Merge pull request 'Docker integration into project' (#1) from Dockered into main

Reviewed-on: #1

.dockerignore

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

docker-compose.yml

@@ -0,0 +1,63 @@
+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

@@ -0,0 +1,24 @@
+FROM pytorch/pytorch:2.2.1-cuda12.1-cudnn8-runtime
+
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+
+# 1. Системные зависимости
+RUN apt-get update && apt-get install -y \
+    libglib2.0-0 libsm6 libxext6 libxrender-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# 2. Python пакеты
+RUN pip install --no-cache-dir fastapi uvicorn timm scikit-learn pandas joblib python-multipart transformers==4.38.2 tokenizers==0.15.2 accelerate
+
+# 3. Копируем код в контейнер
+WORKDIR /app
+COPY src/ /app/src/
+
+# 4. МАГИЯ ЗДЕСЬ: Переходим внутрь папки src
+WORKDIR /app/src
+
+EXPOSE 8000
+
+# 5. Запускаем локально (без префикса src.)
+CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "8000"]

docker/Dockerfile.ui

@@ -0,0 +1,17 @@
+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/
+
+# МАГИЯ ЗДЕСЬ: Переходим внутрь папки src
+WORKDIR /app/src
+
+EXPOSE 8080
+
+# Запускаем локально
+CMD ["streamlit", "run", "main.py", "--server.port", "8080", "--server.address", "0.0.0.0"]

doecker-compose.yml

@@ -1,64 +0,0 @@
-version: '3.8'
-
-# Определение общих сетей для изоляции трафика
-networks:
-  ai_mesh:
-    driver: bridge
-
-services:
-  # ----------------------------------------------------
-  # SERVICE 1: Frontend (Пользовательский интерфейс)
-  # Не требует GPU, может быть вынесен на отдельный сервер
-  # ----------------------------------------------------
-  web_ui:
-    build: 
-      context: .
-      dockerfile: Dockerfile
-    container_name: emom_frontend
-    restart: always
-    ports:
-      - "8080:8080"
-    networks:
-      - ai_mesh
-    environment:
-      - STREAMLIT_RUN=1
-      # Указываем UI, где искать LLM-бэкенд (внутри Docker-сети)
-      - OLLAMA_HOST=http://llm_backend:11434
-    volumes:
-      - ./src:/app/src
-      # Модели пока остаются здесь, так как код монолитный, 
-      # но архитектурно сервис уже изолирован
-      - /home/zin/projects/Thesis/src/emoset_resnet50_best.pth:/app/emoset_resnet50_best.pth:ro
-      - /home/zin/projects/Thesis/src/music_engine/va_regressor.pkl:/app/src/music_engine/va_regressor.pkl:ro
-      - /home/zin/projects/Thesis/dataset/DEAM:/app/dataset/DEAM:ro
-    # Временно оставляем GPU для PyTorch (пока он не вынесен в API)
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: 1
-              capabilities: [gpu]
-
-  # ----------------------------------------------------
-  # SERVICE 2: LLM Inference Backend (Ollama)
-  # Изолированный сервис для языковой модели на GPU
-  # ----------------------------------------------------
-  llm_backend:
-    image: ollama/ollama:latest
-    container_name: ollama_gpu_inference
-    restart: always
-    networks:
-      - ai_mesh
-    ports:
-      - "11434:11434"
-    volumes:
-      # Проброс локальных моделей Ollama, чтобы не качать их заново внутри докера
-      - ~/.ollama:/root/.ollama
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: 1
-              capabilities: [gpu]

src/api.py

@@ -0,0 +1,85 @@
+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 Inference 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("Инициализация нейросетевого ядра EmoM...")
+    ml_context["image_processor"] = load_image_processor()
+    ml_context["music_matcher"] = load_music_engine()
+    ml_context["llm_bridge"] = LLMAcousticBridge()
+    print("Вычислительный конвейер готов к работе.")
+
+@app.post("/analyze")
+async def analyze_event_endpoint(files: List[UploadFile] = File(...)):
+    try:
+        # 1. Читаем все загруженные картинки
+        images = []
+        for file in files:
+            image_bytes = await file.read()
+            img = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+            images.append(img)
+            
+        print(f"Начата обработка события из {len(images)} фотографий...")
+        
+        img_processor = ml_context["image_processor"]
+        matcher = ml_context["music_matcher"]
+        llm = ml_context["llm_bridge"]
+
+        all_v, all_a = [], []
+        all_objects = []
+
+        # 2. Прогоняем каждую картинку через нейросети
+        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)
+
+        # 3. Усредняем эмоции события
+        target_v = float(np.mean(all_v))
+        target_a = float(np.mean(all_a))
+        unique_semantics = list(set(all_objects))
+
+        # 4. Запрашиваем акустический профиль у Ollama
+        print(f"Запрос к Ollama. V={target_v:.2f}, A={target_a:.2f}")
+        llm_profile = llm.get_acoustic_profile(target_v, target_a, unique_semantics)
+
+        # 5. Ищем треки в базе
+        print("Поиск подходящих композиций...")
+        playlist_df = matcher.find_nearest_tracks(target_v, target_a, llm_profile=llm_profile, top_k=15)
+        
+        # Переводим таблицу в JSON-формат
+        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=f"Ошибка инференса: {str(e)}")

src/data_loader.py

@@ -1,55 +1,49 @@
-import os
 from pathlib import Path
 import pandas as pd
 import numpy as np
-import streamlit as st
 
+# Импорты твоих движков
 from music_engine.matcher import MusicMatcher
 from music_engine.image_processor import ImageProcessor
 
+# Базовая директория (папка src)
 BASE_DIR = Path(__file__).resolve().parent
 
-@st.cache_resource
 def load_music_engine():
-    # Инициализация базы данных и регрессора для музыкального мэтчинга
+    """Загрузка базы данных и модели регрессора для бэкенда."""
+    # Пути соответствуют тем, что мы примонтировали в Docker
     db_path = BASE_DIR.parent / "dataset" / "DEAM" / "music_db.csv"
     model_path = BASE_DIR / "music_engine" / "va_regressor.pkl"
     
-    if not db_path.exists():
-        print(f"Музыкальная БД не найдена: {db_path}")
-        return None
-        
     return MusicMatcher(db_path=db_path, model_path=model_path)
 
-@st.cache_resource
 def load_image_processor():
-    # Модуль обработки визуальных признаков
+    """Инициализация нейросетевого экстрактора (ResNet-50)."""
-    model_path = BASE_DIR / "emoset_resnet50_best.pth"
+    weights_path = BASE_DIR / "emoset_resnet50_best.pth"
-    
+    return ImageProcessor(weights_path)
-    # Обработка пути при вызове из корневой директории
-    if not model_path.exists():
-        model_path = BASE_DIR.parent / "emoset_resnet50_best.pth"
-        
-    return ImageProcessor(model_path=model_path)
 
-@st.cache_data
 def load_emoset_data():
-    # Выборка данных датасета для вкладки отладки
+    """
-    dataset_root = BASE_DIR.parent / "dataset" / "EmoSet-118K" / "test"
+    Загрузка эталонного датасета EmoSet. 
-    
+    (Оставлено для обратной совместимости, если понадобится локальная отладка)
-    csv_path = dataset_root / "labels.csv"
+    """
-    img_dir = dataset_root / "images"
+    try:
-    emb_path = BASE_DIR / "emoset_test_embeddings.npy"
+        images_path = BASE_DIR.parent / "dataset" / "EmoSet-118K" / "test" / "images"
-    lbl_path = BASE_DIR / "emoset_test_labels.npy"
+        labels_path = BASE_DIR / "emoset_test_labels.npy"
-
+        embeddings_path = BASE_DIR / "emoset_test_embeddings.npy"
-    if not all([csv_path.exists(), emb_path.exists(), lbl_path.exists()]):
+        
-        print("Тестовые файлы датасета не найдены, вкладка отладки может работать некорректно")
+        # Если файлов нет (например, на проде), возвращаем None
-        return None, None, None, None
+        if not all(p.exists() for p in [labels_path, embeddings_path]):
-
+            return None, None, None, None
-    labels_df = pd.read_csv(csv_path)
+            
-    
+        labels = np.load(labels_path)
-    test_filenames = labels_df['filename'].tolist()
+        embeddings = np.load(embeddings_path)
-    test_embeddings = np.load(emb_path)
+        
-    test_labels = np.load(lbl_path)
+        # Читаем CSV с метками
-    
+        df = pd.read_csv(BASE_DIR.parent / "dataset" / "EmoSet-118K" / "test" / "labels.csv")
-    return test_filenames, test_embeddings, test_labels, img_dir
+        image_files = df['filename'].tolist()
+        
+        return image_files, embeddings, labels, images_path
+    except Exception as e:
+        print(f"Предупреждение: Тестовые артефакты EmoSet не найдены ({e})")
+        return None, None, None, None

src/main.py

@@ -1,73 +1,190 @@
-import sys
 import os
-import subprocess
+import requests
-
 import streamlit as st
 import streamlit.components.v1 as components
+from PIL import Image
+import base64
+from io import BytesIO
 
-from data_loader import load_music_engine, load_emoset_data, load_image_processor
+st.set_page_config(page_title="EmoM Playlist Generator", layout="wide", initial_sidebar_state="collapsed")
-from tabs.tab_dataset import render_dataset_tab
-from tabs.tab_live import render_live_tab
 
-# Костыль для прямого запуска
+API_URL = os.getenv("BACKEND_API_URL", "http://emom_inference:8000") + "/analyze"
-if __name__ == "__main__":
+DEAM_AUDIO_DIR = "/app/dataset/DEAM/DEAM_audio/MEMD_audio"
-    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()
 
-viewport_mode = st.query_params.get("viewport", "desktop")
+def get_thumbnail_html(images, max_display=12):
-page_layout = "centered" if viewport_mode == "mobile" else "wide"
+    html_images = ""
-
+    for file in images[:max_display]:
-st.set_page_config(page_title="Thesis Demo", layout=page_layout)
+        img = Image.open(file)
-
+        img.thumbnail((100, 100))
-# Определения ширины экрана и смены верстки
+        if img.mode != "RGB": 
-components.html(
+            img = img.convert("RGB")
-    """
+        buffered = BytesIO()
-    <script>
+        img.save(buffered, format="JPEG")
-    const w = window.parent.innerWidth;
+        b64_str = base64.b64encode(buffered.getvalue()).decode()
-    const h = window.parent.innerHeight;
+        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);">'
-    const url = new URL(window.parent.location.href);
     
-    // Считаем мобилкой, если ушли в портретный режим или экран уже 768px
+    if len(images) > max_display:
-    const isMobile = (h > w) || (w < 768);
+        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>'
-    const target = isMobile ? "mobile" : "desktop";
+    return f'<div style="display: flex; flex-wrap: wrap;">{html_images}</div>'
-    
-    if (url.searchParams.get("viewport") !== target) {
-        url.searchParams.set("viewport", target);
-        window.parent.location.href = url.href;
-    }
-    </script>
-    """,
-    height=0,
-    width=0,
-)
 
-st.markdown(
+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>
-    img { max-width: 100%; height: auto; object-fit: contain; }
+    [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); }
-    [data-testid="stMetricValue"] { font-size: 1.8rem; }
+    .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)
-    unsafe_allow_html=True
-)
 
-# Подгрузка ML-моделей и датасета
+    if st.session_state.live_state == "upload":
-music_matcher = load_music_engine()
+        upload_placeholder = st.empty()
-img_processor = load_image_processor()
+        with upload_placeholder.container():
-emoset_files, emoset_embeddings, emoset_labels, emoset_path = load_emoset_data()
+            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)
 
-st.title("Генератор саундтреков (Research Demo)")
+    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")
 
-tab_live, tab_debug = st.tabs(["Анализ событий (Свои фото)", "Отладка (Датасет EmoSet)"])
+        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 для комиссии (передаются в аргумент help)
+            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, "")
+                            # Форматируем до 2 знаков после запятой (например, 0.64)
+                            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", [])]))
 
-with tab_live:
+        st.markdown("<br>", unsafe_allow_html=True)
-    if img_processor:
+        
-        render_live_tab(music_matcher, img_processor)
+        if st.button("Новый запрос", use_container_width=True):
-    else:
+            st.session_state.live_state = "upload"
-        st.error("Ошибка загрузки: не найдены веса ResNet для image_processor.")
+            st.session_state.result_data = None
+            st.session_state.pop("uploaded_images", None)
+            st.rerun()
 
-with tab_debug:
+if __name__ == "__main__":
-    render_dataset_tab(music_matcher, emoset_files, emoset_embeddings, emoset_labels, emoset_path)
+    main()

src/music_engine/image_processor.py

@@ -32,7 +32,11 @@ class ImageProcessor:
 
         # Модуль семантического описания сцены
         print("Инициализация BLIP-2...")
-        self.blip_processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
+        # Обход бага конфигурации Hugging Face (ручная сборка процессора)
+        from transformers import BlipImageProcessor, AutoTokenizer
+        img_proc = BlipImageProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
+        tok = AutoTokenizer.from_pretrained("Salesforce/blip2-opt-2.7b", use_fast=False)
+        self.blip_processor = Blip2Processor(image_processor=img_proc, tokenizer=tok)
         self.blip_model = Blip2ForConditionalGeneration.from_pretrained(
             "Salesforce/blip2-opt-2.7b", 
             torch_dtype=torch.float16 

src/music_engine/llm_bridge.py

@@ -1,65 +1,67 @@
-import re
+import os
 import json
+import re
 import requests
 
 class LLMAcousticBridge:
-    def __init__(self, target_model="dolphin-llama3:8b"):
+    def __init__(self, model_name="dolphin-llama3:8b"):
-        self.api_url = "http://localhost:11434/api/generate"
+        self.model_name = model_name
-        self.model = target_model
+        # Динамический выбор URL (внутри Docker используется emom_ollama)
+        base_url = os.getenv("OLLAMA_API_URL", "http://emom_ollama:11434")
+        self.api_url = f"{base_url}/api/generate"
 
-    def _extract_json(self, raw_text: str):
+    def get_acoustic_profile(self, valence, arousal, semantics):
-        # Проверка на ИИдиота, LLM иногда игнорирует format="json" и оборачивает ответ в маркдаун
+        context_str = ", ".join(semantics) if semantics else "abstract scene"
-        try:
+        
-            match = re.search(r'\{.*\}', raw_text, re.DOTALL)
+        # Строгий промпт с примером вывода
-            if match:
+        prompt = f"""
-                return json.loads(match.group(0))
+        Analyze the visual context and emotions to determine the ideal background music properties. 
-            return json.loads(raw_text)
+        Emotions: Valence {valence:.1f}/9.0 (Positivity), Arousal {arousal:.1f}/9.0 (Energy). 
-        except json.JSONDecodeError:
+        Visual Context: {context_str}. 
-            # Если ИИдиот
+        Map this scene to exactly 6 acoustic features. Values MUST be floats between 0.0 and 1.0.
-            return None
+        
-
+        1. "energy": (Loudness/Density)
-    def get_acoustic_profile(self, v_score: float, a_score: float, scene_context: list) -> dict | None:
+        2. "flux": (Rhythmic sharpness/Beat)
-        # Агрегация контекста для обработки серии снимков (события)
+        3. "centroid": (Brightness)
-        context_merged = " | ".join(scene_context) if scene_context else "abstract scene"
+        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}}
+        """
         
-        system_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 {v_score:.1f}/9.0 (Positivity), Arousal {a_score:.1f}/9.0 (Energy).
-Visual Context: {context_merged}.
-
-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)
-
-Return ONLY a valid JSON object. Do not add any text or explanation.
-Example: {{"energy": 0.5, "flux": 0.2, "centroid": 0.4, "pitch": 0.3, "hnr": 0.8, "zcr": 0.1}}"""
-
         try:
-            # Отправка промпта локальной Ollama
+            payload = {
-            response = requests.post(self.api_url, json={
+                "model": self.model_name,
-                "model": self.model,
+                "prompt": prompt,
-                "prompt": system_prompt,
                 "stream": False,
-                "format": "json"
+                "format": "json"  # Принудительный JSON-режим Ollama
-            }, timeout=45)
+            }
-            response.raise_for_status()
             
-            raw_response = response.json().get("response", "")
+            print(f"Запрос акустического профиля к Ollama...")
-            profile_data = self._extract_json(raw_response)
+            response = requests.post(self.api_url, json=payload, timeout=120)
             
-            # Валидация структуры ответа
+            if response.status_code == 200:
-            expected_features = {'energy', 'flux', 'centroid', 'pitch', 'hnr', 'zcr'}
+                data = response.json()
-            
+                response_text = data.get("response", "")
-            if profile_data and expected_features.issubset(profile_data.keys()):
-                return profile_data
                 
-            print("LLM вернула неполный или некорректный набор акустических признаков")
+                try:
-            return None
+                    # 1. Попытка прямой десериализации
-            
+                    profile = json.loads(response_text)
-        except requests.exceptions.RequestException as req_err:
+                    return profile
-            print(f"Не удалось подключиться к Ollama: {req_err}")
+                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)}")
+            return {}

tree.txt

@@ -0,0 +1,461 @@
+.
+├── 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