-CMD ["streamlit", "run", "src/main.py", "--server.port", "8080", "--server.address", "0.0.0.0"]

Makefile

@@ -1,20 +0,0 @@
-.PHONY: up down logs restart status
-
-# Сборка и запуск контейнеров в фоновом режиме
-up:
-	docker compose up --build -d
-
-# Остановка и удаление контейнеров
-down:
-	docker compose down
-
-# Просмотр логов в реальном времени
-logs:
-	docker compose logs -f
-
-# Быстрый перезапуск
-restart: down up
-
-# Проверка статуса
-status:
-	docker compose ps

docker-compose.yml

@@ -1,63 +0,0 @@
-version: '3.8'
-
-networks:
-  emom_mesh:
-    driver: bridge
-
-services:
-  emom_ui:
-    build: 
-      context: .
-      dockerfile: docker/Dockerfile.ui
-    container_name: emom_web_ui
-    restart: unless-stopped
-    ports:
-      - "8080:8080"
-    networks:
-      - emom_mesh
-    env_file:
-      - .env
-    volumes:
-      - ./src:/app/src
-      - ${DATA_DEAM_DIR}:/app/dataset/DEAM:ro
-    depends_on:
-      - emom_inference
-
-  emom_inference:
-    build:
-      context: .
-      dockerfile: docker/Dockerfile.api
-    container_name: emom_pytorch_api
-    restart: unless-stopped
-    networks:
-      - emom_mesh
-    env_file:
-      - .env
-    volumes:
-      - ${HOST_ARTIFACTS_DIR}/emoset_resnet50_best.pth:/app/src/emoset_resnet50_best.pth:ro
-      - ${HOST_ARTIFACTS_DIR}/music_engine/va_regressor.pkl:/app/src/music_engine/va_regressor.pkl:ro
-      - ${DATA_DEAM_DIR}:/app/dataset/DEAM:ro
-      - ~/.cache/huggingface:/root/.cache/huggingface
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: 1
-              capabilities: [gpu]
-
-  emom_ollama:
-    image: ollama/ollama:latest
-    container_name: emom_ollama_engine
-    restart: unless-stopped
-    networks:
-      - emom_mesh
-    volumes:
-      - ~/.ollama:/root/.ollama
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: 1
-              capabilities: [gpu]

docker/Dockerfile.api

@@ -1,19 +0,0 @@
-FROM pytorch/pytorch:2.2.1-cuda12.1-cudnn8-runtime
-
-ENV PYTHONDONTWRITEBYTECODE=1
-ENV PYTHONUNBUFFERED=1
-
-RUN apt-get update && apt-get install -y \
-    libglib2.0-0 libsm6 libxext6 libxrender-dev \
-    && rm -rf /var/lib/apt/lists/*
-
-RUN pip install --no-cache-dir fastapi uvicorn timm scikit-learn pandas joblib python-multipart transformers==4.38.2 tokenizers==0.15.2 accelerate
-
-WORKDIR /app
-COPY src/ /app/src/
-
-WORKDIR /app/src
-
-EXPOSE 8000
-
-CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "8000"]

docker/Dockerfile.ui

@@ -1,15 +0,0 @@
-FROM python:3.12-slim
-
-ENV PYTHONDONTWRITEBYTECODE=1
-ENV PYTHONUNBUFFERED=1
-
-WORKDIR /app
-RUN pip install --no-cache-dir streamlit==1.32.0 requests pandas pillow
-
-COPY src/ /app/src/
-
-WORKDIR /app/src
-
-EXPOSE 8080
-
-CMD ["streamlit", "run", "main.py", "--server.port", "8080", "--server.address", "0.0.0.0"]

requirements.txt

@@ -1,9 +0,0 @@
-streamlit==1.32.0
-torch==2.2.1
-torchvision==0.17.1
-timm==0.9.16
-pandas==2.2.1
-scikit-learn==1.4.1.post1
-joblib==1.3.2
-transformers==4.38.2
-requests==2.31.0

src/api.py

@@ -1,76 +0,0 @@
-import io
-import traceback
-import numpy as np
-from typing import List
-from fastapi import FastAPI, UploadFile, File, HTTPException
-from fastapi.responses import JSONResponse
-from PIL import Image
-
-from data_loader import load_music_engine, load_image_processor
-from music_engine.llm_bridge import LLMAcousticBridge
-
-app = FastAPI(title="EmoM API", version="1.0.0")
-
-ml_context = {
-    "image_processor": None,
-    "music_matcher": None,
-    "llm_bridge": None
-}
-
-@app.on_event("startup")
-async def startup_event():
-    print("Loading ML models...")
-    ml_context["image_processor"] = load_image_processor()
-    ml_context["music_matcher"] = load_music_engine()
-    ml_context["llm_bridge"] = LLMAcousticBridge()
-    print("Initialization complete.")
-
-@app.post("/analyze")
-async def analyze_event_endpoint(files: List[UploadFile] = File(...)):
-    try:
-        images = []
-        for file in files:
-            image_bytes = await file.read()
-            img = Image.open(io.BytesIO(image_bytes)).convert("RGB")
-            images.append(img)
-            
-        print(f"Processing batch: {len(images)} images.")
-        
-        img_processor = ml_context["image_processor"]
-        matcher = ml_context["music_matcher"]
-        llm = ml_context["llm_bridge"]
-
-        all_v, all_a = [], []
-        all_objects = []
-
-        for img in images:
-            embedding = img_processor.extract_embedding(img)
-            v, a = matcher.predict_va(embedding)
-            all_v.append(v)
-            all_a.append(a)
-            
-            caption = img_processor.describe_scene(img)
-            all_objects.append(caption)
-
-        target_v = float(np.mean(all_v))
-        target_a = float(np.mean(all_a))
-        unique_semantics = list(set(all_objects))
-
-        llm_profile = llm.get_acoustic_profile(target_v, target_a, unique_semantics)
-
-        playlist_df = matcher.find_nearest_tracks(target_v, target_a, llm_profile=llm_profile, top_k=15)
-        tracks_list = playlist_df.to_dict(orient="records")
-
-        return JSONResponse(content={
-            "status": "success",
-            "images_processed": len(images),
-            "target_v": target_v,
-            "target_a": target_a,
-            "llm_profile": llm_profile,
-            "semantics": unique_semantics,
-            "tracks": tracks_list
-        })
-        
-    except Exception as e:
-        print(traceback.format_exc()) 
-        raise HTTPException(status_code=500, detail=str(e))

src/data_loader.py

@@ -1,46 +1,54 @@
+import streamlit as st
 from pathlib import Path
-from typing import Tuple, List, Optional, Any
-
 import pandas as pd
 import numpy as np
-
 from music_engine.matcher import MusicMatcher
 from music_engine.image_processor import ImageProcessor
 
+# Определяем базовую директорию (папка src)
 BASE_DIR = Path(__file__).resolve().parent
 
-def load_music_engine() -> MusicMatcher:
+@st.cache_resource
-    #Инициализация модуля подбора музыкальных композиций.
+def load_music_engine():
+    """Загрузка базы данных и модели регрессора."""
+    # music_db.csv лежит в dataset/DEAM/ (на уровень выше от src)
     db_path = BASE_DIR.parent / "dataset" / "DEAM" / "music_db.csv"
+    # va_regressor.pkl лежит в src/music_engine/
     model_path = BASE_DIR / "music_engine" / "va_regressor.pkl"
     
+    if not db_path.exists():
+        print(f"⚠️ Файл базы {db_path} не найден!")
+        return None
     return MusicMatcher(db_path=db_path, model_path=model_path)
 
-def load_image_processor() -> ImageProcessor:
+@st.cache_resource
-    #Инициализация модуля экстракции визуальных признаков.
+def load_image_processor():
-    weights_path = BASE_DIR / "emoset_resnet50_best.pth"
+    """Загрузка ResNet-50 для извлечения признаков на лету."""
+    # Файл весов лежит в той же папке src, что и этот скрипт
+    model_path = BASE_DIR / "emoset_resnet50_best.pth"
     
-    return ImageProcessor(weights_path)
+    if not model_path.exists():
+        print(f"❌ КРИТИЧЕСКАЯ ОШИБКА: Веса не найдены по пути: {model_path}")
+        # Если не нашли в src, попробуем поискать в корне проекта на всякий случай
+        model_path = BASE_DIR.parent / "emoset_resnet50_best.pth"
         
-def load_emoset_data() -> Tuple[Optional[List[str]], Optional[np.ndarray], Optional[np.ndarray], Optional[Path]]:
+    return ImageProcessor(model_path=model_path)
-    # Загрузка тестовой выборки датасета EmoSet.
-    # Модуль сохранен для обеспечения обратной совместимости в отладочном контуре.
-    try:
-        images_path = BASE_DIR.parent / "dataset" / "EmoSet-118K" / "test" / "images"
-        labels_path = BASE_DIR / "emoset_test_labels.npy"
-        embeddings_path = BASE_DIR / "emoset_test_embeddings.npy"
-        
-        if not all(p.exists() for p in [labels_path, embeddings_path]):
-            return None, None, None, None
-            
-        labels = np.load(labels_path)
-        embeddings = np.load(embeddings_path)
 
+@st.cache_data
+def load_emoset_data():
+    """Загрузка тестовой выборки EmoSet для первой вкладки."""
+    # Пути относительно корня проекта
     csv_path = BASE_DIR.parent / "dataset" / "EmoSet-118K" / "test" / "labels.csv"
-        df = pd.read_csv(csv_path)
+    img_dir = BASE_DIR.parent / "dataset" / "EmoSet-118K" / "test" / "images"
+    emb_path = BASE_DIR / "emoset_test_embeddings.npy"
+    lbl_path = BASE_DIR / "emoset_test_labels.npy"
 
-        return df['filename'].tolist(), embeddings, labels, images_path
+    if not all([csv_path.exists(), emb_path.exists(), lbl_path.exists()]):
-        
-    except Exception as e:
-        print(f"[WARN] Failed to load EmoSet test artifacts: {str(e)}")
         return None, None, None, None
+
+    df = pd.read_csv(csv_path)
+    image_list = df['filename'].tolist()
+    embs = np.load(emb_path)
+    lbls = np.load(lbl_path)
+    
+    return image_list, embs, lbls, img_dir

src/main.py

@@ -1,189 +1,43 @@
-import os
-import requests
 import streamlit as st
-import streamlit.components.v1 as components
+import sys
-from PIL import Image
+import os
-import base64
+import subprocess
-from io import BytesIO
 
-st.set_page_config(page_title="EmoM Playlist Generator", layout="wide", initial_sidebar_state="collapsed")
+from data_loader import load_music_engine, load_emoset_data, load_image_processor
-
+from tabs.tab_dataset import render_dataset_tab
-API_URL = os.getenv("BACKEND_API_URL", "http://emom_inference:8000") + "/analyze"
+from tabs.tab_live import render_live_tab
-DEAM_AUDIO_DIR = "/app/dataset/DEAM/DEAM_audio/MEMD_audio"
-
-def get_thumbnail_html(images, max_display=12):
-    html_images = ""
-    for file in images[:max_display]:
-        img = Image.open(file)
-        img.thumbnail((100, 100))
-        if img.mode != "RGB": 
-            img = img.convert("RGB")
-        buffered = BytesIO()
-        img.save(buffered, format="JPEG")
-        b64_str = base64.b64encode(buffered.getvalue()).decode()
-        html_images += f'<img src="data:image/jpeg;base64,{b64_str}" style="width: 60px; height: 60px; object-fit: cover; border-radius: 8px; margin-right: 8px; margin-bottom: 8px; border: 1px solid rgba(255, 255, 255, 0.2);">'
-    
-    if len(images) > max_display:
-        html_images += f'<span style="display: inline-block; width: 60px; height: 60px; line-height: 60px; text-align: center; background: rgba(150, 150, 150, 0.2); border-radius: 8px; vertical-align: top; font-size: 14px;">+{len(images) - max_display}</span>'
-    return f'<div style="display: flex; flex-wrap: wrap;">{html_images}</div>'
-
-def main():
-    if "live_state" not in st.session_state: 
-        st.session_state.live_state = "upload"
-    if "result_data" not in st.session_state: 
-        st.session_state.result_data = None
-        
-    viewport = st.query_params.get("viewport", "desktop")
-
-    st.markdown("""
-    <style>
-    [data-testid="stFileUploadDropzone"] { min-height: 250px !important; display: flex; align-items: center; justify-content: center; border-radius: 16px; background-color: rgba(255, 75, 75, 0.03); }
-    .spinner-container { display: flex; flex-direction: column; align-items: center; justify-content: center; min-height: 40vh; margin-top: 10vh; }
-    .big-spinner { width: 120px; height: 120px; border: 10px solid rgba(255, 75, 75, 0.1); border-top: 10px solid #ff4b4b; border-radius: 50%; animation: spin 1s linear infinite; margin-bottom: 2rem; }
-    @keyframes spin { 0% { transform: rotate(0deg); } 100% { transform: rotate(360deg); } }
-    #MainMenu {visibility: hidden;} footer {visibility: hidden;}
-    </style>
-    """, unsafe_allow_html=True)
-
-    if st.session_state.live_state == "upload":
-        upload_placeholder = st.empty()
-        with upload_placeholder.container():
-            st.write("Загрузите изображения для визуально-семантического анализа.")
-            if viewport == "mobile": 
-                st.markdown("<br>", unsafe_allow_html=True)
-            
-            uploaded_files = st.file_uploader(
-                "Загрузка файлов", 
-                type=['png', 'jpg', 'jpeg'], 
-                accept_multiple_files=True,
-                label_visibility="collapsed" if viewport == "mobile" else "visible"
-            )
-            
-            if uploaded_files:
-                st.markdown("<br>", unsafe_allow_html=True)
-                if st.button("Выполнить анализ", type="primary", use_container_width=True):
-                    st.session_state.uploaded_images = uploaded_files
-                    st.session_state.live_state = "processing"
-                    upload_placeholder.empty()
-                    st.rerun()
-                
-                st.markdown("<br>", unsafe_allow_html=True)
-                st.caption("Выбранные файлы:")
-                st.markdown(get_thumbnail_html(uploaded_files), unsafe_allow_html=True)
-
-    elif st.session_state.live_state == "processing":
-        components.html("<script>window.parent.scrollTo(0, 0);</script>", height=0, width=0)
-        files = st.session_state.get("uploaded_images", [])
-        st.markdown('<div class="spinner-container"><div class="big-spinner"></div><h3 style="text-align: center; font-weight: 400;">Обработка данных...</h3></div>', unsafe_allow_html=True)
-        
-        try:
-            upload_data = [('files', (f.name, f.getvalue(), f.type)) for f in files]
-            response = requests.post(API_URL, files=upload_data, timeout=300) 
-            
-            if response.status_code == 200:
-                st.session_state.result_data = response.json()
-                st.session_state.live_state = "result"
-                st.rerun()
-            else:
-                st.error(f"Ошибка сервера: {response.status_code}")
-                if st.button("Назад"):
-                    st.session_state.live_state = "upload"
-                    st.rerun()
-        except Exception as e:
-            st.error(f"Ошибка соединения: {str(e)}")
-            if st.button("Назад"):
-                st.session_state.live_state = "upload"
-                st.rerun()
-                
-    elif st.session_state.live_state == "result":
-        components.html("<script>window.parent.scrollTo(0, 0);</script>", height=0, width=0)
-        data = st.session_state.result_data
-        
-        st.header(f"Сгенерированный плейлист (обработано файлов: {data['images_processed']})")
-        
-        for row in data.get("tracks", []):
-            with st.container(border=True):
-                song_id = int(row['song_id'])
-                score = row['final_score']
-                
-                audio_path = f"{DEAM_AUDIO_DIR}/{song_id}.mp3"
-                if not os.path.exists(audio_path): 
-                    audio_path = audio_path.replace('.mp3', '.wav')
-                
-                if viewport == "desktop":
-                    c1, c2 = st.columns([1, 3])
-                    with c1:
-                        st.write(f"**Track ID:** {song_id}")
-                        st.caption(f"Score: {score:.4f}")
-                    with c2:
-                        if os.path.exists(audio_path): 
-                            st.audio(audio_path)
-                        else: 
-                            st.caption("Аудиофайл не найден")
-                else:
-                    st.write(f"**Track ID:** {song_id} (Score: {score:.4f})")
-                    if os.path.exists(audio_path): 
-                        st.audio(audio_path)
-                    else: 
-                        st.caption("Аудиофайл не найден")
-
-        st.markdown("<br>", unsafe_allow_html=True)
-        
-        with st.expander("Отладочная информация (Метрики)"):
-            st.subheader("Координаты V/A")
-            c_v, c_a = st.columns(2)
-            c_v.metric("Valence", f"{data['target_v']:.2f}")
-            c_a.metric("Arousal", f"{data['target_a']:.2f}")
-            
-            st.markdown("---")
-            st.subheader("Акустические признаки (LLM)")
-            
-            feature_titles = {
-                "energy": "RMS Energy",
-                "flux": "Spectral Flux",
-                "centroid": "Spectral Centroid",
-                "pitch": "F0 (Pitch)",
-                "hnr": "HNR",
-                "zcr": "ZCR"
-            }
-            
-            # Развернутые описания
-            feature_helps = {
-                "energy": "Среднеквадратичная амплитуда (громкость). Бывает высокой в плотных, интенсивных композициях, отражает общую акустическую энергию сцены.",
-                "flux": "Спектральный поток. Измеряет резкость изменений в спектре. Высок при четком, агрессивном ритме и частой смене нот.",
-                "centroid": "Спектральный центроид («яркость» звука). Высокие значения указывают на преобладание высоких частот (звонкие инструменты, открытые пространства).",
-                "pitch": "Основная частота звука. Высокий pitch характерен для позитивных, легких или, напротив, напряженных мелодий.",
-                "hnr": "Отношение гармоник к шуму. Высокий HNR — чистая мелодия и вокал. Низкий HNR — присутствие дисторшна, шумов или перкуссии.",
-                "zcr": "Частота пересечения нуля. Отражает шумовую составляющую сигнала. Высок в треках с выраженными ударными (hi-hats) или атмосферным шумом."
-            }
-            
-            llm_profile = data.get("llm_profile")
-            if llm_profile and isinstance(llm_profile, dict) and len(llm_profile) > 0:
-                cols_per_row = 2 if viewport == "mobile" else 3
-                llm_items = list(llm_profile.items())
-                
-                for i in range(0, len(llm_items), cols_per_row):
-                    cols = st.columns(cols_per_row)
-                    for j in range(cols_per_row):
-                        if i + j < len(llm_items):
-                            k, v = llm_items[i + j]
-                            label = feature_titles.get(k, k)
-                            tooltip = feature_helps.get(k, "")
-                            cols[j].metric(label, f"{v:.2f}", help=tooltip)
-            else:
-                st.caption("Акустический профиль недоступен. Применен fallback-алгоритм.")
-                            
-            st.markdown("---")
-            st.write("**Извлеченные теги (BLIP-2):**")
-            st.write(", ".join([str(c).capitalize() for c in data.get("semantics", [])]))
-
-        st.markdown("<br>", unsafe_allow_html=True)
-        
-        if st.button("Новый запрос", use_container_width=True):
-            st.session_state.live_state = "upload"
-            st.session_state.result_data = None
-            st.session_state.pop("uploaded_images", None)
-            st.rerun()
 
+# ----------------------------
+# 1️⃣ Запуск приложения
+# ----------------------------
 if __name__ == "__main__":
-    main()
+    if "STREAMLIT_RUN" not in os.environ:
+        os.environ["STREAMLIT_RUN"] = "1"
+        cmd = [sys.executable, "-m", "streamlit", "run", __file__, "--server.port", "8080", "--server.address", "0.0.0.0"]
+        subprocess.run(cmd)
+        sys.exit()
+
+st.set_page_config(page_title="Thesis Demo", layout="wide")
+
+# ----------------------------
+# 2️⃣ Инициализация движка и данных
+# ----------------------------
+matcher = load_music_engine()
+image_processor = load_image_processor()
+image_files, embeddings, labels_array, images_path = load_emoset_data()
+
+# ----------------------------
+# 3️⃣ Интерфейс и Вкладки
+# ----------------------------
+st.title("🖼️ Генератор саундтреков (Research Demo)")
+
+tab1, tab2 = st.tabs(["📊 Отладка (Датасет EmoSet)", "📸 Анализ событий (Свои фото)"])
+
+with tab1:
+    render_dataset_tab(matcher, image_files, embeddings, labels_array, images_path)
+
+with tab2:
+    if image_processor:
+        render_live_tab(matcher, image_processor)
+    else:
+        st.error("Система обработки изображений недоступна (не найдены веса ResNet).")

src/music_engine/image_processor.py

@@ -1,66 +1,57 @@
-import numpy as np
-from pathlib import Path
-from PIL import Image
-
 import torch
 import torchvision.transforms as T
+from PIL import Image
 import timm
-from transformers import Blip2Processor, Blip2ForConditionalGeneration
+from pathlib import Path
+import numpy as np
+
+# НОВЫЙ ИМПОРТ ДЛЯ VLM
+from transformers import BlipProcessor, BlipForConditionalGeneration
 
 class ImageProcessor:
-    def __init__(self, weights_path: str | Path):
+    def __init__(self, model_path: Path | str):
         self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
         
-        # Модель извлечения визуальных признаков
+        # --- ПОТОК 1: ЭМОЦИИ (ResNet-50) ---
-        self.feature_extractor = timm.create_model('resnet50', pretrained=False, num_classes=8)
+        print("⏳ Загрузка эмоционального модуля (ResNet-50)...")
+        self.emo_model = timm.create_model('resnet50', pretrained=False, num_classes=8)
+        if Path(model_path).exists():
+            self.emo_model.load_state_dict(torch.load(model_path, map_location=self.device))
+        self.emo_model.fc = torch.nn.Identity()
+        self.emo_model.to(self.device).eval()
 
-        if Path(weights_path).exists():
+        self.emo_transform = T.Compose([
-            self.feature_extractor.load_state_dict(torch.load(weights_path, map_location=self.device))
-        else:
-            print(f"Не удалось найти веса ResNet по пути: {weights_path}")
-            
-        # Удаление слоя классификации для вывода сырого вектора эмбеддингов
-        self.feature_extractor.fc = torch.nn.Identity()
-        self.feature_extractor.to(self.device).eval()
-
-        # Трансформации для предварительной обработки изображений
-        self.preprocess_image = T.Compose([
             T.Resize((224, 224)),
             T.ToTensor(),
             T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
         ])
 
-        # Модуль семантического описания сцены
+        # --- ПОТОК 2: СЕМАНТИКА И КОНТЕКСТ (BLIP Large) ---
-        print("Инициализация BLIP-2...")
+        print("⏳ Загрузка мощной VLM модели (BLIP) для описания сцен...")
-        # Обход бага конфигурации Hugging Face (ручная сборка процессора)
+        # Используем версию Large, так как позволяет железо V100
-        from transformers import BlipImageProcessor, AutoTokenizer
+        self.blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
-        img_proc = BlipImageProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
+        self.blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to(self.device)
-        tok = AutoTokenizer.from_pretrained("Salesforce/blip2-opt-2.7b", use_fast=False)
+        
-        self.blip_processor = Blip2Processor(image_processor=img_proc, tokenizer=tok)
+        print("✅ Обе нейросети визуального анализа успешно загружены на V100!")
-        self.blip_model = Blip2ForConditionalGeneration.from_pretrained(
-            "Salesforce/blip2-opt-2.7b", 
-            torch_dtype=torch.float16 
-        ).to(self.device)
 
     @torch.no_grad()
     def extract_embedding(self, image: Image.Image) -> np.ndarray:
-        # Извлечение эмбеддингов из изображения
+        """Извлекает 2048-мерный вектор эмоций."""
-        rgb_image = image.convert('RGB')
+        img_rgb = image.convert('RGB')
-        img_tensor = self.preprocess_image(rgb_image).unsqueeze(0).to(self.device)
+        img_tensor = self.emo_transform(img_rgb).unsqueeze(0).to(self.device)
-        
+        return self.emo_model(img_tensor).cpu().numpy().flatten()
-        features = self.feature_extractor(img_tensor)
-        features_np = features.cpu().numpy()
-        
-        return features_np.flatten()
         
     @torch.no_grad()
     def describe_scene(self, image: Image.Image) -> str:
-        # Генерация текстового описания сцены
+        """Генерирует текстовое описание картинки (Captioning) для LLM."""
-        rgb_image = image.convert('RGB')
+        img_rgb = image.convert('RGB')
         
-        inputs = self.blip_processor(images=rgb_image, return_tensors="pt").to(self.device, torch.float16)
+        # Готовим картинку для BLIP
-        generated_ids = self.blip_model.generate(**inputs, max_new_tokens=40)
+        inputs = self.blip_processor(img_rgb, return_tensors="pt").to(self.device)
         
-        scene_description = self.blip_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        # Генерируем описание (max_new_tokens ограничим, чтобы было лаконично)
+        out = self.blip_model.generate(**inputs, max_new_tokens=30)
         
-        return scene_description.strip()
+        # Декодируем тензор в строку
+        caption = self.blip_processor.decode(out[0], skip_special_tokens=True)
+        return caption

src/music_engine/llm_bridge.py

@@ -1,65 +1,60 @@
-import os
+import requests
 import json
 import re
-import requests
 
 class LLMAcousticBridge:
-    def __init__(self, model_name="dolphin-llama3:8b"):
+    def __init__(self, model_name="phi3", host="http://localhost:11434"):
         self.model_name = model_name
-        base_url = os.getenv("OLLAMA_API_URL", "http://emom_ollama:11434")
+        self.api_url = f"{host}/api/generate"
-        self.api_url = f"{base_url}/api/generate"
 
-    def get_acoustic_profile(self, valence, arousal, semantics):
+    def _clean_json(self, text):
-        context_str = ", ".join(semantics) if semantics else "abstract scene"
+        """Вытаскивает чистый JSON из ответа нейросети."""
+        try:
+            match = re.search(r'\{.*\}', text, re.DOTALL)
+            if match:
+                return json.loads(match.group(0))
+            return json.loads(text)
+        except:
+            return None
 
-        prompt = f"""
+    def get_acoustic_profile(self, valence, arousal, scene_descriptions):
+        """Просит LLM сгенерировать идеальный звук под описание."""
+        # Объединяем описания, если загружено несколько фото
+        context_str = " | ".join(scene_descriptions) if scene_descriptions else "abstract scene"
+        
+        prompt = f"""You are an expert music producer and acoustic engineer. 
 Analyze the visual context and emotions to determine the ideal background music properties.
 Emotions: Valence {valence:.1f}/9.0 (Positivity), Arousal {arousal:.1f}/9.0 (Energy).
 Visual Context: {context_str}.
+
 Map this scene to exactly 6 acoustic features. Values MUST be floats between 0.0 and 1.0.
+1. "energy": (Loudness/Density. High for massive/busy scenes, Low for calm)
+2. "flux": (Rhythmic sharpness/Beat. High for action/people/cars, Low for static nature)
+3. "centroid": (Brightness: 0=Dark/Bass/Massive, 1=Bright/Treble/Light)
+4. "pitch": (Fundamental frequency: 0=Low pitch/Huge objects, 1=High pitch/Small objects)
+5. "hnr": (Harmonics-to-Noise: 0=Noisy/Distorted textures, 1=Clear/Melodic/Smooth textures)
+6. "zcr": (Percussiveness. High for detailed noise like leaves/rain, Low for solid blocks)
 
-        1. "energy": (Loudness/Density)
+Return ONLY a valid JSON object. Do not add any text or explanation.
-        2. "flux": (Rhythmic sharpness/Beat)
+Example: {{"energy": 0.5, "flux": 0.2, "centroid": 0.4, "pitch": 0.3, "hnr": 0.8, "zcr": 0.1}}"""
-        3. "centroid": (Brightness)
-        4. "pitch": (Fundamental frequency)
-        5. "hnr": (Harmonics-to-Noise)
-        6. "zcr": (Percussiveness)
-        
-        Return ONLY a valid JSON object. No explanations, no markdown blocks.
-        Example: {{"energy": 0.8, "flux": 0.5, "centroid": 0.6, "pitch": 0.4, "hnr": 0.9, "zcr": 0.3}}
-        """
 
         try:
-            payload = {
+            response = requests.post(self.api_url, json={
                 "model": self.model_name,
                 "prompt": prompt,
                 "stream": False,
-                "format": "json"  # Принудительный JSON-режим Ollama
+                "format": "json"
-            }
+            }, timeout=30)
+            response.raise_for_status()
             
-            print(f"Запрос акустического профиля к Ollama...")
+            result_text = response.json().get("response", "")
-            response = requests.post(self.api_url, json=payload, timeout=120)
+            profile = self._clean_json(result_text)
             
-            if response.status_code == 200:
+            # Проверяем, что все нужные ключи есть
-                data = response.json()
+            required_keys = ['energy', 'flux', 'centroid', 'pitch', 'hnr', 'zcr']
-                response_text = data.get("response", "")
+            if profile and all(k in profile for k in required_keys):
-                
-                try:
-                    # 1. Попытка прямой десериализации
-                    profile = json.loads(response_text)
                 return profile
-                except json.JSONDecodeError:
+            return None
-                    # 2. Аварийное извлечение JSON из текста с помощью регулярного выражения
-                    match = re.search(r'\{.*\}', response_text, re.DOTALL)
-                    if match:
-                        return json.loads(match.group(0))
-                    
-                    print(f"Ошибка парсинга LLM ответа: {response_text}")
-                    return {}
-            else:
-                print(f"Ollama вернула ошибку HTTP: {response.status_code}")
-                return {}
-                
         except Exception as e:
-            print(f"Ошибка соединения с Ollama: {str(e)}")
+            print(f"⚠️ Ошибка связи с локальной LLM: {e}")
-            return {}
+            return None

src/music_engine/matcher.py

@@ -1,83 +1,67 @@
-import joblib
 import numpy as np
 import pandas as pd
 from pathlib import Path
+import joblib
 
 class MusicMatcher:
     def __init__(self, db_path: Path | str, model_path: Path | str):
-        # Загрузка базы данных музыкальных произведений
+        # Загружаем твою новую, обогащенную базу
         self.music_db = pd.read_csv(db_path)
         self.acoustic_features = ['energy', 'flux', 'centroid', 'pitch', 'hnr', 'zcr']
         
-        # Удаление записей с пропущенными целевыми или акустическими признаками
+        # Удаляем строки, где нет акустических фич
-        target_columns = ['valence', 'arousal'] + self.acoustic_features
+        self.music_db = self.music_db.dropna(subset=['valence', 'arousal'] + self.acoustic_features)
-        self.music_db = self.music_db.dropna(subset=target_columns)
         
-        # Масштабирование акустических параметров к диапазону [0, 1]
+        # Нормализуем акустику от 0 до 1, чтобы сравнивать с ответом LLM
         self.norm_db = self.music_db.copy()
         for feat in self.acoustic_features:
-            f_min = self.norm_db[feat].min()
+            f_min, f_max = self.norm_db[feat].min(), self.norm_db[feat].max()
-            f_max = self.norm_db[feat].max()
             if f_max > f_min:
                 self.norm_db[f"norm_{feat}"] = (self.norm_db[feat] - f_min) / (f_max - f_min)
             else:
                 self.norm_db[f"norm_{feat}"] = 0.0
 
-        # Определение путей к аудиофайлам и загрузка модели регрессии
         self.audio_dir = Path(db_path).parent / "DEAM_audio" / "MEMD_audio"
+        self.regressor = joblib.load(model_path) if Path(model_path).exists() else None
 
-        if Path(model_path).exists():
+    def predict_va(self, embedding: np.ndarray):
-            self.regressor = joblib.load(model_path)
+        if self.regressor:
-        else:
+            prediction = self.regressor.predict(embedding.reshape(1, -1))[0]
-            self.regressor = None
+            return np.clip(prediction[0], 1.0, 9.0), np.clip(prediction[1], 1.0, 9.0)
-
-    def predict_va(self, embedding: np.ndarray) -> tuple[float, float]:
-        # Прогнозирование координат Valence/Arousal по визуальному эмбеддингу
-        if not self.regressor:
         return 5.0, 5.0
 
-        raw_prediction = self.regressor.predict(embedding.reshape(1, -1))[0]
+    def get_audio_path(self, song_id):
-        valence_pred = np.clip(raw_prediction[0], 1.0, 9.0)
+        if not self.audio_dir.exists(): return None
-        arousal_pred = np.clip(raw_prediction[1], 1.0, 9.0)
-        
-        return float(valence_pred), float(arousal_pred)
-
-    def get_audio_path(self, song_id: int | float | str) -> Path | None:
-        # Поиск физического пути к аудиофайлу в зависимости от расширения
-        if not self.audio_dir.exists():
-            return None
-            
         clean_id = str(int(float(song_id)))
         for ext in ['.mp3', '.wav']:
             path = self.audio_dir / f"{clean_id}{ext}"
-            if path.exists():
+            if path.exists(): return path
-                return path
         return None
 
-    def find_nearest_tracks(self, target_v: float, target_a: float, llm_profile: dict = None, top_k: int = 5) -> pd.DataFrame:
+    def find_nearest_tracks(self, target_v: float, target_a: float, llm_profile: dict = None, top_k: int = 5):
-        # Расчет евклидова расстояния в эмоциональном пространстве Рассела
+        # 1. Эмоциональная дистанция (как и раньше)
-        v_dist = (self.norm_db['valence'] - target_v) ** 2
+        emo_dist = np.sqrt(
-        a_dist = (self.norm_db['arousal'] - target_a) ** 2
+            1.0 * (self.norm_db['valence'] - target_v)**2 + 
+            2.5 * (self.norm_db['arousal'] - target_a)**2
+        )
+        self.norm_db['emo_distance'] = emo_dist
         
-        # Взвешенное расстояние с приоритетом оси активации (Arousal)
+        # Если LLM не дала ответ, сортируем только по эмоциям
-        self.norm_db['emo_distance'] = np.sqrt(1.0 * v_dist + 2.5 * a_dist)
-        
-        # Ранжирование только по эмоциональному критерию при отсутствии профиля LLM
         if not llm_profile:
             self.norm_db['final_score'] = self.norm_db['emo_distance']
             return self.norm_db.sort_values(by='final_score').head(top_k)
 
-        # Расчет отклонений по вектору акустических параметров LLM
+        # 2. Акустическая дистанция (сравниваем треки с запросом LLM)
         acoustic_penalty = np.zeros(len(self.norm_db))
         for feat in self.acoustic_features:
             if feat in llm_profile:
                 target_val = llm_profile[feat]
                 acoustic_penalty += np.abs(self.norm_db[f"norm_{feat}"] - target_val)
 
-        # Нормирование акустической дистанции
+        # Усредняем штраф
         self.norm_db['acoustic_distance'] = acoustic_penalty / len(self.acoustic_features)
 
-        # Вычисление интегральной метрики соответствия (мультимодальный скоринг)
+        # 3. Финальный Score (Смесь Эмоций и Акустики). Коэф 4.0 делает акустику важной!
         self.norm_db['final_score'] = self.norm_db['emo_distance'] + (self.norm_db['acoustic_distance'] * 4.0)
 
         return self.norm_db.sort_values(by='final_score').head(top_k)

src/scripts/00_setup_env.sh

@@ -1,73 +0,0 @@
-#!/bin/bash
-
-# Данный скрипт написан ИИ для быстрой подготовки окружения, установка драйверов и докера
-# Остановка скрипта при возникновении любой ошибки
-set -e
-
-# Цвета для красивого вывода в консоль
-GREEN='\033[0;32m'
-YELLOW='\033[1;33m'
-RED='\033[0;31m'
-BLUE='\033[0;34m'
-NC='\033[0m'
-
-echo -e "${BLUE}[INFO]${NC} Инициализация проверки окружения для проекта EmoM..."
-
-# 1. ПРОВЕРКА DOCKER
-if ! command -v docker &> /dev/null; then
-    echo -e "${YELLOW}[SETUP]${NC} Docker не найден. Начинаем установку..."
-    # Использование официального скрипта установки Docker
-    curl -fsSL https://get.docker.com -o get-docker.sh
-    sudo sh get-docker.sh
-    rm get-docker.sh
-    
-    # Добавляем текущего пользователя в группу docker, чтобы не писать sudo docker
-    sudo usermod -aG docker $USER
-    echo -e "${GREEN}[OK]${NC} Docker успешно установлен."
-    echo -e "${YELLOW}[ВНИМАНИЕ]${NC} Для применения прав группы docker потребуется перезайти в сессию SSH после завершения скрипта."
-else
-    echo -e "${GREEN}[OK]${NC} Docker установлен ($(docker --version))."
-fi
-
-# 2. ПРОВЕРКА DOCKER COMPOSE
-if ! docker compose version &> /dev/null; then
-    echo -e "${YELLOW}[SETUP]${NC} Плагин Docker Compose не найден. Устанавливаем..."
-    sudo apt-get update && sudo apt-get install -y docker-compose-plugin
-    echo -e "${GREEN}[OK]${NC} Docker Compose успешно установлен."
-else
-    echo -e "${GREEN}[OK]${NC} Плагин Docker Compose доступен."
-fi
-
-# 3. ПРОВЕРКА NVIDIA И ПРОБРОСА GPU В DOCKER
-if command -v nvidia-smi &> /dev/null; then
-    echo -e "${GREEN}[OK]${NC} Драйверы NVIDIA обнаружены."
-    
-    # Проверяем наличие NVIDIA Container Toolkit
-    if ! dpkg -l | grep -q nvidia-container-toolkit; then
-        echo -e "${YELLOW}[SETUP]${NC} NVIDIA Container Toolkit не найден. Выполняется установка..."
-        
-        # Настройка репозиториев NVIDIA
-        curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
-        curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
-            sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
-            sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
-            
-        sudo apt-get update
-        sudo apt-get install -y nvidia-container-toolkit
-        
-        # Конфигурация Docker для работы с NVIDIA
-        echo -e "${YELLOW}[SETUP]${NC} Конфигурация runtime NVIDIA для Docker..."
-        sudo nvidia-ctk runtime configure --runtime=docker
-        sudo systemctl restart docker
-        
-        echo -e "${GREEN}[OK]${NC} NVIDIA Container Toolkit установлен и настроен."
-    else
-        echo -e "${GREEN}[OK]${NC} NVIDIA Container Toolkit уже установлен."
-    fi
-else
-    echo -e "${RED}[WARN]${NC} Утилита nvidia-smi не найдена! Убедитесь, что драйверы видеокарты установлены, иначе Docker будет использовать только CPU."
-fi
-
-echo -e "\n${BLUE}[INFO]${NC} ========================================="
-echo -e "${GREEN}[SUCCESS]${NC} Окружение готово к работе!"
-echo -e "Теперь вы можете запустить проект командой: ${YELLOW}make up${NC}"

src/scripts/01_download_DEAM.py

@@ -1,20 +0,0 @@
-import shutil
-from pathlib import Path
-import kagglehub
-
-dataset_dir = Path("../dataset/DEAM")
-dataset_dir.mkdir(parents=True, exist_ok=True)
-
-print("Скачивание датасета DEAM...")
-
-# kagglehub по умолчанию тянет данные в системный кэш (~/.cache)
-cache_path = kagglehub.dataset_download("imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music")
-
-print(f"Загружено в кэш: {cache_path}")
-print(f"Перенос файлов в {dataset_dir} и очистка временной директории...")
-
-# Перемещаем данные
-shutil.copytree(cache_path, dataset_dir, dirs_exist_ok=True)
-shutil.rmtree(cache_path)
-
-print("Готово. Датасет DEAM загружен, кэш очищен.")

src/scripts/02_download_EmoSet.py

@@ -1,56 +0,0 @@
-import csv
-from pathlib import Path
-
-from datasets import load_dataset
-from tqdm import tqdm
-
-# Конфигурация корневой директории локального датасета
-DATASET_DIR = Path("../dataset/EmoSet-118K")
-
-def process_and_save_split(dataset_split, split_name: str, output_dir: Path):
-    # Подготовка структуры директорий для текущей выборки
-    split_dir = output_dir / split_name
-    img_dir = split_dir / "images"
-    img_dir.mkdir(parents=True, exist_ok=True)
-
-    labels_path = split_dir / "labels.csv"
-
-    print(f"Обработка выборки: {split_name}...")
-
-    # Открытие файла разметки перед циклом для минимизации I/O операций диска
-    with open(labels_path, mode="w", newline="", encoding="utf-8") as csv_file:
-        writer = csv.writer(csv_file)
-        writer.writerow(["filename", "label"])
-
-        for example in tqdm(dataset_split, desc=split_name):
-            img = example["image"]
-            emotion_label = example["emotion"]
-            img_id = example["image_id"]
-
-            file_name = f"{img_id}.jpg"
-            
-            # Принудительная конвертация в RGB для безопасного сохранения в JPEG-формате
-            if img.mode != "RGB":
-                img = img.convert("RGB")
-                
-            img.save(img_dir / file_name, format="JPEG")
-            
-            writer.writerow([file_name, emotion_label])
-
-if __name__ == "__main__":
-    DATASET_DIR.mkdir(exist_ok=True, parents=True)
-
-    # Инициализация подключения к Hugging Face Hub
-    print("Загрузка метаданных EmoSet-118K...")
-    raw_dataset = load_dataset("Woleek/EmoSet-118K")
-
-    # Итеративная выгрузка размеченных данных
-    for split_key in ["train", "val", "test"]:
-        if split_key in raw_dataset:
-            process_and_save_split(
-                dataset_split=raw_dataset[split_key],
-                split_name=split_key,
-                output_dir=DATASET_DIR
-            )
-
-    print("Экспорт датасета завершен.")

src/scripts/11_prerp_DEAM.py

@@ -1,30 +0,0 @@
-import pandas as pd
-from pathlib import Path
-
-# Конфигурация локальных путей
-SOURCE_CSV = Path("../../dataset/DEAM/DEAM_Annotations/annotations/annotations averaged per song/song_level/static_annotations_averaged_songs_1_2000.csv")
-OUTPUT_CSV = Path("../../dataset/DEAM/music_db.csv")
-
-def prepare_deam_database():
-    if not SOURCE_CSV.exists():
-        print(f"Исходный файл аннотаций не найден: {SOURCE_CSV}")
-        return
-
-    print("Обработка разметки датасета DEAM...")
-    
-    # Загрузка сырых данных с очисткой артефактов форматирования
-    raw_df = pd.read_csv(SOURCE_CSV, skipinitialspace=True)
-    
-    # Экстракция координат пространства Рассела (Valence/Arousal)
-    processed_df = raw_df[['song_id', 'valence_mean', 'arousal_mean']].copy()
-    processed_df.columns = ['song_id', 'valence', 'arousal']
-    
-    # Приведение идентификаторов к формату файловой системы (int)
-    processed_df['song_id'] = processed_df['song_id'].astype(int)
-    
-    processed_df.to_csv(OUTPUT_CSV, index=False)
-    
-    print(f"База успешно сформирована. Всего записей: {len(processed_df)}")
-
-if __name__ == "__main__":
-    prepare_deam_database()

src/scripts/20_bench_GPU.py

@@ -1,60 +0,0 @@
-import time
-import torch
-import torch.nn as nn
-import torch.optim as optim
-
-# Конфигурация параметров нагрузочного тестирования
-NUM_SAMPLES = 300_000
-DIM_IN = 4096
-DIM_OUT = 10
-BATCH_SIZE = 16_384
-NUM_STEPS = 1000
-
-def run_gpu_benchmark():
-    # Проверка доступности аппаратного ускорения
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    print(f"Инициализация стресс-теста на устройстве: {device}")
-
-    # Генерация синтетического датасета для аллокации VRAM
-    x_data = torch.randn(NUM_SAMPLES, DIM_IN, device=device, dtype=torch.float32)
-    y_data = torch.randn(NUM_SAMPLES, DIM_OUT, device=device, dtype=torch.float32)
-
-    # Архитектура тестовой полносвязной сети
-    model = nn.Sequential(
-        nn.Linear(DIM_IN, 2048),
-        nn.ReLU(),
-        nn.Linear(2048, 1024),
-        nn.ReLU(),
-        nn.Linear(1024, 512),
-        nn.ReLU(),
-        nn.Linear(512, DIM_OUT)
-    ).to(device)
-
-    loss_fn = nn.MSELoss()
-    optimizer = optim.Adam(model.parameters(), lr=1e-3)
-
-    print("Начало прогрева GPU и симуляции цикла обучения...")
-    start_time = time.time()
-
-    for step in range(NUM_STEPS):
-        # Сэмплирование случайного батча
-        idx = torch.randint(0, NUM_SAMPLES, (BATCH_SIZE,), device=device)
-        x_batch = x_data[idx]
-        y_batch = y_data[idx]
-
-        optimizer.zero_grad()
-        predictions = model(x_batch)
-        loss = loss_fn(predictions, y_batch)
-        
-        loss.backward()
-        optimizer.step()
-
-        # Логирование статуса (каждые 100 итераций для снижения I/O overhead)
-        if step % 100 == 0:
-            print(f"Итерация {step}/{NUM_STEPS} | Текущий loss: {loss.item():.4f}")
-
-    end_time = time.time()
-    print(f"Стресс-тест завершен. Общее время: {end_time - start_time:.2f} сек.")
-
-if __name__ == "__main__":
-    run_gpu_benchmark()

src/scripts/21_train_images.py

@@ -1,184 +0,0 @@
-import os
-import random
-import warnings
-from pathlib import Path
-from PIL import Image
-import pandas as pd
-import numpy as np
-from tqdm import tqdm
-
-import torch
-import torch.nn as nn
-from torch.utils.data import Dataset, DataLoader
-import torchvision.transforms as T
-import timm
-
-# Подавление предупреждений цветовых профилей
-warnings.filterwarnings("ignore", message=".*Unknown Adobe color transform code.*")
-
-# Настройки окружения
-DATA_ROOT = Path("/home/zin/projects/Thesis/NFS/Thesis/Emoset/EmoSet-118K")
-BATCH_SIZE = 64
-EPOCHS = 30
-LR = 5e-5
-NUM_WORKERS = 62
-PATIENCE = 7
-
-# Маппинг классов
-CLASS_MAPPING = {
-    "amusement": 0, "anger": 1, "awe": 2, "contentment": 3,
-    "disgust": 4, "excitement": 5, "fear": 6, "sadness": 7
-}
-
-DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Устройство: {DEVICE}")
-
-# Фиксация генераторов псевдослучайных чисел
-def set_seed(seed=42):
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed(seed)
-        torch.cuda.manual_seed_all(seed)
-
-set_seed()
-
-# Инициализация структур данных
-class EmoSetDataset(Dataset):
-    def __init__(self, root: Path | str, split: str, transform=None):
-        self.root = Path(root) / split
-        self.df = pd.read_csv(self.root / "labels.csv")
-        self.transform = transform
-        
-        # Фильтрация датафрейма
-        self.df = self.df[self.df["label"].isin(CLASS_MAPPING.keys())].reset_index(drop=True)
-
-    def __len__(self):
-        return len(self.df)
-
-    def __getitem__(self, idx):
-        row = self.df.iloc[idx]
-        img_path = self.root / "images" / row["filename"]
-
-        try:
-            img = Image.open(img_path).convert("RGB")
-        except Exception:
-            img = Image.new("RGB", (256, 256), (0, 0, 0))
-
-        if self.transform:
-            img_tensor = self.transform(img)
-        else:
-            img_tensor = T.ToTensor()(img)
-            
-        label_idx = CLASS_MAPPING[row["label"]]
-        return img_tensor, label_idx
-
-# Трансформации
-base_tf = [
-    T.ToTensor(),
-    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-]
-
-train_transform = T.Compose([
-    T.Resize(256, antialias=True),
-    T.RandomCrop(224),
-    T.RandomHorizontalFlip(),
-    *base_tf
-])
-
-val_transform = T.Compose([
-    T.Resize(256, antialias=True),
-    T.CenterCrop(224),
-    *base_tf
-])
-
-train_ds = EmoSetDataset(DATA_ROOT, "train", transform=train_transform)
-val_ds   = EmoSetDataset(DATA_ROOT, "val", transform=val_transform)
-
-train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True, num_workers=NUM_WORKERS, pin_memory=True)
-val_loader   = DataLoader(val_ds, batch_size=BATCH_SIZE, shuffle=False, num_workers=NUM_WORKERS, pin_memory=True)
-
-# Инициализация модели и оптимизатора
-model = timm.create_model("resnet50", pretrained=True, num_classes=8, drop_rate=0.3)
-model.to(DEVICE)
-
-criterion = nn.CrossEntropyLoss(label_smoothing=0.1)
-
-optimizer = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=1e-3)
-scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=EPOCHS)
-
-# Логика эпохи обучения
-def train_epoch(current_model, loader):
-    current_model.train()
-    total_loss, correct_preds, total_samples = 0.0, 0, 0
-
-    for imgs, labels in tqdm(loader, desc="Тренировка", leave=False, smoothing=0):
-        imgs, labels = imgs.to(DEVICE), labels.to(DEVICE)
-
-        optimizer.zero_grad(set_to_none=True)
-        logits = current_model(imgs)
-        loss = criterion(logits, labels)
-
-        loss.backward()
-        optimizer.step()
-
-        total_loss += loss.item() * imgs.size(0)
-        preds = logits.argmax(dim=1)
-        correct_preds += (preds == labels).sum().item()
-        total_samples += labels.size(0)
-
-    return total_loss / total_samples, correct_preds / total_samples
-
-# Логика эпохи валидации
-@torch.no_grad()
-def val_epoch(current_model, loader):
-    current_model.eval()
-    total_loss, correct_preds, total_samples = 0.0, 0, 0
-
-    for imgs, labels in tqdm(loader, desc="Валидация", leave=False, smoothing=0):
-        imgs, labels = imgs.to(DEVICE), labels.to(DEVICE)
-
-        logits = current_model(imgs)
-        loss = criterion(logits, labels)
-
-        total_loss += loss.item() * imgs.size(0)
-        preds = logits.argmax(dim=1)
-        correct_preds += (preds == labels).sum().item()
-        total_samples += labels.size(0)
-
-    return total_loss / total_samples, correct_preds / total_samples
-
-if __name__ == "__main__":
-    best_val_acc = 0.0
-    best_val_loss = float('inf')
-    epochs_no_improve = 0
-    checkpoint_path = "./emosetV2_resnet50_best.pth"
-
-    print("Старт обучения.")
-
-    for epoch in range(1, EPOCHS + 1):
-        train_loss, train_acc = train_epoch(model, train_loader)
-        val_loss, val_acc = val_epoch(model, val_loader)
-
-        scheduler.step()
-
-        print(f"[{epoch}/{EPOCHS}] Train Loss: {train_loss:.4f}, Acc: {train_acc:.4f} | Val Loss: {val_loss:.4f}, Acc: {val_acc:.4f}")
-
-        # Сохранение лучших весов по Accuracy
-        if val_acc > best_val_acc:
-            best_val_acc = val_acc
-            torch.save(model.state_dict(), checkpoint_path)
-            print(f"Сохранен чекпоинт (Acc: {best_val_acc:.4f})")
-
-        # Оценка переобучения по Loss (Early Stopping)
-        if val_loss < best_val_loss:
-            best_val_loss = val_loss
-            epochs_no_improve = 0
-        else:
-            epochs_no_improve += 1
-            if epochs_no_improve >= PATIENCE:
-                print(f"Ранняя остановка: метрика валидации не улучшается {PATIENCE} эпох.")
-                break
-
-    print("Процесс завершен.")

src/scripts/22_1_train_images_m.py

@@ -1,283 +0,0 @@
-import os
-import random
-import warnings
-from pathlib import Path
-from PIL import Image
-import pandas as pd
-import numpy as np
-from tqdm import tqdm
-
-import torch
-import torch.nn as nn
-from torch.utils.data import Dataset, DataLoader
-import torchvision.transforms as T
-import timm
-
-import matplotlib.pyplot as plt
-import seaborn as sns
-from sklearn.metrics import confusion_matrix
-
-# Подавление предупреждений цветовых профилей
-warnings.filterwarnings("ignore", message=".*Unknown Adobe color transform code.*")
-
-# Настройки окружения
-DATA_ROOT = Path("./NFS/Thesis/Emoset/EmoSet-118K")
-# ВАЖНО: Добавили путь для медиа файлов
-MEDIA_DIR = Path("./src/scripts/media")
-MEDIA_DIR.mkdir(parents=True, exist_ok=True)
-
-BATCH_SIZE = 64
-EPOCHS = 30
-LR = 5e-5
-NUM_WORKERS = 32
-PATIENCE = 7
-
-# Маппинг классов
-CLASS_MAPPING = {
-    "amusement": 0, "anger": 1, "awe": 2, "contentment": 3,
-    "disgust": 4, "excitement": 5, "fear": 6, "sadness": 7
-}
-# Инвертированный маппинг для графиков
-INV_CLASS_MAPPING = {v: k for k, v in CLASS_MAPPING.items()}
-CLASS_NAMES = [INV_CLASS_MAPPING[i] for i in range(len(CLASS_MAPPING))]
-
-DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Устройство: {DEVICE}")
-
-# Фиксация генераторов псевдослучайных чисел
-def set_seed(seed=42):
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed(seed)
-        torch.cuda.manual_seed_all(seed)
-
-set_seed()
-
-# Инициализация структур данных
-class EmoSetDataset(Dataset):
-    def __init__(self, root: Path | str, split: str, transform=None):
-        self.root = Path(root) / split
-        self.df = pd.read_csv(self.root / "labels.csv")
-        self.transform = transform
-        
-        # Фильтрация датафрейма
-        self.df = self.df[self.df["label"].isin(CLASS_MAPPING.keys())].reset_index(drop=True)
-
-    def __len__(self):
-        return len(self.df)
-
-    def __getitem__(self, idx):
-        row = self.df.iloc[idx]
-        img_path = self.root / "images" / row["filename"]
-
-        try:
-            img = Image.open(img_path).convert("RGB")
-        except Exception:
-            img = Image.new("RGB", (256, 256), (0, 0, 0))
-
-        if self.transform:
-            img_tensor = self.transform(img)
-        else:
-            img_tensor = T.ToTensor()(img)
-            
-        label_idx = CLASS_MAPPING[row["label"]]
-        return img_tensor, label_idx
-
-# Трансформации
-base_tf = [
-    T.ToTensor(),
-    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-]
-
-train_transform = T.Compose([
-    T.Resize(256, antialias=True),
-    T.RandomCrop(224),
-    T.RandomHorizontalFlip(),
-    *base_tf
-])
-
-val_transform = T.Compose([
-    T.Resize(256, antialias=True),
-    T.CenterCrop(224),
-    *base_tf
-])
-
-train_ds = EmoSetDataset(DATA_ROOT, "train", transform=train_transform)
-val_ds   = EmoSetDataset(DATA_ROOT, "val", transform=val_transform)
-
-train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True, num_workers=NUM_WORKERS, pin_memory=True)
-val_loader   = DataLoader(val_ds, batch_size=BATCH_SIZE, shuffle=False, num_workers=NUM_WORKERS, pin_memory=True)
-
-# Инициализация модели и оптимизатора
-model = timm.create_model("resnet50", pretrained=True, num_classes=8, drop_rate=0.3)
-model.to(DEVICE)
-
-criterion = nn.CrossEntropyLoss(label_smoothing=0.1)
-
-optimizer = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=1e-3)
-scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=EPOCHS)
-
-# Функции для отрисовки графиков
-def plot_learning_curves(history):
-    """Отрисовка графиков функции потерь и точности"""
-    epochs = range(1, len(history['train_loss']) + 1)
-    
-    plt.figure(figsize=(14, 5))
-    
-    # График Loss
-    plt.subplot(1, 2, 1)
-    plt.plot(epochs, history['train_loss'], 'b-', label='Train Loss')
-    plt.plot(epochs, history['val_loss'], 'r--', label='Validation Loss')
-    plt.title('График функции потерь (Loss)', fontsize=14)
-    plt.xlabel('Эпохи', fontsize=12)
-    plt.ylabel('Loss', fontsize=12)
-    plt.legend()
-    plt.grid(True, linestyle=':', alpha=0.7)
-    
-    # График Accuracy
-    plt.subplot(1, 2, 2)
-    plt.plot(epochs, history['train_acc'], 'b-', label='Train Accuracy')
-    plt.plot(epochs, history['val_acc'], 'r--', label='Validation Accuracy')
-    plt.title('График точности (Accuracy)', fontsize=14)
-    plt.xlabel('Эпохи', fontsize=12)
-    plt.ylabel('Accuracy', fontsize=12)
-    plt.legend()
-    plt.grid(True, linestyle=':', alpha=0.7)
-    
-    plt.tight_layout()
-    plot_path = MEDIA_DIR / "training_history.png"
-    plt.savefig(plot_path, dpi=300, bbox_inches='tight')
-    plt.close()
-    print(f"[INFO] График обучения сохранен в: {plot_path}")
-
-def plot_confusion_matrix(y_true, y_pred):
-    """Отрисовка тепловой матрицы ошибок"""
-    cm = confusion_matrix(y_true, y_pred)
-    
-    plt.figure(figsize=(10, 8))
-    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', 
-                xticklabels=CLASS_NAMES, yticklabels=CLASS_NAMES,
-                cbar_kws={'label': 'Количество сэмплов'})
-    
-    plt.title('Матрица ошибок (Confusion Matrix) - ResNet50', fontsize=16, pad=20)
-    plt.ylabel('Истинные классы (Ground Truth)', fontsize=12)
-    plt.xlabel('Предсказанные классы (Predicted)', fontsize=12)
-    
-    plt.xticks(rotation=45, ha='right')
-    plt.yticks(rotation=0)
-    
-    plt.tight_layout()
-    cm_path = MEDIA_DIR / "confusion_matrix_emoset.png"
-    plt.savefig(cm_path, dpi=300, bbox_inches='tight')
-    plt.close()
-    print(f"[INFO] Матрица ошибок сохранена в: {cm_path}")
-
-# Логика эпохи обучения
-def train_epoch(current_model, loader):
-    current_model.train()
-    total_loss, correct_preds, total_samples = 0.0, 0, 0
-
-    for imgs, labels in tqdm(loader, desc="Тренировка", leave=False, smoothing=0):
-        imgs, labels = imgs.to(DEVICE), labels.to(DEVICE)
-
-        optimizer.zero_grad(set_to_none=True)
-        logits = current_model(imgs)
-        loss = criterion(logits, labels)
-
-        loss.backward()
-        optimizer.step()
-
-        total_loss += loss.item() * imgs.size(0)
-        preds = logits.argmax(dim=1)
-        correct_preds += (preds == labels).sum().item()
-        total_samples += labels.size(0)
-
-    return total_loss / total_samples, correct_preds / total_samples
-
-# Логика эпохи валидации с сохранением предсказаний для матрицы ошибок
-@torch.no_grad()
-def val_epoch(current_model, loader, return_preds=False):
-    current_model.eval()
-    total_loss, correct_preds, total_samples = 0.0, 0, 0
-    all_preds, all_labels = [], []
-
-    for imgs, labels in tqdm(loader, desc="Валидация", leave=False, smoothing=0):
-        imgs, labels = imgs.to(DEVICE), labels.to(DEVICE)
-
-        logits = current_model(imgs)
-        loss = criterion(logits, labels)
-
-        total_loss += loss.item() * imgs.size(0)
-        preds = logits.argmax(dim=1)
-        
-        correct_preds += (preds == labels).sum().item()
-        total_samples += labels.size(0)
-        
-        if return_preds:
-            all_preds.extend(preds.cpu().numpy())
-            all_labels.extend(labels.cpu().numpy())
-
-    avg_loss = total_loss / total_samples
-    avg_acc = correct_preds / total_samples
-    
-    if return_preds:
-        return avg_loss, avg_acc, all_labels, all_preds
-    return avg_loss, avg_acc
-
-if __name__ == "__main__":
-    best_val_acc = 0.0
-    best_val_loss = float('inf')
-    epochs_no_improve = 0
-    checkpoint_path = "./emosetV2_resnet50_best.pth"
-    
-    # Словарь для хранения истории обучения
-    history = {
-        'train_loss': [], 'train_acc': [],
-        'val_loss': [], 'val_acc': []
-    }
-    
-    # Переменные для хранения лучших предсказаний для матрицы
-    best_labels, best_preds = [], []
-
-    print("Старт обучения.")
-
-    for epoch in range(1, EPOCHS + 1):
-        train_loss, train_acc = train_epoch(model, train_loader)
-        
-        # Получаем предсказания только если это может быть лучшая эпоха
-        val_loss, val_acc, val_labels, val_preds = val_epoch(model, val_loader, return_preds=True)
-
-        scheduler.step()
-
-        # Запись в историю
-        history['train_loss'].append(train_loss)
-        history['train_acc'].append(train_acc)
-        history['val_loss'].append(val_loss)
-        history['val_acc'].append(val_acc)
-
-        print(f"[{epoch}/{EPOCHS}] Train Loss: {train_loss:.4f}, Acc: {train_acc:.4f} | Val Loss: {val_loss:.4f}, Acc: {val_acc:.4f}")
-
-        # Сохранение лучших весов по Accuracy
-        if val_acc > best_val_acc:
-            best_val_acc = val_acc
-            best_labels = val_labels # Сохраняем предсказания лучшей модели
-            best_preds = val_preds
-            torch.save(model.state_dict(), checkpoint_path)
-            print(f"Сохранен чекпоинт (Acc: {best_val_acc:.4f})")
-
-        # Оценка переобучения по Loss (Early Stopping)
-        if val_loss < best_val_loss:
-            best_val_loss = val_loss
-            epochs_no_improve = 0
-        else:
-            epochs_no_improve += 1
-            if epochs_no_improve >= PATIENCE:
-                print(f"Ранняя остановка: метрика валидации не улучшается {PATIENCE} эпох.")
-                break
-
-    print("Процесс обучения завершен. Генерирую графики для диссертации...")
-    plot_learning_curves(history)
-    plot_confusion_matrix(best_labels, best_preds)
-    print("Все медиафайлы успешно созданы!")

src/scripts/23_1_extract_embeddings_m.py

@@ -1,171 +0,0 @@
-import os
-from pathlib import Path
-from PIL import Image
-import pandas as pd
-import numpy as np
-from tqdm import tqdm
-
-import torch
-from torch.utils.data import Dataset, DataLoader
-import torchvision.transforms as T
-import timm
-
-import matplotlib.pyplot as plt
-import seaborn as sns
-from sklearn.manifold import TSNE
-
-# Настройки путей для медиа
-MEDIA_DIR = Path("scripts/media")
-MEDIA_DIR.mkdir(parents=True, exist_ok=True)
-
-# Конфигурация путей для инференса и кэширования векторов
-DATA_ROOT = Path("./NFS/Thesis/Emoset/EmoSet-118K")
-MODEL_PATH = Path("./src/emoset_resnet50_best.pth")
-
-BATCH_SIZE = 128
-NUM_WORKERS = 32
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Вычисления перенесены на: {device}")
-
-class EmoSetFeatureDataset(Dataset):
-    def __init__(self, root: Path | str, split: str):
-        self.root = Path(root) / split
-        self.df = pd.read_csv(self.root / "labels.csv")
-
-        self.labels = sorted(self.df["label"].unique())
-        self.label2idx = {l: i for i, l in enumerate(self.labels)}
-        self.idx2label = {i: l for l, i in self.label2idx.items()}
-
-        # Для экстракции признаков аугментация отключена, используется строгий CenterCrop
-        self.transform = T.Compose([
-            T.Resize(256),
-            T.CenterCrop(224),
-            T.ToTensor(),
-            T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-        ])
-
-    def __len__(self):
-        return len(self.df)
-
-    def __getitem__(self, idx):
-        row = self.df.iloc[idx]
-        img_path = self.root / "images" / row["filename"]
-        
-        # Перехват битых файлов выборки
-        try:
-            img = Image.open(img_path).convert("RGB")
-        except Exception:
-            img = Image.new("RGB", (224, 224), (0, 0, 0))
-            
-        img_tensor = self.transform(img)
-        label_idx = self.label2idx[row["label"]]
-        
-        return img_tensor, label_idx
-
-def plot_tsne(embeddings, labels, idx2label, sample_limit=3000):
-    """Генерация t-SNE графика для диссертации"""
-    print(f"Построение t-SNE проекции для {sample_limit} сэмплов...")
-    
-    tsne_model = TSNE(n_components=2, perplexity=30, random_state=42)
-    embeddings_2d = tsne_model.fit_transform(embeddings[:sample_limit])
-    labels_subset = labels[:sample_limit]
-
-    plt.figure(figsize=(12, 9))
-    
-    # Используем более академическую палитру
-    scatter = plt.scatter(
-        embeddings_2d[:, 0],
-        embeddings_2d[:, 1],
-        c=labels_subset,
-        cmap="Set2", # Set2 лучше различается при печати
-        alpha=0.7,
-        s=20,
-        edgecolors='w', 
-        linewidths=0.5
-    )
-
-    # Формирование легенды
-    handles, _ = scatter.legend_elements()
-    legend_labels = [idx2label[i] for i in range(len(idx2label))]
-    
-    # Размещение легенды снаружи графика, чтобы не перекрывать данные
-    plt.legend(handles, legend_labels, title="Эмоциональные классы", 
-               bbox_to_anchor=(1.05, 1), loc='upper left')
-
-    plt.title("2D проекция скрытого пространства признаков (t-SNE)", pad=20, fontsize=14)
-    plt.xlabel("Первая главная компонента (t-SNE 1)", fontsize=12)
-    plt.ylabel("Вторая главная компонента (t-SNE 2)", fontsize=12)
-    plt.grid(True, linestyle='--', alpha=0.3)
-    
-    plt.tight_layout()
-    plot_path = MEDIA_DIR / "tsne_embeddings.png"
-    plt.savefig(plot_path, dpi=300, bbox_inches='tight')
-    plt.close()
-    print(f"[INFO] График t-SNE сохранен в: {plot_path}")
-
-
-if __name__ == "__main__":
-    test_ds = EmoSetFeatureDataset(DATA_ROOT, "test")
-    test_loader = DataLoader(
-        test_ds,
-        batch_size=BATCH_SIZE,
-        shuffle=False,  # Отключение шаффла для строгого соответствия индексов
-        num_workers=NUM_WORKERS,
-        pin_memory=True
-    )
-
-    print(f"Подготовлено для извлечения: {len(test_ds)} файлов.")
-    
-    # Инициализация модели и загрузка лучших весов
-    feature_extractor = timm.create_model(
-        "resnet50",
-        pretrained=False,
-        num_classes=len(test_ds.labels)
-    )
-
-    try:
-        checkpoint = torch.load(MODEL_PATH, map_location=device)
-        feature_extractor.load_state_dict(checkpoint)
-        print("Веса модели успешно загружены.")
-    except Exception as e:
-        print(f"Ошибка загрузки весов: {e}. Убедитесь, что модель обучена.")
-        exit(1)
-
-    # Удаление классификационного слоя (fc)
-    feature_extractor.reset_classifier(0)
-    feature_extractor.to(device)
-    feature_extractor.eval()
-    
-    print("Слой классификации удален. Модель готова к экстракции.")
-    
-    extracted_embeddings = []
-    extracted_labels = []
-
-    print("Старт пакетной экстракции признаков...")
-
-    with torch.no_grad():
-        for imgs, labels in tqdm(test_loader, desc="Экстракция"):
-            imgs = imgs.to(device)
-
-            # Получение вектора [BATCH_SIZE, 2048]
-            embeddings_batch = feature_extractor(imgs)
-            
-            extracted_embeddings.append(embeddings_batch.cpu().numpy())
-            extracted_labels.append(labels.numpy())
-
-    # Агрегация батчей в единые массивы
-    np_embeddings = np.concatenate(extracted_embeddings, axis=0)
-    np_labels = np.concatenate(extracted_labels, axis=0)
-
-    print(f"Размерность матрицы признаков: {np_embeddings.shape}")
-
-    # Сохранение артефактов
-    np.save("./src/emoset_test_embeddings.npy", np_embeddings)
-    np.save("./src/emoset_test_labels.npy", np_labels)
-    print("Матрицы успешно экспортированы в .npy файлы.")
-    
-    # Генерация медиа для диссертации
-    plot_tsne(np_embeddings, np_labels, test_ds.idx2label, sample_limit=3000)
-    
-    print("Процесс полностью завершен.")

src/scripts/23_aggregate_DEAM_timeline.py

@@ -1,69 +0,0 @@
-import pandas as pd
-from pathlib import Path
-from tqdm import tqdm
-
-# Конфигурация путей и целевых признаков
-BASE_DIR = Path("../../dataset/DEAM")
-MUSIC_DB_PATH = BASE_DIR / "music_db.csv"
-FEATURES_DIR = BASE_DIR / "features" / "features"
-OUTPUT_PATH = BASE_DIR / "music_db_enriched.csv"
-
-# Маппинг низкоуровневых признаков экстрактора (openSMILE/GeMAPS) в дескрипторы системы
-TARGET_FEATURES = {
-    'pcm_RMSenergy_sma_amean': 'energy',
-    'pcm_fftMag_spectralFlux_sma_amean': 'flux',
-    'pcm_fftMag_spectralCentroid_sma_amean': 'centroid',
-    'F0final_sma_amean': 'pitch',
-    'logHNR_sma_amean': 'hnr',
-    'pcm_zcr_sma_amean': 'zcr',
-    'pcm_fftMag_spectralEntropy_sma_amean': 'entropy',
-    'pcm_fftMag_psySharpness_sma_amean': 'sharpness'
-}
-
-def aggregate_acoustic_features():
-    if not MUSIC_DB_PATH.exists():
-        print(f"Базовый файл аннотаций не найден: {MUSIC_DB_PATH}")
-        return
-
-    print("Загрузка эмоциональной разметки DEAM...")
-    df_main = pd.read_csv(MUSIC_DB_PATH)
-    
-    print("Агрегация фреймовых акустических признаков...")
-    aggregated_data = []
-
-    # Итерация по трекам для сбора покадровых характеристик
-    for _, row in tqdm(df_main.iterrows(), total=len(df_main), desc="Обработка аудио-векторов"):
-        song_id = int(row['song_id'])
-        feature_file = FEATURES_DIR / f"{song_id}.csv"
-        
-        if feature_file.exists():
-            try:
-                # Чтение сырых векторов (формат csv с разделителем ';')
-                df_feat = pd.read_csv(feature_file, sep=';')
-                
-                # Усреднение характеристик по временной оси (time frames)
-                mean_features = df_feat[list(TARGET_FEATURES.keys())].mean()
-                
-                # Формирование агрегированной записи
-                track_data = {'song_id': song_id}
-                for orig_col, new_col in TARGET_FEATURES.items():
-                    track_data[new_col] = mean_features[orig_col]
-                    
-                aggregated_data.append(track_data)
-                
-            except Exception as e:
-                print(f"Ошибка парсинга файла {feature_file.name}: {e}")
-
-    # Слияние акустических дескрипторов с эмоциональными координатами (Inner Join)
-    df_features = pd.DataFrame(aggregated_data)
-    df_enriched = pd.merge(df_main, df_features, on='song_id', how='inner')
-    
-    # Очистка возможных артефактов NaN после агрегации
-    df_enriched = df_enriched.dropna(subset=list(TARGET_FEATURES.values()))
-    
-    df_enriched.to_csv(OUTPUT_PATH, index=False)
-    print(f"Экспорт завершен. Сформирована обогащенная база: {OUTPUT_PATH.name}")
-    print(f"Итоговый размер выборки: {len(df_enriched)} треков.")
-
-if __name__ == "__main__":
-    aggregate_acoustic_features()

src/scripts/24_train_regressor.py

@@ -1,80 +0,0 @@
-import joblib
-import numpy as np
-import pandas as pd
-from pathlib import Path
-
-from sklearn.linear_model import RidgeCV
-from sklearn.multioutput import MultiOutputRegressor
-from sklearn.preprocessing import StandardScaler
-from sklearn.pipeline import Pipeline
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import mean_squared_error, r2_score
-
-# Проекция дискретных классов эмоций на непрерывное пространство Рассела (Valence, Arousal)
-# Значения откалиброваны в диапазоне [1.0, 9.0]
-EMOTION_TO_VA_COORDS = {
-    0: (7.5, 6.5),  # amusement 
-    1: (2.0, 8.0),  # anger 
-    2: (6.5, 5.0),  # awe 
-    3: (7.0, 3.0),  # contentment 
-    4: (3.0, 6.0),  # disgust 
-    5: (8.0, 8.0),  # excitement 
-    6: (2.5, 7.5),  # fear 
-    7: (2.0, 2.0),  # sadness 
-}
-
-def train_va_regressor():
-    # Настройка путей
-    base_dir = Path(__file__).resolve().parent.parent
-    embeddings_path = base_dir / "emoset_test_embeddings.npy"
-    labels_path = base_dir / "emoset_test_labels.npy"
-    model_output_path = base_dir / "music_engine" / "va_regressor.pkl"
-
-    if not embeddings_path.exists() or not labels_path.exists():
-        print(f"Артефакты признаков не найдены в директории: {base_dir}")
-        return
-
-    print("Загрузка вектора признаков и меток классов...")
-    x_features = np.load(embeddings_path)
-    y_discrete = np.load(labels_path)
-
-    # Трансформация целевой переменной: классы -> непрерывные координаты V/A
-    y_continuous = np.array([EMOTION_TO_VA_COORDS[label] for label in y_discrete])
-    
-    x_train, x_test, y_train, y_test = train_test_split(
-        x_features, y_continuous, test_size=0.2, random_state=42
-    )
-
-    # Построение пайплайна: Z-масштабирование и L2-регуляризованная регрессия
-    # RidgeCV автоматически подбирает оптимальный гиперпараметр alpha (силу регуляризации)
-    print("Инициализация и обучение пайплайна RidgeCV...")
-    regression_pipeline = Pipeline([
-        ('scaler', StandardScaler()),
-        ('regressor', MultiOutputRegressor(RidgeCV(alphas=[0.1, 1.0, 10.0, 100.0, 1000.0])))
-    ])
-
-    regression_pipeline.fit(x_train, y_train)
-
-    # Оценка обобщающей способности модели
-    y_pred = regression_pipeline.predict(x_test)
-    
-    mse_score = mean_squared_error(y_test, y_pred)
-    r2 = r2_score(y_test, y_pred)
-
-    print("Обучение завершено. Метрики качества на тестовой выборке:")
-    print(f" - MSE: {mse_score:.4f}")
-    print(f" - R^2: {r2:.4f}")
-
-    # Диагностика дисперсии предсказаний
-    v_min, v_max = y_pred[:, 0].min(), y_pred[:, 0].max()
-    a_min, a_max = y_pred[:, 1].min(), y_pred[:, 1].max()
-    print(f"Распределение Valence (прогноз): [{v_min:.2f}, {v_max:.2f}] (Эталон: 1.0 - 9.0)")
-    print(f"Распределение Arousal (прогноз): [{a_min:.2f}, {a_max:.2f}] (Эталон: 1.0 - 9.0)")
-
-    # Экспорт обученного пайплайна
-    model_output_path.parent.mkdir(parents=True, exist_ok=True)
-    joblib.dump(regression_pipeline, model_output_path)
-    print(f"Пайплайн сохранен: {model_output_path.name}")
-
-if __name__ == "__main__":
-    train_va_regressor()

src/scripts/91_generate_metrics.py

@@ -1,97 +0,0 @@
-import numpy as np
-import pandas as pd
-import joblib
-from pathlib import Path
-from sklearn.metrics import mean_squared_error, r2_score
-
-# 1. Настройка путей
-embeddings_path = Path("./src/emoset_test_embeddings.npy")
-csv_path = Path("./NFS/Thesis/Emoset/EmoSet-118K/test/labels.csv")
-model_path = Path("./src/music_engine/va_regressor.pkl")
-
-output_dir = Path("./src/scripts/media")
-output_file = output_dir / "metrics_output.txt"
-
-# 2. Корректный маппинг 8 классов EmoSet в шкалу DEAM [1.0, 9.0]
-# Формула перевода из [-1, 1] в [1, 9]: 5.0 + (X * 4.0)
-EMO_TO_VA = {
-    "amusement":   [8.2, 6.6],  # Веселье (Высокий позитив, средняя энергия)
-    "awe":         [7.0, 7.4],  # Восхищение (Позитив, высокая энергия)
-    "contentment": [7.8, 3.4],  # Умиротворение (Позитив, низкая энергия)
-    "excitement":  [8.2, 8.2],  # Возбуждение (Макс. позитив, макс. энергия)
-    "anger":       [2.2, 7.8],  # Гнев (Глубокий негатив, высокая энергия)
-    "disgust":     [2.6, 6.6],  # Отвращение (Негатив, средняя энергия)
-    "fear":        [2.6, 8.2],  # Страх (Негатив, максимальная энергия)
-    "sadness":     [2.2, 2.6]   # Грусть (Глубокий негатив, низкая энергия)
-}
-
-def generate_slide_metrics():
-    print("[INFO] Загрузка тестовых артефактов...")
-    
-    if not all(p.exists() for p in [embeddings_path, csv_path, model_path]):
-        print("[ERROR] Проверьте наличие файлов данных или модели регрессора.")
-        return
-
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    # 3. Загрузка эмбеддингов и меток
-    X_test = np.load(embeddings_path)
-    df = pd.read_csv(csv_path)
-    
-    if len(X_test) != len(df):
-        print(f"[WARN] Корректировка размеров выборки: Эмбеддинги ({len(X_test)}) != Метки ({len(df)})")
-        min_len = min(len(X_test), len(df))
-        X_test = X_test[:min_len]
-        df = df.iloc[:min_len]
-
-    y_test_list = [EMO_TO_VA.get(label.lower().strip(), [5.0, 5.0]) for label in df['label']]
-    y_test = np.array(y_test_list)
-
-    # 4. Выполнение инференса
-    print("[INFO] Выполнение инференса регрессора на скрытом пространстве признаков...")
-    regressor = joblib.load(model_path)
-    y_pred = regressor.predict(X_test)
-    
-    # === БЛОК ДИАГНОСТИКИ ШКАЛЫ ===
-    print("\n" + "-"*50)
-    print(" ДИАГНОСТИКА ДИАПАЗОНОВ ЗНАЧЕНИЙ ".center(50))
-    print("-"*50)
-    print(f"Истинные (y_test) -> Мин: {y_test.min():.2f}, Макс: {y_test.max():.2f}, Среднее: {y_test.mean():.2f}")
-    print(f"Предсказания (y_pred) -> Мин: {y_pred.min():.2f}, Макс: {y_pred.max():.2f}, Среднее: {y_pred.mean():.2f}")
-    print("-"*50 + "\n")
-    # ==============================
-    
-    # 5. Расчет метрик
-    mse_v = mean_squared_error(y_test[:, 0], y_pred[:, 0])
-    r2_v = r2_score(y_test[:, 0], y_pred[:, 0])
-    
-    mse_a = mean_squared_error(y_test[:, 1], y_pred[:, 1])
-    r2_a = r2_score(y_test[:, 1], y_pred[:, 1])
-    
-    mse_total = mean_squared_error(y_test, y_pred)
-    r2_total = r2_score(y_test, y_pred)
-    
-    # 6. Вывод и сохранение результатов
-    table_content = f"""
-==================================================
-          ТАБЛИЦА МЕТРИК ДЛЯ СЛАЙДА 10          
-==================================================
-| Метрика    | Valence (V)  | Arousal (A)  | Общая (Total) |
-|------------|--------------|--------------|---------------|
-| MSE        | {mse_v:<12.4f} | {mse_a:<12.4f} | {mse_total:<13.4f} |
-| R²         | {r2_v:<12.4f} | {r2_a:<12.4f} | {r2_total:<13.4f} |
-==================================================
-
-Формула целевой функции для вставки на слайд (LaTeX):
-$$Score_{{final}} = D_{{emo}} + 4.0 \cdot Acoustic_{{penalty}}$$
-"""
-
-    print(table_content)
-
-    with open(output_file, 'w', encoding='utf-8') as f:
-        f.write(table_content)
-        
-    print(f"[SUCCESS] Метрики успешно сохранены в файл: {output_file.absolute()}")
-
-if __name__ == "__main__":
-    generate_slide_metrics()

src/scripts/acc_images_model.ipynb

@@ -2,30 +2,30 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "8523d028",
    "metadata": {},
    "outputs": [],
    "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "\n",
     "import pandas as pd\n",
-    "import numpy as np\n",
     "from pathlib import Path\n",
     "from PIL import Image\n",
     "from tqdm import tqdm\n",
     "\n",
-    "import torch\n",
-    "from torch.utils.data import Dataset, DataLoader\n",
     "import torchvision.transforms as T\n",
     "import timm\n",
+    "import numpy as np\n",
     "\n",
-    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
+    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n"
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "e0781b02",
    "metadata": {},
    "outputs": [
@@ -41,26 +41,25 @@
     }
    ],
    "source": [
-    "# Конфигурация путей и параметров инференса\n",
     "DATA_ROOT = Path(\"../dataset/EmoSet-118K\")\n",
     "MODEL_PATH = Path(\"./emoset_resnet50_best.pth\")\n",
     "\n",
     "BATCH_SIZE = 64\n",
     "NUM_WORKERS = 4\n",
-    "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "print(f\"Аппаратное ускорение: {DEVICE}\")"
+    "\n",
+    "DEVICE\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "79da9640",
    "metadata": {},
    "outputs": [],
    "source": [
-    "class EmoSetEvaluationDataset(Dataset):\n",
+    "class EmoSetDataset(Dataset):\n",
-    "    # Датасет для строгой валидации с центрированным кропом\n",
+    "    def __init__(self, root, split):\n",
-    "    def __init__(self, root: Path | str, split: str):\n",
     "        self.root = Path(root) / split\n",
     "        self.df = pd.read_csv(self.root / \"labels.csv\")\n",
     "\n",
@@ -68,12 +67,13 @@
     "        self.label2idx = {l: i for i, l in enumerate(self.labels)}\n",
     "        self.idx2label = {i: l for l, i in self.label2idx.items()}\n",
     "\n",
-    "        # Стандартный пайплайн трансформаций для инференса ResNet\n",
     "        self.transform = T.Compose([\n",
-    "            T.Resize(256),\n",
+    "            T.Resize((224, 224)),\n",
-    "            T.CenterCrop(224),\n",
     "            T.ToTensor(),\n",
-    "            T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
+    "            T.Normalize(\n",
+    "                mean=[0.485, 0.456, 0.406],\n",
+    "                std=[0.229, 0.224, 0.225]\n",
+    "            )\n",
     "        ])\n",
     "\n",
     "    def __len__(self):\n",
@@ -81,23 +81,15 @@
     "\n",
     "    def __getitem__(self, idx):\n",
     "        row = self.df.iloc[idx]\n",
-    "        img_path = self.root / \"images\" / row[\"filename\"]\n",
+    "        img = Image.open(self.root / \"images\" / row[\"filename\"]).convert(\"RGB\")\n",
-    "        \n",
+    "        img = self.transform(img)\n",
-    "        # Перехват битых файлов для непрерывности оценки\n",
+    "        label = self.label2idx[row[\"label\"]]\n",
-    "        try:\n",
+    "        return img, label\n"
-    "            img = Image.open(img_path).convert(\"RGB\")\n",
-    "        except Exception:\n",
-    "            img = Image.new(\"RGB\", (224, 224), (0, 0, 0))\n",
-    "            \n",
-    "        img_tensor = self.transform(img)\n",
-    "        label_idx = self.label2idx[row[\"label\"]]\n",
-    "        \n",
-    "        return img_tensor, label_idx"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "12201756",
    "metadata": {},
    "outputs": [
@@ -111,8 +103,8 @@
     }
    ],
    "source": [
-    "# Инициализация тестовой выборки\n",
+    "test_ds = EmoSetDataset(DATA_ROOT, \"test\")\n",
-    "test_ds = EmoSetEvaluationDataset(DATA_ROOT, \"test\")\n",
+    "\n",
     "test_loader = DataLoader(\n",
     "    test_ds,\n",
     "    batch_size=BATCH_SIZE,\n",
@@ -121,13 +113,13 @@
     "    pin_memory=True\n",
     ")\n",
     "\n",
-    "print(f\"Индексированные классы: {test_ds.labels}\")\n",
+    "print(\"Classes:\", test_ds.labels)\n",
-    "print(f\"Размер тестовой выборки: {len(test_ds)}\")"
+    "print(\"Test samples:\", len(test_ds))\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "7e3dc1d5",
    "metadata": {},
    "outputs": [
@@ -382,17 +374,22 @@
     }
    ],
    "source": [
-    "# Инициализация модели в режиме классификации\n",
     "model = timm.create_model(\n",
     "    \"resnet50\",\n",
     "    pretrained=False,\n",
     "    num_classes=len(test_ds.labels)\n",
-    ")"
+    ")\n",
+    "\n",
+    "state = torch.load(MODEL_PATH, map_location=DEVICE)\n",
+    "model.load_state_dict(state)\n",
+    "\n",
+    "model.to(DEVICE)\n",
+    "model.eval()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "b42a84f1",
    "metadata": {},
    "outputs": [
@@ -405,16 +402,27 @@
     }
    ],
    "source": [
-    "# Загрузка весов и перевод в режим инференса\n",
+    "all_preds = []\n",
-    "checkpoint = torch.load(MODEL_PATH, map_location=DEVICE)\n",
+    "all_targets = []\n",
-    "model.load_state_dict(checkpoint)\n",
+    "\n",
-    "model.to(DEVICE)\n",
+    "with torch.no_grad():\n",
-    "model.eval()"
+    "    for imgs, labels in tqdm(test_loader):\n",
+    "        imgs = imgs.to(DEVICE)\n",
+    "        labels = labels.to(DEVICE)\n",
+    "\n",
+    "        logits = model(imgs)\n",
+    "        preds = logits.argmax(dim=1)\n",
+    "\n",
+    "        all_preds.append(preds.cpu().numpy())\n",
+    "        all_targets.append(labels.cpu().numpy())\n",
+    "\n",
+    "all_preds = np.concatenate(all_preds)\n",
+    "all_targets = np.concatenate(all_targets)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "4c1f1377",
    "metadata": {},
    "outputs": [
@@ -427,25 +435,13 @@
     }
    ],
    "source": [
-    "# Сбор предсказаний на тестовой выборке\n",
+    "acc = accuracy_score(all_targets, all_preds)\n",
-    "all_preds = []\n",
+    "print(f\"Test accuracy: {acc:.4f}\")\n"
-    "all_targets = []\n",
-    "\n",
-    "print(\"Запуск инференса на тестовой выборке...\")\n",
-    "with torch.no_grad():\n",
-    "    for imgs, labels in tqdm(test_loader, desc=\"Оценка метрик\"):\n",
-    "        imgs = imgs.to(DEVICE)\n",
-    "        \n",
-    "        logits = model(imgs)\n",
-    "        preds = logits.argmax(dim=1)\n",
-    "\n",
-    "        all_preds.append(preds.cpu().numpy())\n",
-    "        all_targets.append(labels.numpy())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "6b022825",
    "metadata": {},
    "outputs": [
@@ -472,14 +468,19 @@
     }
    ],
    "source": [
-    "# Агрегация результатов\n",
+    "print(\n",
-    "all_preds = np.concatenate(all_preds, axis=0)\n",
+    "    classification_report(\n",
-    "all_targets = np.concatenate(all_targets, axis=0)"
+    "        all_targets,\n",
+    "        all_preds,\n",
+    "        target_names=test_ds.labels,\n",
+    "        digits=4\n",
+    "    )\n",
+    ")\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "2fcb69ac",
    "metadata": {},
    "outputs": [
@@ -495,70 +496,20 @@
     }
    ],
    "source": [
-    "# Расчет интегральных метрик классификации\n",
+    "import matplotlib.pyplot as plt\n",
-    "acc = accuracy_score(all_targets, all_preds)\n",
-    "print(f\"\\nОбщая точность (Accuracy): {acc:.4f}\\n\")\n",
     "\n",
-    "print(\"Детализированный отчет (Classification Report):\")\n",
-    "print(\n",
-    "    classification_report(\n",
-    "        all_targets,\n",
-    "        all_preds,\n",
-    "        target_names=test_ds.labels,\n",
-    "        digits=4\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2084ab91",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Построение матрицы ошибок (Confusion Matrix)\n",
     "cm = confusion_matrix(all_targets, all_preds)\n",
     "\n",
-    "plt.figure(figsize=(10, 8))"
+    "plt.figure(figsize=(8, 8))\n",
-   ]
+    "plt.imshow(cm)\n",
-  },
+    "plt.colorbar()\n",
-  {
+    "plt.xticks(range(len(test_ds.labels)), test_ds.labels, rotation=45)\n",
-   "cell_type": "code",
+    "plt.yticks(range(len(test_ds.labels)), test_ds.labels)\n",
-   "execution_count": null,
+    "plt.xlabel(\"Predicted\")\n",
-   "id": "83a84e14",
+    "plt.ylabel(\"True\")\n",
-   "metadata": {},
+    "plt.title(\"Confusion Matrix (Test)\")\n",
-   "outputs": [],
+    "plt.tight_layout()\n",
-   "source": [
+    "plt.show()\n"
-    "# Использование seaborn для академичной визуализации с числами\n",
-    "sns.heatmap(\n",
-    "    cm, \n",
-    "    annot=True, \n",
-    "    fmt=\"d\", \n",
-    "    cmap=\"Blues\", \n",
-    "    xticklabels=test_ds.labels, \n",
-    "    yticklabels=test_ds.labels,\n",
-    "    cbar=False\n",
-    ")\n",
-    "\n",
-    "plt.title(\"Матрица ошибок классификации EmoSet (ResNet-50)\", pad=20)\n",
-    "plt.xlabel(\"Предсказанный класс\", labelpad=15)\n",
-    "plt.ylabel(\"Истинный класс\", labelpad=15)\n",
-    "plt.xticks(rotation=45)\n",
-    "plt.yticks(rotation=0)\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "280d5637",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Экспорт графика\n",
-    "plt.savefig(\"../confusion_matrix_resnet50.png\", dpi=300, bbox_inches='tight')\n",
-    "plt.show()"
    ]
   }
  ],

src/scripts/aggregate_features.ipynb

@@ -0,0 +1,125 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0336fd0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ База загружена. Треков: 1744\n",
+      "🔍 Собираем акустические признаки...\n",
+      "\n",
+      "🚀 ГОТОВО! Обогащенная база сохранена: ../../dataset/DEAM/music_db_enriched.csv\n",
+      "Собрано фичей для 1744 из 1744 треков.\n",
+      "   song_id  valence  arousal    energy      flux    centroid       pitch  \\\n",
+      "0        2      3.1      3.0  0.097268  0.846947  483.421751   93.884056   \n",
+      "1        3      3.5      3.3  0.126809  0.959460  173.219616   62.682589   \n",
+      "2        4      5.7      5.5  0.156699  1.333944  466.434797   92.850316   \n",
+      "3        5      4.4      5.3  0.126455  1.009927  546.152506  158.673853   \n",
+      "4        7      5.8      6.4  0.268180  1.589191  175.369162   83.823484   \n",
+      "\n",
+      "         hnr       zcr   entropy  sharpness  \n",
+      "0   3.615380  0.034270  3.299075   0.426490  \n",
+      "1  -2.600122  0.017893  2.294971   0.165583  \n",
+      "2  -0.579130  0.042936  3.258138   0.395410  \n",
+      "3   1.751148  0.043781  3.514585   0.494367  \n",
+      "4  12.006770  0.014783  2.177862   0.170058  \n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from pathlib import Path\n",
+    "from tqdm import tqdm # для красивого прогресс-бара, если не установлен - убери\n",
+    "\n",
+    "# 1. Пути к файлам\n",
+    "base_dir = Path(\"../../dataset/DEAM\") # Поправь, если запускаешь из другого места\n",
+    "music_db_path = base_dir / \"music_db.csv\"\n",
+    "features_dir = base_dir / \"features\" / \"features\"\n",
+    "output_path = base_dir / \"music_db_enriched.csv\"\n",
+    "\n",
+    "# 2. Наш \"Золотой список\" (8 признаков)\n",
+    "target_columns = {\n",
+    "    'pcm_RMSenergy_sma_amean': 'energy',\n",
+    "    'pcm_fftMag_spectralFlux_sma_amean': 'flux',\n",
+    "    'pcm_fftMag_spectralCentroid_sma_amean': 'centroid',\n",
+    "    'F0final_sma_amean': 'pitch',\n",
+    "    'logHNR_sma_amean': 'hnr',\n",
+    "    'pcm_zcr_sma_amean': 'zcr',\n",
+    "    'pcm_fftMag_spectralEntropy_sma_amean': 'entropy',\n",
+    "    'pcm_fftMag_psySharpness_sma_amean': 'sharpness'\n",
+    "}\n",
+    "\n",
+    "# 3. Загружаем текущую базу с V/A\n",
+    "if not music_db_path.exists():\n",
+    "    print(f\"❌ ОШИБКА: Не найден файл {music_db_path}\")\n",
+    "else:\n",
+    "    df_main = pd.read_csv(music_db_path)\n",
+    "    print(f\"✅ База загружена. Треков: {len(df_main)}\")\n",
+    "\n",
+    "    # Подготавливаем новые колонки\n",
+    "    for col_name in target_columns.values():\n",
+    "        df_main[col_name] = np.nan\n",
+    "\n",
+    "    # 4. Проходимся по всем трекам и ищем их акустические CSV\n",
+    "    print(\"🔍 Собираем акустические признаки...\")\n",
+    "    found_count = 0\n",
+    "    \n",
+    "    for index, row in df_main.iterrows():\n",
+    "        song_id = int(row['song_id'])\n",
+    "        feature_file = features_dir / f\"{song_id}.csv\"\n",
+    "        \n",
+    "        if feature_file.exists():\n",
+    "            try:\n",
+    "                # Читаем CSV с признаками (разделитель там обычно точка с запятой)\n",
+    "                df_feat = pd.read_csv(feature_file, sep=';')\n",
+    "                \n",
+    "                # Усредняем значения по всем фреймам (одна песня разбита на сотни строк-фреймов)\n",
+    "                mean_features = df_feat[list(target_columns.keys())].mean()\n",
+    "                \n",
+    "                # Записываем в главную базу\n",
+    "                for orig_col, new_col in target_columns.items():\n",
+    "                    df_main.at[index, new_col] = mean_features[orig_col]\n",
+    "                    \n",
+    "                found_count += 1\n",
+    "            except Exception as e:\n",
+    "                print(f\"Ошибка чтения {feature_file}: {e}\")\n",
+    "                \n",
+    "    # 5. Сохраняем результат\n",
+    "    # Удаляем треки, для которых не нашлось фичей (если такие есть)\n",
+    "    df_main = df_main.dropna(subset=list(target_columns.values()))\n",
+    "    \n",
+    "    df_main.to_csv(output_path, index=False)\n",
+    "    print(f\"\\n🚀 ГОТОВО! Обогащенная база сохранена: {output_path}\")\n",
+    "    print(f\"Собрано фичей для {found_count} из {len(df_main)} треков.\")\n",
+    "    print(df_main.head())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (thesis)",
+   "language": "python",
+   "name": "thesis"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

src/scripts/download emo_dataset.ipynb

@@ -0,0 +1,614 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "09f9237a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: datasets in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (4.4.2)\n",
+      "Requirement already satisfied: tqdm in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (4.67.1)\n",
+      "Requirement already satisfied: pillow in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (12.1.0)\n",
+      "Requirement already satisfied: requests in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (2.32.5)\n",
+      "Requirement already satisfied: filelock in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from datasets) (3.20.3)\n",
+      "Requirement already satisfied: numpy>=1.17 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from datasets) (2.4.1)\n",
+      "Requirement already satisfied: pyarrow>=21.0.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from datasets) (22.0.0)\n",
+      "Requirement already satisfied: dill<0.4.1,>=0.3.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from datasets) (0.4.0)\n",
+      "Requirement already satisfied: pandas in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from datasets) (2.3.3)\n",
+      "Requirement already satisfied: httpx<1.0.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from datasets) (0.28.1)\n",
+      "Requirement already satisfied: xxhash in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from datasets) (3.6.0)\n",
+      "Requirement already satisfied: multiprocess<0.70.19 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from datasets) (0.70.18)\n",
+      "Requirement already satisfied: fsspec<=2025.10.0,>=2023.1.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (2025.10.0)\n",
+      "Requirement already satisfied: huggingface-hub<2.0,>=0.25.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from datasets) (1.3.1)\n",
+      "Requirement already satisfied: packaging in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from datasets) (25.0)\n",
+      "Requirement already satisfied: pyyaml>=5.1 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from datasets) (6.0.3)\n",
+      "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (3.13.3)\n",
+      "Requirement already satisfied: anyio in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from httpx<1.0.0->datasets) (4.12.1)\n",
+      "Requirement already satisfied: certifi in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from httpx<1.0.0->datasets) (2026.1.4)\n",
+      "Requirement already satisfied: httpcore==1.* in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from httpx<1.0.0->datasets) (1.0.9)\n",
+      "Requirement already satisfied: idna in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from httpx<1.0.0->datasets) (3.11)\n",
+      "Requirement already satisfied: h11>=0.16 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from httpcore==1.*->httpx<1.0.0->datasets) (0.16.0)\n",
+      "Requirement already satisfied: hf-xet<2.0.0,>=1.2.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from huggingface-hub<2.0,>=0.25.0->datasets) (1.2.0)\n",
+      "Requirement already satisfied: shellingham in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from huggingface-hub<2.0,>=0.25.0->datasets) (1.5.4)\n",
+      "Requirement already satisfied: typer-slim in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from huggingface-hub<2.0,>=0.25.0->datasets) (0.21.1)\n",
+      "Requirement already satisfied: typing-extensions>=4.1.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from huggingface-hub<2.0,>=0.25.0->datasets) (4.15.0)\n",
+      "Requirement already satisfied: charset_normalizer<4,>=2 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from requests) (3.4.4)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from requests) (2.6.3)\n",
+      "Requirement already satisfied: aiohappyeyeballs>=2.5.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (2.6.1)\n",
+      "Requirement already satisfied: aiosignal>=1.4.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (1.4.0)\n",
+      "Requirement already satisfied: attrs>=17.3.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (25.4.0)\n",
+      "Requirement already satisfied: frozenlist>=1.1.1 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (1.8.0)\n",
+      "Requirement already satisfied: multidict<7.0,>=4.5 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (6.7.0)\n",
+      "Requirement already satisfied: propcache>=0.2.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (0.4.1)\n",
+      "Requirement already satisfied: yarl<2.0,>=1.17.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.10.0,>=2023.1.0->datasets) (1.22.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from pandas->datasets) (2.9.0.post0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from pandas->datasets) (2025.2)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from pandas->datasets) (2025.3)\n",
+      "Requirement already satisfied: six>=1.5 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n",
+      "Requirement already satisfied: click>=8.0.0 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from typer-slim->huggingface-hub<2.0,>=0.25.0->datasets) (8.3.1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install datasets tqdm pillow requests\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6f0b2e2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95f07577d20642b09f2cda6f0b2cca14",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Resolving data files:   0%|          | 0/18 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "868d872a109d49f9966f2f19985e7048",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Resolving data files:   0%|          | 0/18 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "06741794289540849ad179c5966dcab8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0/18 [00:00<?, ?files/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e47aad5270144913996cb5b226213ab9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/train-00000-of-00018.parquet:   0%|          | 0.00/509M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "30d1492a948245e3b6b58e92218cd760",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/train-00001-of-00018.parquet:   0%|          | 0.00/504M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "931823b458cb4696b459e9011537cf1e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/train-00002-of-00018.parquet:   0%|          | 0.00/489M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "846f4245b16d4cc096a43c940590ad11",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/train-00003-of-00018.parquet:   0%|          | 0.00/507M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71df201ff1a24811af67458c3fe3f2f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/train-00004-of-00018.parquet:   0%|          | 0.00/495M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "404dce6c69fc413dbe4aa84c289a0ab6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/train-00005-of-00018.parquet:   0%|          | 0.00/501M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e52b0bbbfdd14c599f44f02a48542317",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/train-00006-of-00018.parquet:   0%|          | 0.00/510M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "172981d77fc941cfa32c05f5a34bf742",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/train-00007-of-00018.parquet:   0%|          | 0.00/497M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cc9d886ff22f4165bf696c8b4d758931",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/train-00008-of-00018.parquet:   0%|          | 0.00/512M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5f118a9923c64ee2aa2001a1414927a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/train-00009-of-00018.parquet:   0%|          | 0.00/502M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "db61d8d556dc4574adbd8f916f790fa7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/train-00010-of-00018.parquet:   0%|          | 0.00/507M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75414190b19c4affbe190f6dd4f7bc4f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/train-00011-of-00018.parquet:   0%|          | 0.00/500M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "172aa22ed0c44a289e0ac68b240c13c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/train-00012-of-00018.parquet:   0%|          | 0.00/504M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2baa935ed3524a73883909752cb15907",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/train-00013-of-00018.parquet:   0%|          | 0.00/491M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e716611b29b44788e0bf2e7ad05be5b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/train-00014-of-00018.parquet:   0%|          | 0.00/502M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9c0baac101b449794155392f07b49c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/train-00015-of-00018.parquet:   0%|          | 0.00/504M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b31cdc7f17ac4ac8a04593e8a01a300a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/train-00016-of-00018.parquet:   0%|          | 0.00/507M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed6766f750c54b4194957bfe3db78ed6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/train-00017-of-00018.parquet:   0%|          | 0.00/494M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5454d2ecded64b82a12823f02a7ab12d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/val-00000-of-00002.parquet:   0%|          | 0.00/282M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "62dd1439e0514c98b0c24cc8f600c57e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/val-00001-of-00002.parquet:   0%|          | 0.00/283M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a5b966f79314e069251462bff82395f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/test-00000-of-00004.parquet:   0%|          | 0.00/422M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "422974f938924910a0712b30a9c2bd84",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/test-00001-of-00004.parquet:   0%|          | 0.00/430M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f155a08427094de7ad1a5884e623db2b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/test-00002-of-00004.parquet:   0%|          | 0.00/420M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a94a4621d19f45f690e0064fee83767b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "data/test-00003-of-00004.parquet:   0%|          | 0.00/422M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50f55b00a27b4213b573b398e5b0d708",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split:   0%|          | 0/94481 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8658b8414f604f0ca2fd248a214ad4aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating val split:   0%|          | 0/5905 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d59b7dea75f84b64bb8b262b43730e51",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating test split:   0%|          | 0/17716 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0c5815040f0a4a31903348a8327811a5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading dataset shards:   0%|          | 0/18 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DatasetDict({\n",
+      "    train: Dataset({\n",
+      "        features: ['image', 'label', 'image_id', 'emotion', 'brightness', 'colorfulness', 'facial_expression', 'human_action', 'scene', 'object'],\n",
+      "        num_rows: 94481\n",
+      "    })\n",
+      "    val: Dataset({\n",
+      "        features: ['image', 'label', 'image_id', 'emotion', 'brightness', 'colorfulness', 'facial_expression', 'human_action', 'scene', 'object'],\n",
+      "        num_rows: 5905\n",
+      "    })\n",
+      "    test: Dataset({\n",
+      "        features: ['image', 'label', 'image_id', 'emotion', 'brightness', 'colorfulness', 'facial_expression', 'human_action', 'scene', 'object'],\n",
+      "        num_rows: 17716\n",
+      "    })\n",
+      "})\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "from pathlib import Path\n",
+    "from PIL import Image\n",
+    "import requests\n",
+    "\n",
+    "# куда сохраняем датасет\n",
+    "DATA_DIR = Path(\"../dataset/EmoSet-118K\")\n",
+    "DATA_DIR.mkdir(exist_ok=True, parents=True)\n",
+    "\n",
+    "# загружаем через Hugging Face\n",
+    "ds = load_dataset(\"Woleek/EmoSet-118K\")\n",
+    "\n",
+    "print(ds)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "052ab073",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm\n",
+    "from pathlib import Path\n",
+    "\n",
+    "def save_split(split):\n",
+    "    split_dir = DATA_DIR / split\n",
+    "    img_dir = split_dir / \"images\"\n",
+    "    img_dir.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "    labels_path = split_dir / \"labels.csv\"\n",
+    "\n",
+    "    # перезаписываем labels.csv\n",
+    "    with open(labels_path, \"w\") as f:\n",
+    "        f.write(\"filename,label\\n\")\n",
+    "\n",
+    "    for example in tqdm(ds[split]):\n",
+    "        img = example[\"image\"]          # уже PIL.Image\n",
+    "        label = example[\"emotion\"]\n",
+    "        image_id = example[\"image_id\"]\n",
+    "\n",
+    "        fname = f\"{image_id}.jpg\"\n",
+    "        img.save(img_dir / fname)\n",
+    "\n",
+    "        with open(labels_path, \"a\") as f:\n",
+    "            f.write(f\"{fname},{label}\\n\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a74ceedf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 94481/94481 [18:43<00:00, 84.10it/s]  \n",
+      "100%|██████████| 5905/5905 [01:08<00:00, 86.57it/s] \n",
+      "100%|██████████| 17716/17716 [02:57<00:00, 100.01it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "save_split(\"train\")\n",
+    "save_split(\"val\")\n",
+    "save_split(\"test\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "thesis-py3.11",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

src/scripts/download_DEAM.ipynb

@@ -0,0 +1,140 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Загрузка датасета DEAM\n",
+    "\n",
+    "Этот ноутбук предназначен для автоматизации процесса скачивания и подготовки музыкального датасета **DEAM** (Database for Emotional Analysis in Music).\n",
+    "Данные будут помещены в папку `dataset/DEAM`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting kagglehub\n",
+      "  Downloading kagglehub-1.0.1-py3-none-any.whl.metadata (40 kB)\n",
+      "Collecting kagglesdk<1.0,>=0.1.22 (from kagglehub)\n",
+      "  Downloading kagglesdk-0.1.23-py3-none-any.whl.metadata (13 kB)\n",
+      "Requirement already satisfied: packaging in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from kagglehub) (25.0)\n",
+      "Requirement already satisfied: pyyaml in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from kagglehub) (6.0.3)\n",
+      "Requirement already satisfied: requests in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from kagglehub) (2.32.5)\n",
+      "Requirement already satisfied: tqdm in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from kagglehub) (4.67.1)\n",
+      "Requirement already satisfied: protobuf in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from kagglesdk<1.0,>=0.1.22->kagglehub) (6.33.4)\n",
+      "Requirement already satisfied: charset_normalizer<4,>=2 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from requests->kagglehub) (3.4.4)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from requests->kagglehub) (3.11)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from requests->kagglehub) (2.6.3)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /home/zin/projects/Thesis/.venv/lib/python3.11/site-packages (from requests->kagglehub) (2026.1.4)\n",
+      "Downloading kagglehub-1.0.1-py3-none-any.whl (70 kB)\n",
+      "Downloading kagglesdk-0.1.23-py3-none-any.whl (217 kB)\n",
+      "Installing collected packages: kagglesdk, kagglehub\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2/2\u001b[0m [kagglehub]\n",
+      "\u001b[1A\u001b[2KSuccessfully installed kagglehub-1.0.1 kagglesdk-0.1.23\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m26.1.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install kagglehub"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Скачиваем датасет DEAM...\n",
+      "Downloading to /home/zin/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/1.archive...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1.83G/1.83G [01:09<00:00, 28.2MB/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting files...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Датасет скачан во временную директорию: /home/zin/.cache/kagglehub/datasets/imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music/versions/1\n",
+      "Переносим файлы в ../dataset/DEAM...\n",
+      "\n",
+      "[УСПЕХ] Датасет DEAM готов к работе!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import shutil\n",
+    "import kagglehub\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# 1. Настройка путей\n",
+    "DATASET_ROOT = Path(\"../dataset\")\n",
+    "DEAM_ROOT = DATASET_ROOT / \"DEAM\"\n",
+    "DEAM_ROOT.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "# 2. Загрузка через kagglehub\n",
+    "print(\"Скачиваем датасет DEAM...\")\n",
+    "kaggle_cache_path = kagglehub.dataset_download(\"imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music\")\n",
+    "print(f\"Датасет скачан во временную директорию: {kaggle_cache_path}\")\n",
+    "\n",
+    "# 3. Перемещение файлов в проект\n",
+    "print(f\"Переносим файлы в {DEAM_ROOT}...\")\n",
+    "shutil.copytree(kaggle_cache_path, DEAM_ROOT, dirs_exist_ok=True)\n",
+    "\n",
+    "print(\"\\n[УСПЕХ] Датасет DEAM готов к работе!\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (my-python-project)",
+   "language": "python",
+   "name": "my-python-project"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

src/scripts/download_dataset.py

@@ -0,0 +1,171 @@
+import torch
+from torch.utils.data import Dataset, DataLoader
+from torchvision import transforms
+import os
+import json
+
+from PIL import Image
+
+
+class EmoSet(Dataset):
+    ATTRIBUTES_MULTI_CLASS = [
+        'scene', 'facial_expression', 'human_action', 'brightness', 'colorfulness',
+    ]
+    ATTRIBUTES_MULTI_LABEL = [
+        'object'
+    ]
+    NUM_CLASSES = {
+        'brightness': 11,
+        'colorfulness': 11,
+        'scene': 254,
+        'object': 409,
+        'facial_expression': 6,
+        'human_action': 264,
+    }
+
+    def __init__(self,
+                 data_root,
+                 num_emotion_classes,
+                 phase,
+                 ):
+        assert num_emotion_classes in (8, 2)
+        assert phase in ('train', 'val', 'test')
+        self.transforms_dict = self.get_data_transforms()
+
+        self.info = self.get_info(data_root, num_emotion_classes)
+
+        if phase == 'train':
+            self.transform = self.transforms_dict['train']
+        elif phase == 'val':
+            self.transform = self.transforms_dict['val']
+        elif phase == 'test':
+            self.transform = self.transforms_dict['test']
+        else:
+            raise NotImplementedError
+
+        data_store = json.load(open(os.path.join(data_root, f'{phase}.json')))
+        self.data_store = [
+            [
+                self.info['emotion']['label2idx'][item[0]],
+                item[1],
+                os.path.join(data_root, item[2]),
+                os.path.join(data_root, item[3])
+            ]
+            for item in data_store
+        ]
+
+    @classmethod
+    def get_data_transforms(cls):
+        transforms_dict = {
+            'train': transforms.Compose([
+                transforms.RandomResizedCrop(224),
+                transforms.RandomHorizontalFlip(),
+                transforms.ToTensor(),
+                transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+            ]),
+            'val': transforms.Compose([
+                transforms.Resize(224),
+                transforms.CenterCrop(224),
+                transforms.ToTensor(),
+                transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+            ]),
+            'test': transforms.Compose([
+                transforms.Resize(224),
+                transforms.CenterCrop(224),
+                transforms.ToTensor(),
+                transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+            ]),
+        }
+        return transforms_dict
+
+    def get_info(self, data_root, num_emotion_classes):
+        assert num_emotion_classes in (8, 2)
+        info = json.load(open(os.path.join(data_root, 'info.json')))
+        if num_emotion_classes == 8:
+            pass
+        elif num_emotion_classes == 2:
+            emotion_info = {
+                'label2idx': {
+                    'amusement': 0,
+                    'awe': 0,
+                    'contentment': 0,
+                    'excitement': 0,
+                    'anger': 1,
+                    'disgust': 1,
+                    'fear': 1,
+                    'sadness': 1,
+                },
+                'idx2label': {
+                    '0': 'positive',
+                    '1': 'negative',
+                }
+            }
+            info['emotion'] = emotion_info
+        else:
+            raise NotImplementedError
+
+        return info
+
+    def load_image_by_path(self, path):
+        image = Image.open(path).convert('RGB')
+        image = self.transform(image)
+        return image
+
+    def load_annotation_by_path(self, path):
+        json_data = json.load(open(path))
+        return json_data
+
+    def __getitem__(self, item):
+        emotion_label_idx, image_id, image_path, annotation_path = self.data_store[item]
+        image = self.load_image_by_path(image_path)
+        annotation_data = self.load_annotation_by_path(annotation_path)
+        data = {'image_id': image_id, 'image': image, 'emotion_label_idx': emotion_label_idx}
+
+        for attribute in self.ATTRIBUTES_MULTI_CLASS:
+            # if empty, set to -1, else set to label index
+            attribute_label_idx = -1
+            if attribute in annotation_data:
+                attribute_label_idx = self.info[attribute]['label2idx'][str(annotation_data[attribute])]
+            data.update({f'{attribute}_label_idx': attribute_label_idx})
+
+        for attribute in self.ATTRIBUTES_MULTI_LABEL:
+            # if empty, set to 0, else set to 1
+            assert attribute == 'object'
+            num_classes = self.NUM_CLASSES[attribute]
+            attribute_label_idx = torch.zeros(num_classes)
+            if attribute in annotation_data:
+                for label in annotation_data[attribute]:
+                    attribute_label_idx[self.info[attribute]['label2idx'][label]] = 1
+            data.update({f'{attribute}_label_idx': attribute_label_idx})
+
+        return data
+
+    def __len__(self):
+        return len(self.data_store)
+
+
+if __name__ == '__main__':
+    data_root = r'F:\common_file_system\EmoSet\EmoSet_v5_划分train-test-val'
+    num_emotion_classes = 8
+    phase = 'train'
+
+    dataset = EmoSet(
+        data_root=data_root,
+        num_emotion_classes=num_emotion_classes,
+        phase=phase,
+    )
+
+    # print(dataset.info)
+    dataloader = DataLoader(dataset, batch_size = 16, shuffle = True)
+
+    for i, data in enumerate(dataloader):
+        pass
+        # print(data['emotion_label_idx'])
+        # print(data['scene_label_idx'])
+        # print(data['facial_expression_label_idx'])
+        # print(data['human_action_label_idx'])
+        # print(data['brightness_label_idx'])
+        # print(data['colorfulness_label_idx'])
+        # print(data['object_label_idx'])
+        # break
+

src/scripts/media/metrics_output.txt

@@ -1,12 +0,0 @@
-
-==================================================
-          ТАБЛИЦА МЕТРИК ДЛЯ СЛАЙДА 10          
-==================================================
-| Метрика    | Valence (V)  | Arousal (A)  | Общая (Total) |
-|------------|--------------|--------------|---------------|
-| MSE        | 1.5135       | 2.2743       | 1.8939        |
-| R²         | 0.7927       | 0.4321       | 0.6124        |
-==================================================
-
-Формула целевой функции для вставки на слайд (LaTeX):
-$$Score_{final} = D_{emo} + 4.0 \cdot Acoustic_{penalty}$$

src/scripts/prep_deam.ipynb

@@ -0,0 +1,88 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b92e0213",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1763c51e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ УСПЕХ! База создана: ../../dataset/DEAM/music_db.csv\n",
+      "Всего треков в базе: 1744\n",
+      "Пример данных:\n",
+      "   song_id  valence  arousal\n",
+      "0        2      3.1      3.0\n",
+      "1        3      3.5      3.3\n",
+      "2        4      5.7      5.5\n",
+      "3        5      4.4      5.3\n",
+      "4        7      5.8      6.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Точный путь к оригинальным аннотациям\n",
+    "source_path = Path(\"../../dataset/DEAM/DEAM_Annotations/annotations/annotations averaged per song/song_level/static_annotations_averaged_songs_1_2000.csv\")\n",
+    "# Путь, куда сохраним очищенную базу для движка\n",
+    "output_path = Path(\"../../dataset/DEAM/music_db.csv\")\n",
+    "\n",
+    "if not source_path.exists():\n",
+    "    print(f\"❌ Исходный файл не найден по пути: {source_path}\")\n",
+    "else:\n",
+    "    # skipinitialspace=True уберет лишние пробелы в названиях колонок, если они есть\n",
+    "    df = pd.read_csv(source_path, skipinitialspace=True)\n",
+    "    \n",
+    "    # Берем только нужные колонки (по твоему примеру)\n",
+    "    clean_df = df[['song_id', 'valence_mean', 'arousal_mean']].copy()\n",
+    "    \n",
+    "    # Переименовываем для простоты кода в движке\n",
+    "    clean_df.columns = ['song_id', 'valence', 'arousal']\n",
+    "    \n",
+    "    # Приводим ID к целому числу (2, 3, 4...), чтобы искать файлы '2.mp3'\n",
+    "    clean_df['song_id'] = clean_df['song_id'].astype(int)\n",
+    "    \n",
+    "    # Сохраняем финальный файл\n",
+    "    clean_df.to_csv(output_path, index=False)\n",
+    "    \n",
+    "    print(f\"✅ УСПЕХ! База создана: {output_path}\")\n",
+    "    print(f\"Всего треков в базе: {len(clean_df)}\")\n",
+    "    print(\"Пример данных:\")\n",
+    "    print(clean_df.head())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (thesis)",
+   "language": "python",
+   "name": "thesis"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

src/scripts/prepare_dataset_to_train.ipynb

@@ -0,0 +1,114 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d70d8e32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from concurrent.futures import ProcessPoolExecutor\n",
+    "import pandas as pd\n",
+    "from pathlib import Path\n",
+    "from PIL import Image\n",
+    "import torch\n",
+    "from torchvision import transforms\n",
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "31b0fa82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATA_ROOT = Path(\"../dataset/EmoSet-118K\")\n",
+    "TRANSFORM = transforms.Compose([\n",
+    "    transforms.Resize((224,224)),\n",
+    "    transforms.ToTensor(),\n",
+    "    transforms.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225])\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1a17ecf5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/94481 [00:00<?, ?it/s]\n"
+     ]
+    },
+    {
+     "ename": "PicklingError",
+     "evalue": "Can't pickle <class 'pandas.core.frame.Pandas'>: attribute lookup Pandas on pandas.core.frame failed",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31m_RemoteTraceback\u001b[39m                          Traceback (most recent call last)",
+      "\u001b[31m_RemoteTraceback\u001b[39m: \n\"\"\"\nTraceback (most recent call last):\n  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 244, in _feed\n    obj = _ForkingPickler.dumps(obj)\n          ^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/reduction.py\", line 51, in dumps\n    cls(buf, protocol).dump(obj)\n_pickle.PicklingError: Can't pickle <class 'pandas.core.frame.Pandas'>: attribute lookup Pandas on pandas.core.frame failed\n\"\"\"",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[31mPicklingError\u001b[39m                             Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 20\u001b[39m\n\u001b[32m     18\u001b[39m     futures = [executor.submit(process_row, row, split_dir, tensor_dir) \u001b[38;5;28;01mfor\u001b[39;00m row \u001b[38;5;129;01min\u001b[39;00m df.itertuples()]\n\u001b[32m     19\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m tqdm(futures):\n\u001b[32m---> \u001b[39m\u001b[32m20\u001b[39m         results.append(\u001b[43mf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m     22\u001b[39m new_df = pd.DataFrame(results)\n\u001b[32m     23\u001b[39m new_df.to_csv(DATA_ROOT / split / \u001b[33m\"\u001b[39m\u001b[33mlabels_tensor.csv\u001b[39m\u001b[33m\"\u001b[39m, index=\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/_base.py:449\u001b[39m, in \u001b[36mFuture.result\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m    447\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[32m    448\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._state == FINISHED:\n\u001b[32m--> \u001b[39m\u001b[32m449\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    451\u001b[39m \u001b[38;5;28mself\u001b[39m._condition.wait(timeout)\n\u001b[32m    453\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/_base.py:401\u001b[39m, in \u001b[36mFuture.__get_result\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    399\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._exception:\n\u001b[32m    400\u001b[39m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m401\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m._exception\n\u001b[32m    402\u001b[39m     \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m    403\u001b[39m         \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[32m    404\u001b[39m         \u001b[38;5;28mself\u001b[39m = \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py:244\u001b[39m, in \u001b[36mQueue._feed\u001b[39m\u001b[34m(buffer, notempty, send_bytes, writelock, reader_close, writer_close, ignore_epipe, onerror, queue_sem)\u001b[39m\n\u001b[32m    241\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m    243\u001b[39m \u001b[38;5;66;03m# serialize the data before acquiring the lock\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m244\u001b[39m obj = \u001b[43m_ForkingPickler\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdumps\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    245\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m wacquire \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    246\u001b[39m     send_bytes(obj)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/reduction.py:51\u001b[39m, in \u001b[36mForkingPickler.dumps\u001b[39m\u001b[34m(cls, obj, protocol)\u001b[39m\n\u001b[32m     48\u001b[39m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[32m     49\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdumps\u001b[39m(\u001b[38;5;28mcls\u001b[39m, obj, protocol=\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m     50\u001b[39m     buf = io.BytesIO()\n\u001b[32m---> \u001b[39m\u001b[32m51\u001b[39m     \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mbuf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprotocol\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdump\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     52\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m buf.getbuffer()\n",
+      "\u001b[31mPicklingError\u001b[39m: Can't pickle <class 'pandas.core.frame.Pandas'>: attribute lookup Pandas on pandas.core.frame failed"
+     ]
+    }
+   ],
+   "source": [
+    "def process_row(row, split_dir, tensor_dir):\n",
+    "    img_path = split_dir / row.filename\n",
+    "    img = Image.open(img_path).convert(\"RGB\")\n",
+    "    tensor = TRANSFORM(img)\n",
+    "    tensor_path = tensor_dir / f\"{row.filename}.pt\"\n",
+    "    torch.save(tensor, tensor_path)\n",
+    "    return {\"tensor_path\": str(tensor_path), \"label\": row.label}\n",
+    "\n",
+    "for split in [\"train\",\"val\",\"test\"]:\n",
+    "    split_dir = DATA_ROOT / split / \"images\"\n",
+    "    tensor_dir = DATA_ROOT / split / \"tensors\"\n",
+    "    tensor_dir.mkdir(exist_ok=True, parents=True)\n",
+    "\n",
+    "    df = pd.read_csv(DATA_ROOT / split / \"labels.csv\")\n",
+    "\n",
+    "    results = []\n",
+    "    with ProcessPoolExecutor(max_workers=12) as executor:\n",
+    "        futures = [executor.submit(process_row, row, split_dir, tensor_dir) for row in df.itertuples()]\n",
+    "        for f in tqdm(futures):\n",
+    "            results.append(f.result())\n",
+    "\n",
+    "    new_df = pd.DataFrame(results)\n",
+    "    new_df.to_csv(DATA_ROOT / split / \"labels_tensor.csv\", index=False)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "thesis-py3.11",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

src/scripts/removed_31_finetune_2.41M.py

@@ -1,319 +0,0 @@
-import os
-import random
-import warnings
-from collections import defaultdict
-from pathlib import Path
-from PIL import Image, ImageFile
-import pandas as pd
-import numpy as np
-from tqdm import tqdm
-
-import torch
-import torch.nn as nn
-from torch.utils.data import Dataset, DataLoader
-import torchvision.transforms as T
-from torch.amp import autocast, GradScaler
-import timm
-
-# Подавление предупреждений и защита от битых "хвостов" JPEG
-warnings.filterwarnings("ignore")
-ImageFile.LOAD_TRUNCATED_IMAGES = True
-
-DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"Устройство: {DEVICE}")
-
-# --- ПУТИ ---
-TRAIN_ROOT = Path("./dataset/Original-2.41M")
-ANCHOR_118K_ROOT = Path("./NFS/Thesis/Emoset/EmoSet-118K/train") # ЯКОРЬ (Чистые данные для обучения)
-VAL_118K_ROOT = Path("./NFS/Thesis/Emoset/EmoSet-118K/val")
-
-SAVE_MODEL_PATH = Path("./src/emosetV2_resnet50_finetuned_2_41M.pth")
-RESUME_CHECKPOINT = Path("./src/finetuneV2_resume.pth")
-PRETRAINED_PATH = Path("./src/emosetV2_resnet50_best.pth")
-
-CLASS_MAPPING = {
-    "amusement": 0, "anger": 1, "awe": 2, "contentment": 3,
-    "disgust": 4, "excitement": 5, "fear": 6, "sadness": 7
-}
-
-# --- НАСТРОЙКИ ---
-TOTAL_BATCH_SIZE = 64
-BATCH_NOISY = 48  # 75% батча - новые данные 2.41M
-BATCH_ANCHOR = 16 # 25% батча - чистые якорные данные 118K
-
-EPOCHS_PER_FOLDER = 15
-PATIENCE = 5
-LR = 1e-6 
-NUM_TRAIN_WORKERS = 32
-NUM_VAL_WORKERS = 32
-
-def worker_init_fn(worker_id):
-    np.random.seed(np.random.get_state()[1][0] + worker_id)
-
-# --- 1. ТРАНСФОРМАЦИИ ---
-train_transform = T.Compose([
-    T.Resize(256),
-    T.RandomResizedCrop(224, scale=(0.8, 1.0)),
-    T.RandomHorizontalFlip(),
-    T.ToTensor(),
-    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-])
-
-val_transform = T.Compose([
-    T.Resize(256),
-    T.CenterCrop(224),
-    T.ToTensor(),
-    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-])
-
-# --- 2. ДАТАСЕТЫ ---
-class ChunkTrainDataset(Dataset):
-    def __init__(self, paths, transform):
-        self.paths = paths
-        self.transform = transform
-
-    def __len__(self):
-        return len(self.paths)
-
-    def __getitem__(self, idx):
-        path = self.paths[idx]
-        try:
-            img = Image.open(path).convert('RGB')
-            tensor = self.transform(img)
-            label = CLASS_MAPPING.get(path.parts[-3].lower(), 0)
-            return tensor, label
-        except Exception:
-            return torch.zeros((3, 224, 224)), 0
-
-class CsvDataset(Dataset):
-    def __init__(self, root, transform):
-        self.root = Path(root)
-        self.df = pd.read_csv(self.root / "labels.csv")
-        self.transform = transform
-
-    def __len__(self):
-        return len(self.df)
-
-    def __getitem__(self, idx):
-        row = self.df.iloc[idx]
-        path = self.root / "images" / row["filename"]
-        try:
-            img = Image.open(path).convert('RGB')
-            tensor = self.transform(img)
-            label = CLASS_MAPPING.get(row["label"].lower(), 0)
-            return tensor, label
-        except Exception:
-            return torch.zeros((3, 224, 224)), 0
-
-# --- 3. СБОР ДАННЫХ ---
-def prepare_chunks():
-    print("\nСканирование датасета 2.41M...")
-    chunk_dict = defaultdict(list)
-    for path in TRAIN_ROOT.rglob('*.jpg'):
-        emotion = path.parts[-3].lower()
-        if emotion not in CLASS_MAPPING: 
-            continue
-        folder_str = path.parts[-2]
-        if folder_str.isdigit():
-            chunk_dict[int(folder_str)].append(path)
-            
-    sorted_chunks = sorted(chunk_dict.keys())
-    print(f"Найдено пронумерованных папок (чанков): {len(sorted_chunks)}")
-    return chunk_dict, sorted_chunks
-    # --- 4. ОСНОВНОЙ ЦИКЛ ОБУЧЕНИЯ ---
-if __name__ == "__main__":
-    chunk_dict, sorted_chunks = prepare_chunks()
-
-    # Валидационный датасет (только чистые данные)
-    val_loader = DataLoader(
-        CsvDataset(VAL_118K_ROOT, val_transform), 
-        batch_size=TOTAL_BATCH_SIZE, shuffle=False, 
-        num_workers=NUM_VAL_WORKERS, pin_memory=True
-    )
-
-    # ЯКОРНЫЙ ЗАГРУЗЧИК (Чистые данные для подмешивания)
-    # Используем prefetch_factor и persistent_workers для устранения рывков CPU
-    anchor_dataset = CsvDataset(ANCHOR_118K_ROOT, train_transform)
-    anchor_loader = DataLoader(
-        anchor_dataset, batch_size=BATCH_ANCHOR, shuffle=True, 
-        num_workers=16, pin_memory=True, drop_last=True,
-        prefetch_factor=2, persistent_workers=False
-    )
-
-    # Инициализация модели
-    model = timm.create_model('resnet50', pretrained=False, num_classes=8).to(DEVICE)
-    if PRETRAINED_PATH.exists():
-        model.load_state_dict(torch.load(PRETRAINED_PATH, map_location=DEVICE))
-        print(f"Базовые веса загружены из {PRETRAINED_PATH.name}")
-
-    # Размораживаем всю модель
-    for param in model.parameters(): 
-        param.requires_grad = True
-
-    # Дифференцированный оптимизатор
-    backbone_params = [p for n, p in model.named_parameters() if "fc" not in n]
-    fc_params = [p for n, p in model.named_parameters() if "fc" in n]
-    
-    optimizer = torch.optim.AdamW([
-        {'params': backbone_params, 'lr': LR},         # 1e-6: микро-шаг для основы
-        {'params': fc_params, 'lr': LR * 10}           # 1e-5: шаг для классификатора
-    ], weight_decay=1e-3)
-
-    # Label Smoothing помогает игнорировать мусор в разметке 2.41M
-    criterion = nn.CrossEntropyLoss(label_smoothing=0.15)
-    scaler = GradScaler()
-
-    # --- ПАРАМЕТРЫ ВОССТАНОВЛЕНИЯ ---
-    start_stage = 0
-    start_epoch = 1
-    best_val_loss = float('inf')
-
-    if RESUME_CHECKPOINT.exists():
-        print(f"\nОбнаружен чекпоинт: {RESUME_CHECKPOINT.name}. Восстановление...")
-        checkpoint = torch.load(RESUME_CHECKPOINT, map_location=DEVICE)
-        model.load_state_dict(checkpoint['model_state_dict'])
-        
-        try: 
-            optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
-        except Exception as e: 
-            print(f"Оптимизатор сброшен: {e}")
-            
-        best_val_loss = checkpoint['best_val_loss']
-        start_stage = checkpoint['stage']
-        start_epoch = checkpoint['epoch'] + 1 
-        print(f"Успешный запуск с ЭТАПА {start_stage + 1}, Эпохи {start_epoch}. Best Val Loss: {best_val_loss:.4f}\n")
-    else:
-        # --- ЗАМЕР EPOCH 0 (БАЗОВАЯ ТОЧНОСТЬ) ---
-        # Выполняется только если мы начинаем с нуля
-        print("\n[Проверка базовых весов перед обучением (Epoch 0)]")
-        model.eval()
-        val_loss, val_correct, val_total = 0.0, 0, 0
-        with torch.no_grad():
-            for inputs, labels in tqdm(val_loader, desc="Baseline Eval", smoothing=0):
-                inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
-                with autocast(device_type="cuda"):
-                    outputs = model(inputs)
-                    v_loss = criterion(outputs, labels)
-                val_loss += v_loss.item() * inputs.size(0)
-                _, pred = outputs.max(1)
-                val_total += labels.size(0)
-                val_correct += pred.eq(labels).sum().item()
-        
-        best_val_loss = val_loss / val_total
-        baseline_acc = val_correct / val_total
-        print(f"Стартовая точка -> Val Loss: {best_val_loss:.4f} | Val Acc: {baseline_acc:.4f}\n")
-
-    # ВОССТАНОВЛЕНИЕ НАКОПЛЕННЫХ ДАННЫХ
-    current_train_paths = []
-    for s in range(start_stage):
-        current_train_paths.extend(chunk_dict[sorted_chunks[s]])
-
-    print("Старт Anchor Curriculum Learning (Смешивание чистых и шумных данных).")
-
-    # ГЛАВНЫЙ ЦИКЛ ПО ПАПКАМ
-    for stage in range(start_stage, len(sorted_chunks)):
-        chunk_id = sorted_chunks[stage]
-        print(f"\n{'='*50}")
-        print(f"ЭТАП {stage+1}/{len(sorted_chunks)}: Добавляем папку '{chunk_id}'")
-        
-        # Накопление и перемешивание
-        current_train_paths.extend(chunk_dict[chunk_id])
-        random.shuffle(current_train_paths)
-        print(f"Всего файлов (грязных) в текущем пуле: {len(current_train_paths)}")
-        
-        # ОСНОВНОЙ ЗАГРУЗЧИК (Грязные данные) с PREFETCH
-        train_loader = DataLoader(
-            ChunkTrainDataset(current_train_paths, train_transform), 
-            batch_size=BATCH_NOISY, shuffle=True, 
-            num_workers=NUM_TRAIN_WORKERS, pin_memory=True, 
-            worker_init_fn=worker_init_fn, drop_last=True,
-            prefetch_factor=4, persistent_workers=True # Устраняет рывки CPU
-        )
-
-        epochs_no_improve = 0
-        first_epoch = start_epoch if stage == start_stage else 1
-        
-        # Инициализация итератора якорей
-        anchor_iter = iter(anchor_loader)
-
-        # ЦИКЛ ЭПОХ ДЛЯ ТЕКУЩЕГО ЭТАПА
-        for epoch in range(first_epoch, EPOCHS_PER_FOLDER + 1):
-            model.train()
-            train_loss, train_correct, train_total = 0.0, 0, 0
-            
-            for noisy_inputs, noisy_labels in tqdm(train_loader, desc=f"S{stage+1}-Ep{epoch}/{EPOCHS_PER_FOLDER} [Train]", smoothing=0):
-                
-                # Достаем якорный чистый батч
-                try:
-                    anc_inputs, anc_labels = next(anchor_iter)
-                except StopIteration:
-                    anchor_iter = iter(anchor_loader)
-                    anc_inputs, anc_labels = next(anchor_iter)
-                
-                # СМЕШИВАЕМ БАТЧИ (Грязные + Чистые)
-                inputs = torch.cat([noisy_inputs, anc_inputs]).to(DEVICE)
-                labels = torch.cat([noisy_labels, anc_labels]).to(DEVICE)
-                
-                optimizer.zero_grad(set_to_none=True)
-                with autocast(device_type="cuda"):
-                    outputs = model(inputs)
-                    loss = criterion(outputs, labels)
-                    
-                scaler.scale(loss).backward()
-                scaler.step(optimizer)
-                scaler.update()
-                
-                train_loss += loss.item() * inputs.size(0)
-                _, pred = outputs.max(1)
-                train_total += labels.size(0)
-                train_correct += pred.eq(labels).sum().item()
-
-            # ВАЛИДАЦИЯ
-            model.eval()
-            val_loss, val_correct, val_total = 0.0, 0, 0
-            with torch.no_grad():
-                for inputs, labels in tqdm(val_loader, desc="[Val]", leave=False, smoothing=0):
-                    inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
-                    with autocast(device_type="cuda"):
-                        outputs = model(inputs)
-                        v_loss = criterion(outputs, labels)
-                    val_loss += v_loss.item() * inputs.size(0)
-                    _, pred = outputs.max(1)
-                    val_total += labels.size(0)
-                    val_correct += pred.eq(labels).sum().item()
-            
-            avg_train_loss = train_loss / train_total
-            avg_train_acc = train_correct / train_total
-            avg_val_loss = val_loss / val_total
-            avg_val_acc = val_correct / val_total
-            
-            print(f"S{stage+1}-E{epoch} | Train L: {avg_train_loss:.4f}, Acc: {avg_train_acc:.4f} | Val L: {avg_val_loss:.4f}, Acc: {avg_val_acc:.4f}")
-
-            # СОХРАНЕНИЕ ЛУЧШИХ ВЕСОВ
-            if avg_val_loss < best_val_loss:
-                best_val_loss = avg_val_loss
-                epochs_no_improve = 0
-                torch.save(model.state_dict(), SAVE_MODEL_PATH)
-                print("--> Обновлены лучшие веса")
-            else:
-                epochs_no_improve += 1
-
-            # АВАРИЙНОЕ СОХРАНЕНИЕ В КОНЦЕ ЭПОХИ
-            checkpoint_state = {
-                'stage': stage,
-                'epoch': epoch,
-                'model_state_dict': model.state_dict(),
-                'optimizer_state_dict': optimizer.state_dict(),
-                'best_val_loss': best_val_loss
-            }
-            torch.save(checkpoint_state, RESUME_CHECKPOINT)
-            os.sync() # Защита от отключения электричества
-            print(f"--> Чекпоинт (Этап {stage+1}, Эпоха {epoch}) зафиксирован на диске.")
-
-            # РАННЯЯ ОСТАНОВКА ДЛЯ ТЕКУЩЕГО ЭТАПА
-            if epochs_no_improve >= PATIENCE:
-                print(f"Ранняя остановка для ЭТАПА {stage+1}. Переход к следующей папке...")
-                break
-        
-        # Сброс счетчика стартовой эпохи после прохождения восстановительного этапа
-        start_epoch = 1

src/scripts/test.ipynb

@@ -0,0 +1,199 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ca08df84",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using device: cuda\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Step 0/1000, Loss: 1.0013\n",
+      "Step 10/1000, Loss: 1.0088\n",
+      "Step 20/1000, Loss: 0.9956\n",
+      "Step 30/1000, Loss: 0.9781\n",
+      "Step 40/1000, Loss: 0.9613\n",
+      "Step 50/1000, Loss: 0.9313\n",
+      "Step 60/1000, Loss: 0.8927\n",
+      "Step 70/1000, Loss: 0.8503\n",
+      "Step 80/1000, Loss: 0.7537\n",
+      "Step 90/1000, Loss: 0.6689\n",
+      "Step 100/1000, Loss: 0.6063\n",
+      "Step 110/1000, Loss: 0.5172\n",
+      "Step 120/1000, Loss: 0.4592\n",
+      "Step 130/1000, Loss: 0.4044\n",
+      "Step 140/1000, Loss: 0.3610\n",
+      "Step 150/1000, Loss: 0.3175\n",
+      "Step 160/1000, Loss: 0.2825\n",
+      "Step 170/1000, Loss: 0.2560\n",
+      "Step 180/1000, Loss: 0.2360\n",
+      "Step 190/1000, Loss: 0.2203\n",
+      "Step 200/1000, Loss: 0.1930\n",
+      "Step 210/1000, Loss: 0.1854\n",
+      "Step 220/1000, Loss: 0.1723\n",
+      "Step 230/1000, Loss: 0.1546\n",
+      "Step 240/1000, Loss: 0.1386\n",
+      "Step 250/1000, Loss: 0.1271\n",
+      "Step 260/1000, Loss: 0.1109\n",
+      "Step 270/1000, Loss: 0.1032\n",
+      "Step 280/1000, Loss: 0.0899\n",
+      "Step 290/1000, Loss: 0.0807\n",
+      "Step 300/1000, Loss: 0.0750\n",
+      "Step 310/1000, Loss: 0.0813\n",
+      "Step 320/1000, Loss: 0.0612\n",
+      "Step 330/1000, Loss: 0.0544\n",
+      "Step 340/1000, Loss: 0.0552\n",
+      "Step 350/1000, Loss: 0.0446\n",
+      "Step 360/1000, Loss: 0.0403\n",
+      "Step 370/1000, Loss: 0.0350\n",
+      "Step 380/1000, Loss: 0.0612\n",
+      "Step 390/1000, Loss: 0.0364\n",
+      "Step 400/1000, Loss: 0.0322\n",
+      "Step 410/1000, Loss: 0.0302\n",
+      "Step 420/1000, Loss: 0.0519\n",
+      "Step 430/1000, Loss: 0.0319\n",
+      "Step 440/1000, Loss: 0.0260\n",
+      "Step 450/1000, Loss: 0.0208\n",
+      "Step 460/1000, Loss: 0.0409\n",
+      "Step 470/1000, Loss: 0.0291\n",
+      "Step 480/1000, Loss: 0.0234\n",
+      "Step 490/1000, Loss: 0.0194\n",
+      "Step 500/1000, Loss: 0.0274\n",
+      "Step 510/1000, Loss: 0.0231\n",
+      "Step 520/1000, Loss: 0.0199\n",
+      "Step 530/1000, Loss: 0.0154\n",
+      "Step 540/1000, Loss: 0.0278\n",
+      "Step 550/1000, Loss: 0.0185\n",
+      "Step 560/1000, Loss: 0.0180\n",
+      "Step 570/1000, Loss: 0.0152\n",
+      "Step 580/1000, Loss: 0.0132\n",
+      "Step 590/1000, Loss: 0.0111\n",
+      "Step 600/1000, Loss: 0.0396\n",
+      "Step 610/1000, Loss: 0.0179\n",
+      "Step 620/1000, Loss: 0.0148\n",
+      "Step 630/1000, Loss: 0.0123\n",
+      "Step 640/1000, Loss: 0.0265\n",
+      "Step 650/1000, Loss: 0.0133\n",
+      "Step 660/1000, Loss: 0.0128\n",
+      "Step 670/1000, Loss: 0.0107\n",
+      "Step 680/1000, Loss: 0.0142\n",
+      "Step 690/1000, Loss: 0.0202\n",
+      "Step 700/1000, Loss: 0.0125\n",
+      "Step 710/1000, Loss: 0.0107\n",
+      "Step 720/1000, Loss: 0.0140\n",
+      "Step 730/1000, Loss: 0.0195\n",
+      "Step 740/1000, Loss: 0.0148\n",
+      "Step 750/1000, Loss: 0.0109\n",
+      "Step 760/1000, Loss: 0.0094\n",
+      "Step 770/1000, Loss: 0.0121\n",
+      "Step 780/1000, Loss: 0.0233\n",
+      "Step 790/1000, Loss: 0.0151\n",
+      "Step 800/1000, Loss: 0.0134\n",
+      "Step 810/1000, Loss: 0.0117\n",
+      "Step 820/1000, Loss: 0.0124\n",
+      "Step 830/1000, Loss: 0.0221\n",
+      "Step 840/1000, Loss: 0.0161\n",
+      "Step 850/1000, Loss: 0.0136\n",
+      "Step 860/1000, Loss: 0.0161\n",
+      "Step 870/1000, Loss: 0.0194\n",
+      "Step 880/1000, Loss: 0.0145\n",
+      "Step 890/1000, Loss: 0.0149\n",
+      "Step 900/1000, Loss: 0.0232\n",
+      "Step 910/1000, Loss: 0.0166\n",
+      "Step 920/1000, Loss: 0.0156\n",
+      "Step 930/1000, Loss: 0.0276\n",
+      "Step 940/1000, Loss: 0.0176\n",
+      "Step 950/1000, Loss: 0.0152\n",
+      "Step 960/1000, Loss: 0.0162\n",
+      "Step 970/1000, Loss: 0.0143\n",
+      "Step 980/1000, Loss: 0.0136\n",
+      "Step 990/1000, Loss: 0.0117\n",
+      "Total time: 67.25 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "import time\n",
+    "\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(\"Using device:\", device)\n",
+    "\n",
+    "\n",
+    "# Огромные параметры\n",
+    "N, D_in, H1, H2, H3, D_out = 300_000, 4096, 2048, 1024, 512, 10\n",
+    "batch_size = 16_384  # большой батч\n",
+    "steps = 1000          # много итераций для длительной нагрузки\n",
+    "\n",
+    "# Случайные данные на GPU\n",
+    "x = torch.randn(N, D_in, device=device, dtype=torch.float32)\n",
+    "y = torch.randn(N, D_out, device=device, dtype=torch.float32)\n",
+    "\n",
+    "model = nn.Sequential(\n",
+    "    nn.Linear(D_in, H1),\n",
+    "    nn.ReLU(),\n",
+    "    nn.Linear(H1, H2),\n",
+    "    nn.ReLU(),\n",
+    "    nn.Linear(H2, H3),\n",
+    "    nn.ReLU(),\n",
+    "    nn.Linear(H3, D_out)\n",
+    ").to(device)\n",
+    "\n",
+    "loss_fn = nn.MSELoss()\n",
+    "optimizer = optim.Adam(model.parameters(), lr=1e-3)\n",
+    "\n",
+    "start = time.time()\n",
+    "for t in range(steps):\n",
+    "    idx = torch.randint(0, N, (batch_size,), device=device)\n",
+    "    x_batch = x[idx]\n",
+    "    y_batch = y[idx]\n",
+    "\n",
+    "    y_pred = model(x_batch)\n",
+    "    loss = loss_fn(y_pred, y_batch)\n",
+    "\n",
+    "    optimizer.zero_grad()\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "\n",
+    "    if t % 10 == 0:\n",
+    "        # замедляем вывод, чтобы можно было наблюдать\n",
+    "        print(f\"Step {t}/{steps}, Loss: {loss.item():.4f}\")\n",
+    "\n",
+    "end = time.time()\n",
+    "print(f\"Total time: {end-start:.2f} s\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

src/scripts/train_images.ipynb

@@ -0,0 +1,759 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9336560f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0c00b67b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "import torchvision.transforms as T\n",
+    "\n",
+    "import pandas as pd\n",
+    "from pathlib import Path\n",
+    "from PIL import Image\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "import timm\n",
+    "import numpy as np\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "84c3657f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'cuda'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# === CONFIG ===\n",
+    "DATA_ROOT = Path(\"../dataset/EmoSet-118K\")\n",
+    "BATCH_SIZE = 64          # V100 спокойно тянет\n",
+    "EPOCHS = 15\n",
+    "LR = 3e-4\n",
+    "NUM_WORKERS = 24\n",
+    "\n",
+    "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "DEVICE\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9f749add",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class EmoSetDataset(Dataset):\n",
+    "    def __init__(self, root, split):\n",
+    "        self.root = Path(root) / split\n",
+    "        self.df = pd.read_csv(self.root / \"labels.csv\")\n",
+    "\n",
+    "        self.labels = sorted(self.df[\"label\"].unique())\n",
+    "        self.label2idx = {l: i for i, l in enumerate(self.labels)}\n",
+    "        self.idx2label = {i: l for l, i in self.label2idx.items()}\n",
+    "\n",
+    "        self.transform = T.Compose([\n",
+    "            T.Resize((224, 224)),\n",
+    "            T.ToTensor(),\n",
+    "            T.Normalize(\n",
+    "                mean=[0.485, 0.456, 0.406],\n",
+    "                std=[0.229, 0.224, 0.225]\n",
+    "            )\n",
+    "        ])\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.df)\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        row = self.df.iloc[idx]\n",
+    "        img_path = self.root / \"images\" / row[\"filename\"]\n",
+    "\n",
+    "        img = Image.open(img_path).convert(\"RGB\")\n",
+    "        img = self.transform(img)\n",
+    "\n",
+    "        label = self.label2idx[row[\"label\"]]\n",
+    "        return img, label\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c8805341",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classes: ['amusement', 'anger', 'awe', 'contentment', 'disgust', 'excitement', 'fear', 'sadness']\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_ds = EmoSetDataset(DATA_ROOT, \"train\")\n",
+    "val_ds   = EmoSetDataset(DATA_ROOT, \"val\")\n",
+    "\n",
+    "train_loader = DataLoader(\n",
+    "    train_ds,\n",
+    "    batch_size=BATCH_SIZE,\n",
+    "    shuffle=True,\n",
+    "    num_workers=NUM_WORKERS,\n",
+    "    pin_memory=True\n",
+    ")\n",
+    "\n",
+    "val_loader = DataLoader(\n",
+    "    val_ds,\n",
+    "    batch_size=BATCH_SIZE,\n",
+    "    shuffle=False,\n",
+    "    num_workers=NUM_WORKERS,\n",
+    "    pin_memory=True\n",
+    ")\n",
+    "\n",
+    "print(\"Classes:\", train_ds.labels)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "dffce582",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ResNet(\n",
+       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
+       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "  (act1): ReLU(inplace=True)\n",
+       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
+       "  (layer1): Sequential(\n",
+       "    (0): Bottleneck(\n",
+       "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act1): ReLU(inplace=True)\n",
+       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (drop_block): Identity()\n",
+       "      (act2): ReLU(inplace=True)\n",
+       "      (aa): Identity()\n",
+       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act3): ReLU(inplace=True)\n",
+       "      (downsample): Sequential(\n",
+       "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (1): Bottleneck(\n",
+       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act1): ReLU(inplace=True)\n",
+       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (drop_block): Identity()\n",
+       "      (act2): ReLU(inplace=True)\n",
+       "      (aa): Identity()\n",
+       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act3): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (2): Bottleneck(\n",
+       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act1): ReLU(inplace=True)\n",
+       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (drop_block): Identity()\n",
+       "      (act2): ReLU(inplace=True)\n",
+       "      (aa): Identity()\n",
+       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act3): ReLU(inplace=True)\n",
+       "    )\n",
+       "  )\n",
+       "  (layer2): Sequential(\n",
+       "    (0): Bottleneck(\n",
+       "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act1): ReLU(inplace=True)\n",
+       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (drop_block): Identity()\n",
+       "      (act2): ReLU(inplace=True)\n",
+       "      (aa): Identity()\n",
+       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act3): ReLU(inplace=True)\n",
+       "      (downsample): Sequential(\n",
+       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (1): Bottleneck(\n",
+       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act1): ReLU(inplace=True)\n",
+       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (drop_block): Identity()\n",
+       "      (act2): ReLU(inplace=True)\n",
+       "      (aa): Identity()\n",
+       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act3): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (2): Bottleneck(\n",
+       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act1): ReLU(inplace=True)\n",
+       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (drop_block): Identity()\n",
+       "      (act2): ReLU(inplace=True)\n",
+       "      (aa): Identity()\n",
+       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act3): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (3): Bottleneck(\n",
+       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act1): ReLU(inplace=True)\n",
+       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (drop_block): Identity()\n",
+       "      (act2): ReLU(inplace=True)\n",
+       "      (aa): Identity()\n",
+       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act3): ReLU(inplace=True)\n",
+       "    )\n",
+       "  )\n",
+       "  (layer3): Sequential(\n",
+       "    (0): Bottleneck(\n",
+       "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act1): ReLU(inplace=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (drop_block): Identity()\n",
+       "      (act2): ReLU(inplace=True)\n",
+       "      (aa): Identity()\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act3): ReLU(inplace=True)\n",
+       "      (downsample): Sequential(\n",
+       "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+       "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (1): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act1): ReLU(inplace=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (drop_block): Identity()\n",
+       "      (act2): ReLU(inplace=True)\n",
+       "      (aa): Identity()\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act3): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (2): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act1): ReLU(inplace=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (drop_block): Identity()\n",
+       "      (act2): ReLU(inplace=True)\n",
+       "      (aa): Identity()\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act3): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (3): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act1): ReLU(inplace=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (drop_block): Identity()\n",
+       "      (act2): ReLU(inplace=True)\n",
+       "      (aa): Identity()\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act3): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (4): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act1): ReLU(inplace=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (drop_block): Identity()\n",
+       "      (act2): ReLU(inplace=True)\n",
+       "      (aa): Identity()\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act3): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (5): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act1): ReLU(inplace=True)\n",
+       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (drop_block): Identity()\n",
+       "      (act2): ReLU(inplace=True)\n",
+       "      (aa): Identity()\n",
+       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act3): ReLU(inplace=True)\n",
+       "    )\n",
+       "  )\n",
+       "  (layer4): Sequential(\n",
+       "    (0): Bottleneck(\n",
+       "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act1): ReLU(inplace=True)\n",
+       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (drop_block): Identity()\n",
+       "      (act2): ReLU(inplace=True)\n",
+       "      (aa): Identity()\n",
+       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act3): ReLU(inplace=True)\n",
+       "      (downsample): Sequential(\n",
+       "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+       "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (1): Bottleneck(\n",
+       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act1): ReLU(inplace=True)\n",
+       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (drop_block): Identity()\n",
+       "      (act2): ReLU(inplace=True)\n",
+       "      (aa): Identity()\n",
+       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act3): ReLU(inplace=True)\n",
+       "    )\n",
+       "    (2): Bottleneck(\n",
+       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act1): ReLU(inplace=True)\n",
+       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (drop_block): Identity()\n",
+       "      (act2): ReLU(inplace=True)\n",
+       "      (aa): Identity()\n",
+       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "      (act3): ReLU(inplace=True)\n",
+       "    )\n",
+       "  )\n",
+       "  (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1))\n",
+       "  (fc): Linear(in_features=2048, out_features=8, bias=True)\n",
+       ")"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = timm.create_model(\n",
+    "    \"resnet50\",\n",
+    "    pretrained=True,\n",
+    "    num_classes=len(train_ds.labels)\n",
+    ")\n",
+    "\n",
+    "model.to(DEVICE)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "81a457ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "criterion = nn.CrossEntropyLoss()\n",
+    "\n",
+    "optimizer = torch.optim.AdamW(\n",
+    "    model.parameters(),\n",
+    "    lr=LR,\n",
+    "    weight_decay=1e-4\n",
+    ")\n",
+    "\n",
+    "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n",
+    "    optimizer,\n",
+    "    T_max=EPOCHS\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "951aa9e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train_epoch(model, loader):\n",
+    "    model.train()\n",
+    "    total_loss = 0\n",
+    "    correct = 0\n",
+    "    total = 0\n",
+    "\n",
+    "    for imgs, labels in tqdm(loader, leave=False):\n",
+    "        imgs = imgs.to(DEVICE)\n",
+    "        labels = labels.to(DEVICE)\n",
+    "\n",
+    "        optimizer.zero_grad()\n",
+    "        logits = model(imgs)\n",
+    "        loss = criterion(logits, labels)\n",
+    "\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        total_loss += loss.item() * imgs.size(0)\n",
+    "        preds = logits.argmax(dim=1)\n",
+    "        correct += (preds == labels).sum().item()\n",
+    "        total += labels.size(0)\n",
+    "\n",
+    "    return total_loss / total, correct / total\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fb7e9398",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@torch.no_grad()\n",
+    "def val_epoch(model, loader):\n",
+    "    model.eval()\n",
+    "    total_loss = 0\n",
+    "    correct = 0\n",
+    "    total = 0\n",
+    "\n",
+    "    for imgs, labels in loader:\n",
+    "        imgs = imgs.to(DEVICE)\n",
+    "        labels = labels.to(DEVICE)\n",
+    "\n",
+    "        logits = model(imgs)\n",
+    "        loss = criterion(logits, labels)\n",
+    "\n",
+    "        total_loss += loss.item() * imgs.size(0)\n",
+    "        preds = logits.argmax(dim=1)\n",
+    "        correct += (preds == labels).sum().item()\n",
+    "        total += labels.size(0)\n",
+    "\n",
+    "    return total_loss / total, correct / total\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9e870e5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/1477 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                    \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 01 | Train loss: 0.8383, acc: 0.6954 | Val loss: 0.6694, acc: 0.7563\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                   \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 02 | Train loss: 0.5462, acc: 0.7972 | Val loss: 0.6592, acc: 0.7594\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                   \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 03 | Train loss: 0.3654, acc: 0.8632 | Val loss: 0.7263, acc: 0.7600\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                   \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 04 | Train loss: 0.2111, acc: 0.9230 | Val loss: 0.8572, acc: 0.7472\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                    \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 05 | Train loss: 0.1187, acc: 0.9585 | Val loss: 1.0372, acc: 0.7453\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                    \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 06 | Train loss: 0.0690, acc: 0.9768 | Val loss: 1.1982, acc: 0.7529\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                    \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 07 | Train loss: 0.0466, acc: 0.9843 | Val loss: 1.3178, acc: 0.7492\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                   \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 08 | Train loss: 0.0295, acc: 0.9905 | Val loss: 1.3926, acc: 0.7551\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                   \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 09 | Train loss: 0.0204, acc: 0.9938 | Val loss: 1.4682, acc: 0.7497\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                   \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 10 | Train loss: 0.0146, acc: 0.9955 | Val loss: 1.4784, acc: 0.7604\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                   \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 11 | Train loss: 0.0087, acc: 0.9975 | Val loss: 1.5263, acc: 0.7580\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                   \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 12 | Train loss: 0.0057, acc: 0.9987 | Val loss: 1.5689, acc: 0.7558\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                    \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 13 | Train loss: 0.0044, acc: 0.9990 | Val loss: 1.5952, acc: 0.7566\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                    \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 14 | Train loss: 0.0030, acc: 0.9993 | Val loss: 1.6130, acc: 0.7600\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                   \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 15 | Train loss: 0.0025, acc: 0.9995 | Val loss: 1.5921, acc: 0.7627\n"
+     ]
+    }
+   ],
+   "source": [
+    "best_val_acc = 0.0\n",
+    "\n",
+    "for epoch in range(1, EPOCHS + 1):\n",
+    "    train_loss, train_acc = train_epoch(model, train_loader)\n",
+    "    val_loss, val_acc = val_epoch(model, val_loader)\n",
+    "\n",
+    "    scheduler.step()\n",
+    "\n",
+    "    print(\n",
+    "        f\"Epoch {epoch:02d} | \"\n",
+    "        f\"Train loss: {train_loss:.4f}, acc: {train_acc:.4f} | \"\n",
+    "        f\"Val loss: {val_loss:.4f}, acc: {val_acc:.4f}\"\n",
+    "    )\n",
+    "\n",
+    "    if val_acc > best_val_acc:\n",
+    "        best_val_acc = val_acc\n",
+    "        torch.save(model.state_dict(), \"emoset_resnet50_best.pth\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7796ef11",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "thesis-py3.11",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

src/scripts/train_regressor.py

@@ -0,0 +1,65 @@
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from sklearn.linear_model import RidgeCV
+from sklearn.multioutput import MultiOutputRegressor
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import Pipeline
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error, r2_score
+import joblib
+
+# 1. Алфавитный маппинг EmoSet
+EMO_VA_MAP = {
+    0: (7.5, 6.5), # amusement 
+    1: (2.0, 8.0), # anger 
+    2: (6.5, 5.0), # awe 
+    3: (7.0, 3.0), # contentment 
+    4: (3.0, 6.0), # disgust 
+    5: (8.0, 8.0), # excitement 
+    6: (2.5, 7.5), # fear 
+    7: (2.0, 2.0), # sadness 
+}
+
+BASE_DIR = Path(__file__).resolve().parent.parent
+EMBEDDINGS_PATH = BASE_DIR / "emoset_test_embeddings.npy"
+LABELS_PATH = BASE_DIR / "emoset_test_labels.npy"
+
+print("Загрузка данных...")
+X = np.load(EMBEDDINGS_PATH)
+y_labels = np.load(LABELS_PATH)
+
+y_va = np.array([EMO_VA_MAP[label] for label in y_labels])
+X_train, X_test, y_train, y_test = train_test_split(X, y_va, test_size=0.2, random_state=42)
+
+# 2. НОВАЯ, ПРАВИЛЬНАЯ АРХИТЕКТУРА (Pipeline)
+print("Обучение масштабатора и RidgeCV регрессора...")
+# Pipeline гарантирует, что при предсказании в main.py новые векторы тоже будут масштабированы
+model = Pipeline([
+    ('scaler', StandardScaler()),
+    ('regressor', MultiOutputRegressor(RidgeCV(alphas=[0.1, 1.0, 10.0, 100.0, 1000.0])))
+])
+
+model.fit(X_train, y_train)
+
+# 3. Диагностика и Оценка
+y_pred = model.predict(X_test)
+
+mse = mean_squared_error(y_test, y_pred)
+r2 = r2_score(y_test, y_pred)
+
+print(f"\n[УСПЕХ] Обучение завершено!")
+print(f"MSE: {mse:.4f}")
+print(f"R^2 Score: {r2:.4f}")
+
+# === ТОТ САМЫЙ ТЕСТ НА КОЛЛАПС ===
+print("\n--- ДИАГНОСТИКА РАЗБРОСА ПРЕДСКАЗАНИЙ ---")
+print(f"Valence: от {y_pred[:, 0].min():.2f} до {y_pred[:, 0].max():.2f} (Эталон: 2.0 - 8.0)")
+print(f"Arousal: от {y_pred[:, 1].min():.2f} до {y_pred[:, 1].max():.2f} (Эталон: 2.0 - 8.0)")
+# ===============================================
+
+# 4. Сохранение (Pipeline сохраняется целиком со StandardScaler)
+output_model_path = BASE_DIR / "music_engine" / "va_regressor.pkl"
+output_model_path.parent.mkdir(parents=True, exist_ok=True)
+joblib.dump(model, output_model_path)
+print(f"\nМодель сохранена в: {output_model_path}")

src/tabs/tab_dataset.py

@@ -42,7 +42,7 @@ def render_dataset_tab(matcher, image_files, embeddings, labels_array, images_pa
                         st.session_state.ds_current_options = random.sample(range(len(image_files)), 6)
                     st.rerun()
     else:
-        st.success("Анализ завершен! Ваш эмоциональный профиль готов.")
+        st.success("✅ Анализ завершен! Ваш эмоциональный профиль готов.")
         
         all_v, all_a = [], []
         for idx in st.session_state.ds_chosen_indices:
@@ -56,7 +56,7 @@ def render_dataset_tab(matcher, image_files, embeddings, labels_array, images_pa
         col_left, col_right = st.columns([1, 2])
 
         with col_left:
-            st.header("Ваш профиль")
+            st.header("📊 Ваш профиль")
             st.metric("Позитивность (Valence)", f"{target_v:.2f}")
             st.metric("Энергия (Arousal)", f"{target_a:.2f}")
             
@@ -74,8 +74,7 @@ def render_dataset_tab(matcher, image_files, embeddings, labels_array, images_pa
                     c1, c2 = st.columns([1, 3])
                     with c1:
                         st.write(f"**ID:** {int(row['song_id'])}")
-                        score_val = row.get('final_score', row.get('emo_distance', 0))
+                        st.caption(f"L2 Dist: {row['distance']:.2f}")
-                        st.caption(f"Dist Score: {score_val:.2f}")
                     with c2:
                         audio_path = matcher.get_audio_path(row['song_id'])
                         if audio_path:

src/tabs/tab_live.py

@@ -1,162 +1,75 @@
 import streamlit as st
-import streamlit.components.v1 as components
 import numpy as np
 from PIL import Image
-import base64
+import matplotlib.pyplot as plt
-from io import BytesIO
+from music_engine.llm_bridge import LLMAcousticBridge # ИМПОРТИРУЕМ МОСТ
-from music_engine.llm_bridge import LLMAcousticBridge
-
-# Вспомогательная функция для крохотного предпросмотра
-def get_thumbnail_html(images, max_display=12):
-    html_images = ""
-    for file in images[:max_display]:
-        img = Image.open(file)
-        img.thumbnail((100, 100)) # Сжимаем картинку
-        if img.mode != "RGB":
-            img = img.convert("RGB")
-        
-        buffered = BytesIO()
-        img.save(buffered, format="JPEG")
-        b64_str = base64.b64encode(buffered.getvalue()).decode()
-        
-        # Строгие стили для квадратных миниатюр
-        html_images += f'<img src="data:image/jpeg;base64,{b64_str}" style="width: 60px; height: 60px; object-fit: cover; border-radius: 8px; margin-right: 8px; margin-bottom: 8px; border: 1px solid rgba(255, 255, 255, 0.2);">'
-    
-    # Индикатор оставшихся фото, если их много
-    if len(images) > max_display:
-        html_images += f'<span style="display: inline-block; width: 60px; height: 60px; line-height: 60px; text-align: center; background: rgba(150, 150, 150, 0.2); border-radius: 8px; vertical-align: top; font-size: 14px;">+{len(images) - max_display}</span>'
-
-    return f'<div style="display: flex; flex-wrap: wrap;">{html_images}</div>'
 
 def render_live_tab(matcher, image_processor):
-    if "live_state" not in st.session_state:
-        st.session_state.live_state = "upload"
-    if "result_data" not in st.session_state:
-        st.session_state.result_data = None
-        
-    viewport = st.query_params.get("viewport", "desktop")
-
-    # ==========================================
-    # CSS ИНЪЕКЦИИ
-    # ==========================================
-    st.markdown("""
-    <style>
-    [data-testid="stFileUploadDropzone"] {
-        min-height: 250px !important;
-        display: flex;
-        align-items: center;
-        justify-content: center;
-        border-radius: 16px;
-        background-color: rgba(255, 75, 75, 0.03);
-    }
-    .spinner-container {
-        display: flex; flex-direction: column; align-items: center;
-        justify-content: center; min-height: 40vh; margin-top: 10vh;
-    }
-    .big-spinner {
-        width: 120px; height: 120px; border: 10px solid rgba(255, 75, 75, 0.1);
-        border-top: 10px solid #ff4b4b; border-radius: 50%;
-        animation: spin 1s linear infinite; margin-bottom: 2rem;
-    }
-    @keyframes spin { 0% { transform: rotate(0deg); } 100% { transform: rotate(360deg); } }
-    </style>
-    """, unsafe_allow_html=True)
-
-    # ==========================================
-    # ЭКРАН 1: ЗАГРУЗКА
-    # ==========================================
-    if st.session_state.live_state == "upload":
-        
-        upload_placeholder = st.empty()
-        with upload_placeholder.container():
     st.write("Загрузите фотографии с вашего устройства. Система проанализирует эмоции и семантику кадра.")
     
-            if viewport == "mobile":
-                st.markdown("<br>", unsafe_allow_html=True)
-                
     uploaded_files = st.file_uploader(
         "Перетащите изображения сюда", 
         type=['png', 'jpg', 'jpeg'], 
-                accept_multiple_files=True,
+        accept_multiple_files=True
-                label_visibility="collapsed" if viewport == "mobile" else "visible"
     )
     
     if uploaded_files:
-                # 1. КНОПКА СРАЗУ ПОСЛЕ ЗАГРУЗКИ (Не нужно скроллить вниз)
+        st.subheader("Анализ визуальных признаков:")
-                st.markdown("<br>", unsafe_allow_html=True)
-                if st.button("Сгенерировать саундтрек", type="primary", use_container_width=True):
-                    st.session_state.uploaded_images = uploaded_files
-                    st.session_state.live_state = "processing"
-                    upload_placeholder.empty()
-                    st.rerun()
-                
-                # 2. МИНИАТЮРЫ ПОД КНОПКОЙ
-                st.markdown("<br>", unsafe_allow_html=True)
-                st.caption("Выбранные кадры:")
-                # Генерируем компактный блок миниатюр
-                st.markdown(get_thumbnail_html(uploaded_files), unsafe_allow_html=True)
-
-    # ==========================================
-    # ЭКРАН 2: АНАЛИЗ (СПИННЕР)
-    # ==========================================
-    elif st.session_state.live_state == "processing":
-        
-        components.html("<script>window.parent.scrollTo(0, 0);</script>", height=0, width=0)
-        
-        files = st.session_state.get("uploaded_images", [])
-        st.markdown('<div class="spinner-container"><div class="big-spinner"></div></div>', unsafe_allow_html=True)
-        status_text = st.empty()
         
+        cols = st.columns(min(len(uploaded_files), 5))
         images = []
         all_objects = []
-        all_v, all_a = [], []
-        
-        for i, file in enumerate(files):
-            status_text.markdown(f"<h3 style='text-align: center; font-weight: 400;'>Анализ кадра {i + 1} из {len(files)}...</h3>", unsafe_allow_html=True)
         
+        for i, file in enumerate(uploaded_files):
             img = Image.open(file)
             images.append(img)
+            with cols[i % 5]:
+                st.image(img, use_container_width=True)
+                with st.spinner("VLM Анализ..."):
+                    caption = image_processor.describe_scene(img)
+                    st.caption(f"👁️ *{caption.capitalize()}*")
+                    all_objects.append(caption)
                 
+        if st.button("🎵 Сгенерировать саундтрек", type="primary", use_container_width=True):
+            
+            # 1. Извлекаем эмоции
+            all_v, all_a = [], []
+            for img in images:
                 embedding = image_processor.extract_embedding(img)
                 v, a = matcher.predict_va(embedding)
                 all_v.append(v)
                 all_a.append(a)
             
-            caption = image_processor.describe_scene(img)
-            all_objects.append(caption)
-            
             target_v, target_a = np.mean(all_v), np.mean(all_a)
             
-        status_text.markdown("<h3 style='text-align: center; font-weight: 400;'>Трансляция семантики в аудиопрофиль...</h3>", unsafe_allow_html=True)
+            # 2. Переводим Объекты -> Акустику через LLM
+            with st.spinner("Phi-3 генерирует акустический профиль..."):
                 llm = LLMAcousticBridge()
                 llm_profile = llm.get_acoustic_profile(target_v, target_a, list(set(all_objects)))
             
-        status_text.markdown("<h3 style='text-align: center; font-weight: 400;'>Поиск идеальных композиций...</h3>", unsafe_allow_html=True)
+            # 3. Ищем треки
-        playlist = matcher.find_nearest_tracks(target_v, target_a, llm_profile=llm_profile, top_k=15)
+            with st.spinner("Поиск треков в базе DEAM..."):
+                playlist = matcher.find_nearest_tracks(target_v, target_a, llm_profile=llm_profile, top_k=5)
                 
-        st.session_state.result_data = {
+            st.success("✅ Кросс-модальный анализ завершен!")
-            "target_v": target_v,
-            "target_a": target_a,
-            "llm_profile": llm_profile,
-            "playlist": playlist,
-            "semantics": list(set(all_objects))
-        }
-        st.session_state.live_state = "result"
-        st.rerun()
             
-    # ==========================================
+            # ВЫВОД РЕЗУЛЬТАТОВ
-    # ЭКРАН 3: РЕЗУЛЬТАТЫ
+            col_left, col_right = st.columns([1, 2])
-    # ==========================================
-    elif st.session_state.live_state == "result":
 
-        components.html("<script>window.parent.scrollTo(0, 0);</script>", height=0, width=0)
+            with col_left:
+                st.header("📊 Профиль")
+                st.metric("Valence (Настроение)", f"{target_v:.2f}")
+                st.metric("Arousal (Энергия)", f"{target_a:.2f}")
                 
-        data = st.session_state.result_data
+                if llm_profile:
-        st.header("Рекомендованный плейлист")
+                    st.write("**Требования LLM к звуку:**")
+                    for k, v in llm_profile.items():
+                        st.caption(f"- {k}: {v:.2f}")
 
-        for _, row in data["playlist"].iterrows():
+            with col_right:
+                st.header("🎵 Плейлист")
+                for _, row in playlist.iterrows():
                     with st.container(border=True):
-                if viewport == "desktop":
                         c1, c2 = st.columns([1, 3])
                         with c1:
                             st.write(f"**Track:** {int(row['song_id'])}")
@@ -167,42 +80,3 @@ def render_live_tab(matcher, image_processor):
                                 st.audio(str(audio_path))
                             else:
                                 st.warning("Файл не найден")
-                else:
-                    st.write(f"**Track:** {int(row['song_id'])} (Score: {row['final_score']:.2f})")
-                    audio_path = matcher.get_audio_path(row['song_id'])
-                    if audio_path:
-                        st.audio(str(audio_path))
-                    else:
-                        st.warning("Файл не найден")
-
-        st.markdown("<br>", unsafe_allow_html=True)
-        
-        with st.expander("Технические параметры анализа"):
-            c_v, c_a = st.columns(2)
-            c_v.metric("Valence (Настроение)", f"{data['target_v']:.2f}")
-            c_a.metric("Arousal (Энергия)", f"{data['target_a']:.2f}")
-            
-            st.markdown("---")
-            st.write("**Акустические таргеты (LLM):**")
-            if data["llm_profile"]:
-                cols_per_row = 2 if viewport == "mobile" else 3
-                llm_items = list(data["llm_profile"].items())
-                
-                for i in range(0, len(llm_items), cols_per_row):
-                    cols = st.columns(cols_per_row)
-                    for j in range(cols_per_row):
-                        if i + j < len(llm_items):
-                            k, v = llm_items[i + j]
-                            cols[j].metric(k, f"{v:.2f}")
-                            
-            st.markdown("---")
-            st.write("**Обнаруженная семантика:**")
-            st.write(", ".join([str(c).capitalize() for c in data["semantics"]]))
-
-        st.markdown("<br>", unsafe_allow_html=True)
-        
-        if st.button("Новый анализ", use_container_width=True):
-            st.session_state.live_state = "upload"
-            st.session_state.result_data = None
-            st.session_state.pop("uploaded_images", None)
-            st.rerun()

tree.txt

@@ -1,461 +0,0 @@
-.
-├── bin
-│   ├── activate
-│   ├── activate.csh
-│   ├── activate.fish
-│   ├── activate.nu
-│   ├── activate.ps1
-│   ├── activate_this.py
-│   ├── debugpy
-│   ├── debugpy-adapter
-│   ├── f2py
-│   ├── fonttools
-│   ├── httpx
-│   ├── ipython
-│   ├── ipython3
-│   ├── isympy
-│   ├── jlpm
-│   ├── jsonpointer
-│   ├── jsonschema
-│   ├── jupyter
-│   ├── jupyter-dejavu
-│   ├── jupyter-events
-│   ├── jupyter-execute
-│   ├── jupyter-kernel
-│   ├── jupyter-kernelspec
-│   ├── jupyter-lab
-│   ├── jupyter-labextension
-│   ├── jupyter-labhub
-│   ├── jupyter-migrate
-│   ├── jupyter-nbconvert
-│   ├── jupyter-run
-│   ├── jupyter-server
-│   ├── jupyter-troubleshoot
-│   ├── jupyter-trust
-│   ├── normalizer
-│   ├── numpy-config
-│   ├── pip
-│   ├── pip3
-│   ├── pip3.12
-│   ├── proton
-│   ├── proton-viewer
-│   ├── pybabel
-│   ├── pyftmerge
-│   ├── pyftsubset
-│   ├── pygmentize
-│   ├── pyjson5
-│   ├── python -> /usr/bin/python3
-│   ├── python3 -> python
-│   ├── python3.12 -> python
-│   ├── send2trash
-│   ├── streamlit
-│   ├── streamlit.cmd
-│   ├── torchfrtrace
-│   ├── torchrun
-│   ├── tqdm
-│   ├── ttx
-│   ├── watchmedo
-│   └── wsdump
-├── CACHEDIR.TAG
-├── docker
-│   ├── Dockerfile.api
-│   └── Dockerfile.ui
-├── docker-compose.yml
-├── Dockerfile
-├── .dockerignore
-├── .env
-├── etc
-│   └── jupyter
-│       ├── jupyter_notebook_config.d
-│       │   └── jupyterlab.json
-│       ├── jupyter_server_config.d
-│       │   ├── jupyterlab.json
-│       │   ├── jupyter-lsp-jupyter-server.json
-│       │   ├── jupyter_server_terminals.json
-│       │   └── notebook_shim.json
-│       └── nbconfig
-│           └── notebook.d
-├── .gitignore
-├── .idea
-│   ├── .gitignore
-│   ├── inspectionProfiles
-│   │   └── profiles_settings.xml
-│   ├── misc.xml
-│   ├── modules.xml
-│   ├── Thesis.iml
-│   ├── vcs.xml
-│   └── workspace.xml
-├── lib
-│   └── python3.12
-│       └── site-packages
-│           ├── altair
-│           ├── altair-6.0.0.dist-info
-│           ├── anyio
-│           ├── anyio-4.12.1.dist-info
-│           ├── argon2
-│           ├── argon2_cffi-25.1.0.dist-info
-│           ├── _argon2_cffi_bindings
-│           ├── argon2_cffi_bindings-25.1.0.dist-info
-│           ├── arrow
-│           ├── arrow-1.4.0.dist-info
-│           ├── asttokens
-│           ├── asttokens-3.0.1.dist-info
-│           ├── async_lru
-│           ├── async_lru-2.0.5.dist-info
-│           ├── attr
-│           ├── attrs
-│           ├── attrs-25.4.0.dist-info
-│           ├── babel
-│           ├── babel-2.17.0.dist-info
-│           ├── beautifulsoup4-4.14.3.dist-info
-│           ├── bleach
-│           ├── bleach-6.3.0.dist-info
-│           ├── blinker
-│           ├── blinker-1.9.0.dist-info
-│           ├── bs4
-│           ├── cachetools
-│           ├── cachetools-6.2.4.dist-info
-│           ├── certifi
-│           ├── certifi-2026.1.4.dist-info
-│           ├── cffi
-│           ├── cffi-2.0.0.dist-info
-│           ├── _cffi_backend.cpython-312-x86_64-linux-gnu.so
-│           ├── charset_normalizer
-│           ├── charset_normalizer-3.4.4.dist-info
-│           ├── click
-│           ├── click-8.3.1.dist-info
-│           ├── comm
-│           ├── comm-0.2.3.dist-info
-│           ├── contourpy
-│           ├── contourpy-1.3.3.dist-info
-│           ├── cycler
-│           ├── cycler-0.12.1.dist-info
-│           ├── dateutil
-│           ├── debugpy
-│           ├── debugpy-1.8.19.dist-info
-│           ├── decorator-5.2.1.dist-info
-│           ├── decorator.py
-│           ├── defusedxml
-│           ├── defusedxml-0.7.1.dist-info
-│           ├── _distutils_hack
-│           ├── distutils-precedence.pth
-│           ├── .DS_Store
-│           ├── executing
-│           ├── executing-2.2.1.dist-info
-│           ├── fastjsonschema
-│           ├── fastjsonschema-2.21.2.dist-info
-│           ├── filelock
-│           ├── filelock-3.20.3.dist-info
-│           ├── fontTools
-│           ├── fonttools-4.61.1.dist-info
-│           ├── fqdn
-│           ├── fqdn-1.5.1.dist-info
-│           ├── fsspec
-│           ├── fsspec-2026.1.0.dist-info
-│           ├── functorch
-│           ├── git
-│           ├── gitdb
-│           ├── gitdb-4.0.12.dist-info
-│           ├── gitpython-3.1.46.dist-info
-│           ├── google
-│           ├── h11
-│           ├── h11-0.16.0.dist-info
-│           ├── httpcore
-│           ├── httpcore-1.0.9.dist-info
-│           ├── httpx
-│           ├── httpx-0.28.1.dist-info
-│           ├── idna
-│           ├── idna-3.11.dist-info
-│           ├── ipykernel
-│           ├── ipykernel-7.1.0.dist-info
-│           ├── ipykernel_launcher.py
-│           ├── IPython
-│           ├── ipython-9.9.0.dist-info
-│           ├── ipython_pygments_lexers-1.1.1.dist-info
-│           ├── ipython_pygments_lexers.py
-│           ├── isoduration
-│           ├── isoduration-20.11.0.dist-info
-│           ├── isympy.py
-│           ├── jedi
-│           ├── jedi-0.19.2.dist-info
-│           ├── jinja2
-│           ├── jinja2-3.1.6.dist-info
-│           ├── joblib
-│           ├── joblib-1.5.3.dist-info
-│           ├── json5
-│           ├── json5-0.13.0.dist-info
-│           ├── jsonpointer-3.0.0.dist-info
-│           ├── jsonpointer.py
-│           ├── jsonschema
-│           ├── jsonschema-4.26.0.dist-info
-│           ├── jsonschema_specifications
-│           ├── jsonschema_specifications-2025.9.1.dist-info
-│           ├── jupyter_client
-│           ├── jupyter_client-8.8.0.dist-info
-│           ├── jupyter_core
-│           ├── jupyter_core-5.9.1.dist-info
-│           ├── jupyter_events
-│           ├── jupyter_events-0.12.0.dist-info
-│           ├── jupyterlab
-│           ├── jupyterlab-4.5.1.dist-info
-│           ├── jupyterlab_pygments
-│           ├── jupyterlab_pygments-0.3.0.dist-info
-│           ├── jupyterlab_server
-│           ├── jupyterlab_server-2.28.0.dist-info
-│           ├── jupyter_lsp
-│           ├── jupyter_lsp-2.3.0.dist-info
-│           ├── jupyter.py
-│           ├── jupyter_server
-│           ├── jupyter_server-2.17.0.dist-info
-│           ├── jupyter_server_terminals
-│           ├── jupyter_server_terminals-0.5.3.dist-info
-│           ├── kiwisolver
-│           ├── kiwisolver-1.4.9.dist-info
-│           ├── lark
-│           ├── lark-1.3.1.dist-info
-│           ├── markupsafe
-│           ├── markupsafe-3.0.3.dist-info
-│           ├── matplotlib
-│           ├── matplotlib-3.10.8.dist-info
-│           ├── matplotlib_inline
-│           ├── matplotlib_inline-0.2.1.dist-info
-│           ├── mistune
-│           ├── mistune-3.2.0.dist-info
-│           ├── mpl_toolkits
-│           ├── mpmath
-│           ├── mpmath-1.3.0.dist-info
-│           ├── narwhals
-│           ├── narwhals-2.15.0.dist-info
-│           ├── nbclient
-│           ├── nbclient-0.10.4.dist-info
-│           ├── nbconvert
-│           ├── nbconvert-7.16.6.dist-info
-│           ├── nbformat
-│           ├── nbformat-5.10.4.dist-info
-│           ├── nest_asyncio-1.6.0.dist-info
-│           ├── nest_asyncio.py
-│           ├── networkx
-│           ├── networkx-3.6.1.dist-info
-│           ├── notebook_shim
-│           ├── notebook_shim-0.2.4.dist-info
-│           ├── numpy
-│           ├── numpy-2.4.1.dist-info
-│           ├── numpy.libs
-│           ├── nvidia
-│           ├── nvidia_cublas_cu12-12.8.4.1.dist-info
-│           ├── nvidia_cuda_cupti_cu12-12.8.90.dist-info
-│           ├── nvidia_cuda_nvrtc_cu12-12.8.93.dist-info
-│           ├── nvidia_cuda_runtime_cu12-12.8.90.dist-info
-│           ├── nvidia_cudnn_cu12-9.10.2.21.dist-info
-│           ├── nvidia_cufft_cu12-11.3.3.83.dist-info
-│           ├── nvidia_cufile_cu12-1.13.1.3.dist-info
-│           ├── nvidia_curand_cu12-10.3.9.90.dist-info
-│           ├── nvidia_cusolver_cu12-11.7.3.90.dist-info
-│           ├── nvidia_cusparse_cu12-12.5.8.93.dist-info
-│           ├── nvidia_cusparselt_cu12-0.7.1.dist-info
-│           ├── nvidia_nccl_cu12-2.27.5.dist-info
-│           ├── nvidia_nvjitlink_cu12-12.8.93.dist-info
-│           ├── nvidia_nvshmem_cu12-3.3.20.dist-info
-│           ├── nvidia_nvtx_cu12-12.8.90.dist-info
-│           ├── packaging
-│           ├── packaging-25.0.dist-info
-│           ├── pandas
-│           ├── pandas-2.3.3.dist-info
-│           ├── pandocfilters-1.5.1.dist-info
-│           ├── pandocfilters.py
-│           ├── parso
-│           ├── parso-0.8.5.dist-info
-│           ├── pexpect
-│           ├── pexpect-4.9.0.dist-info
-│           ├── PIL
-│           ├── pillow-12.1.0.dist-info
-│           ├── pillow.libs
-│           ├── pip
-│           ├── pip-25.3.dist-info
-│           ├── pkg_resources
-│           ├── platformdirs
-│           ├── platformdirs-4.5.1.dist-info
-│           ├── prometheus_client
-│           ├── prometheus_client-0.23.1.dist-info
-│           ├── prompt_toolkit
-│           ├── prompt_toolkit-3.0.52.dist-info
-│           ├── protobuf-6.33.4.dist-info
-│           ├── psutil
-│           ├── psutil-7.2.1.dist-info
-│           ├── ptyprocess
-│           ├── ptyprocess-0.7.0.dist-info
-│           ├── pure_eval
-│           ├── pure_eval-0.2.3.dist-info
-│           ├── pyarrow
-│           ├── pyarrow-22.0.0.dist-info
-│           ├── pycparser
-│           ├── pycparser-2.23.dist-info
-│           ├── pydeck
-│           ├── pydeck-0.9.1.dist-info
-│           ├── pygments
-│           ├── pygments-2.19.2.dist-info
-│           ├── pylab.py
-│           ├── pyparsing
-│           ├── pyparsing-3.3.1.dist-info
-│           ├── python_dateutil-2.9.0.post0.dist-info
-│           ├── pythonjsonlogger
-│           ├── python_json_logger-4.0.0.dist-info
-│           ├── pytz
-│           ├── pytz-2025.2.dist-info
-│           ├── pyyaml-6.0.3.dist-info
-│           ├── pyzmq-27.1.0.dist-info
-│           ├── pyzmq.libs
-│           ├── referencing
-│           ├── referencing-0.37.0.dist-info
-│           ├── requests
-│           ├── requests-2.32.5.dist-info
-│           ├── rfc3339_validator-0.1.4.dist-info
-│           ├── rfc3339_validator.py
-│           ├── rfc3986_validator-0.1.1.dist-info
-│           ├── rfc3986_validator.py
-│           ├── rfc3987_syntax
-│           ├── rfc3987_syntax-1.1.0.dist-info
-│           ├── rpds
-│           ├── rpds_py-0.30.0.dist-info
-│           ├── scikit_learn-1.8.0.dist-info
-│           ├── scikit_learn.libs
-│           ├── scipy
-│           ├── scipy-1.17.0.dist-info
-│           ├── scipy.libs
-│           ├── send2trash
-│           ├── send2trash-2.0.0.dist-info
-│           ├── setuptools
-│           ├── setuptools-80.9.0.dist-info
-│           ├── six-1.17.0.dist-info
-│           ├── six.py
-│           ├── sklearn
-│           ├── smmap
-│           ├── smmap-5.0.2.dist-info
-│           ├── soupsieve
-│           ├── soupsieve-2.8.1.dist-info
-│           ├── stack_data
-│           ├── stack_data-0.6.3.dist-info
-│           ├── streamlit
-│           ├── streamlit-1.53.0.dist-info
-│           ├── sympy
-│           ├── sympy-1.14.0.dist-info
-│           ├── tenacity
-│           ├── tenacity-9.1.2.dist-info
-│           ├── terminado
-│           ├── terminado-0.18.1.dist-info
-│           ├── threadpoolctl-3.6.0.dist-info
-│           ├── threadpoolctl.py
-│           ├── tinycss2
-│           ├── tinycss2-1.4.0.dist-info
-│           ├── toml
-│           ├── toml-0.10.2.dist-info
-│           ├── torch
-│           ├── torch-2.9.1.dist-info
-│           ├── torchaudio
-│           ├── torchaudio-2.9.1.dist-info
-│           ├── torchgen
-│           ├── torchvision
-│           ├── torchvision-0.24.1.dist-info
-│           ├── torchvision.libs
-│           ├── tornado
-│           ├── tornado-6.5.4.dist-info
-│           ├── tqdm
-│           ├── tqdm-4.67.1.dist-info
-│           ├── traitlets
-│           ├── traitlets-5.14.3.dist-info
-│           ├── triton
-│           ├── triton-3.5.1.dist-info
-│           ├── typing_extensions-4.15.0.dist-info
-│           ├── typing_extensions.py
-│           ├── tzdata
-│           ├── tzdata-2025.3.dist-info
-│           ├── uri_template
-│           ├── uri_template-1.3.0.dist-info
-│           ├── urllib3
-│           ├── urllib3-2.6.3.dist-info
-│           ├── _virtualenv.pth
-│           ├── _virtualenv.py
-│           ├── watchdog
-│           ├── watchdog-6.0.0.dist-info
-│           ├── wcwidth
-│           ├── wcwidth-0.2.14.dist-info
-│           ├── webcolors
-│           ├── webcolors-25.10.0.dist-info
-│           ├── webencodings
-│           ├── webencodings-0.5.1.dist-info
-│           ├── websocket
-│           ├── websocket_client-1.9.0.dist-info
-│           ├── _yaml
-│           ├── yaml
-│           └── zmq
-├── Makefile
-├── NFS
-├── poetry.lock
-├── pyproject.toml
-├── pyvenv.cfg
-├── README.md
-├── requirements.txt
-├── runs
-├── share
-│   ├── applications
-│   │   └── jupyterlab.desktop
-│   ├── icons
-│   │   └── hicolor
-│   │       └── scalable
-│   ├── jupyter
-│   │   ├── kernels
-│   │   │   └── python3
-│   │   ├── lab
-│   │   │   ├── schemas
-│   │   │   ├── static
-│   │   │   └── themes
-│   │   ├── labextensions
-│   │   │   └── jupyterlab_pygments
-│   │   ├── nbconvert
-│   │   │   └── templates
-│   │   └── nbextensions
-│   │       └── pydeck
-│   └── man
-│       └── man1
-│           ├── ipython.1
-│           ├── isympy.1
-│           └── ttx.1
-├── src
-│   ├── 5_epoch_emoset_resnet50_finetuned_2.41M.pth
-│   ├── api.py
-│   ├── data_loader.py
-│   ├── dataset_paths_cache.pkl
-│   ├── emoset_resnet50_best.pth
-│   ├── emoset_resnet50_finetuned_2_41M_best.pth
-│   ├── emoset_resnet50_resume.pth
-│   ├── emoset_test_embeddings.npy
-│   ├── emoset_test_labels.npy
-│   ├── main.py
-│   ├── music_engine
-│   │   ├── image_processor.py
-│   │   ├── __init__.py
-│   │   ├── llm_bridge.py
-│   │   ├── matcher.py
-│   │   └── va_regressor.pkl
-│   ├── scripts
-│   │   ├── 00_setup_env.sh
-│   │   ├── 01_download_DEAM.py
-│   │   ├── 02_download_EmoSet.py
-│   │   ├── 11_prerp_DEAM.py
-│   │   ├── 20_bench_GPU.py
-│   │   ├── 21_train_images.ipynb
-│   │   ├── 22_extract_embeddings.ipynb
-│   │   ├── 23_aggregate_DEAM_timeline.py
-│   │   ├── 24_train_regressor.py
-│   │   ├── 31_finetune_2.41M.py
-│   │   ├── 90_acc_images_model.ipynb
-│   │   └── 91_generate_metrics.py
-│   └── tabs
-│       ├── tab_dataset.py
-│       └── tab_live.py
-├── tree.txt
-└── .vscode
-    ├── launch.json
-    └── tasks.json
-
-322 directories, 137 files