diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..02e2e99 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,18 @@ +bin/ +lib/ +share/ +etc/ +include/ +pyvenv.cfg +.idea/ +.vscode/ +__pycache__/ +*.pyc +.git/ +runs/ +dataset/ +NFS/ +*.pth +*.pkl +*.npy +.env \ No newline at end of file diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..e73e388 --- /dev/null +++ b/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] \ No newline at end of file diff --git a/docker/Dockerfile.api b/docker/Dockerfile.api new file mode 100644 index 0000000..f93d3f2 --- /dev/null +++ b/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"] \ No newline at end of file diff --git a/docker/Dockerfile.ui b/docker/Dockerfile.ui new file mode 100644 index 0000000..1bbfc38 --- /dev/null +++ b/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"] \ No newline at end of file diff --git a/doecker-compose.yml b/doecker-compose.yml deleted file mode 100644 index 2365e05..0000000 --- a/doecker-compose.yml +++ /dev/null @@ -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] \ No newline at end of file diff --git a/src/api.py b/src/api.py new file mode 100644 index 0000000..2dd0b04 --- /dev/null +++ b/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)}") \ No newline at end of file diff --git a/src/data_loader.py b/src/data_loader.py index caa7ae5..64dfd9f 100644 --- a/src/data_loader.py +++ b/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 - model_path = BASE_DIR / "emoset_resnet50_best.pth" - - # 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) + """Инициализация нейросетевого экстрактора (ResNet-50).""" + weights_path = BASE_DIR / "emoset_resnet50_best.pth" + return ImageProcessor(weights_path) -@st.cache_data def load_emoset_data(): - # Выборка данных датасета для вкладки отладки - dataset_root = BASE_DIR.parent / "dataset" / "EmoSet-118K" / "test" - - csv_path = dataset_root / "labels.csv" - img_dir = dataset_root / "images" - emb_path = BASE_DIR / "emoset_test_embeddings.npy" - lbl_path = BASE_DIR / "emoset_test_labels.npy" - - if not all([csv_path.exists(), emb_path.exists(), lbl_path.exists()]): - print("Тестовые файлы датасета не найдены, вкладка отладки может работать некорректно") - return None, None, None, None - - labels_df = pd.read_csv(csv_path) - - test_filenames = labels_df['filename'].tolist() - test_embeddings = np.load(emb_path) - test_labels = np.load(lbl_path) - - return test_filenames, test_embeddings, test_labels, img_dir \ No newline at end of file + """ + Загрузка эталонного датасета EmoSet. + (Оставлено для обратной совместимости, если понадобится локальная отладка) + """ + 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" + + # Если файлов нет (например, на проде), возвращаем None + 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) + + # Читаем CSV с метками + df = pd.read_csv(BASE_DIR.parent / "dataset" / "EmoSet-118K" / "test" / "labels.csv") + 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 \ No newline at end of file diff --git a/src/main.py b/src/main.py index b0da338..f771a47 100644 --- a/src/main.py +++ b/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 -from tabs.tab_dataset import render_dataset_tab -from tabs.tab_live import render_live_tab +st.set_page_config(page_title="EmoM Playlist Generator", layout="wide", initial_sidebar_state="collapsed") -# Костыль для прямого запуска -if __name__ == "__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() +API_URL = os.getenv("BACKEND_API_URL", "http://emom_inference:8000") + "/analyze" +DEAM_AUDIO_DIR = "/app/dataset/DEAM/DEAM_audio/MEMD_audio" -viewport_mode = st.query_params.get("viewport", "desktop") -page_layout = "centered" if viewport_mode == "mobile" else "wide" - -st.set_page_config(page_title="Thesis Demo", layout=page_layout) - -# Определения ширины экрана и смены верстки -components.html( - """ - - """, - height=0, - width=0, -) + if len(images) > max_display: + html_images += f'+{len(images) - max_display}' + return f'
{html_images}
' -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(""" - """, - unsafe_allow_html=True -) + """, unsafe_allow_html=True) -# Подгрузка ML-моделей и датасета -music_matcher = load_music_engine() -img_processor = load_image_processor() -emoset_files, emoset_embeddings, emoset_labels, emoset_path = load_emoset_data() + 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("
", 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("
", 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("
", 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("", height=0, width=0) + files = st.session_state.get("uploaded_images", []) + st.markdown('', 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("", 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("
", 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: - if img_processor: - render_live_tab(music_matcher, img_processor) - else: - st.error("Ошибка загрузки: не найдены веса ResNet для image_processor.") + st.markdown("
", 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() -with tab_debug: - render_dataset_tab(music_matcher, emoset_files, emoset_embeddings, emoset_labels, emoset_path) \ No newline at end of file +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/music_engine/image_processor.py b/src/music_engine/image_processor.py index fb654c2..01caacd 100644 --- a/src/music_engine/image_processor.py +++ b/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 diff --git a/src/music_engine/llm_bridge.py b/src/music_engine/llm_bridge.py index 9e698ad..ef1cd60 100644 --- a/src/music_engine/llm_bridge.py +++ b/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"): - self.api_url = "http://localhost:11434/api/generate" - self.model = target_model + def __init__(self, model_name="dolphin-llama3:8b"): + self.model_name = model_name + # 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): - # Проверка на ИИдиота, LLM иногда игнорирует format="json" и оборачивает ответ в маркдаун - try: - match = re.search(r'\{.*\}', raw_text, re.DOTALL) - if match: - return json.loads(match.group(0)) - return json.loads(raw_text) - except json.JSONDecodeError: - # Если ИИдиот - return None - - def get_acoustic_profile(self, v_score: float, a_score: float, scene_context: list) -> dict | None: - # Агрегация контекста для обработки серии снимков (события) - context_merged = " | ".join(scene_context) if scene_context else "abstract scene" + def get_acoustic_profile(self, valence, arousal, semantics): + context_str = ", ".join(semantics) if semantics else "abstract scene" + + # Строгий промпт с примером вывода + prompt = f""" + 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) + 2. "flux": (Rhythmic sharpness/Beat) + 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}} + """ - 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 - response = requests.post(self.api_url, json={ - "model": self.model, - "prompt": system_prompt, + payload = { + "model": self.model_name, + "prompt": prompt, "stream": False, - "format": "json" - }, timeout=45) - response.raise_for_status() + "format": "json" # Принудительный JSON-режим Ollama + } - raw_response = response.json().get("response", "") - profile_data = self._extract_json(raw_response) + print(f"Запрос акустического профиля к Ollama...") + response = requests.post(self.api_url, json=payload, timeout=120) - # Валидация структуры ответа - expected_features = {'energy', 'flux', 'centroid', 'pitch', 'hnr', 'zcr'} - - if profile_data and expected_features.issubset(profile_data.keys()): - return profile_data + if response.status_code == 200: + data = response.json() + response_text = data.get("response", "") - print("LLM вернула неполный или некорректный набор акустических признаков") - return None - - except requests.exceptions.RequestException as req_err: - print(f"Не удалось подключиться к Ollama: {req_err}") - return None \ No newline at end of file + try: + # 1. Попытка прямой десериализации + profile = json.loads(response_text) + return profile + except json.JSONDecodeError: + # 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 {} \ No newline at end of file diff --git a/tree.txt b/tree.txt new file mode 100644 index 0000000..a54bd94 --- /dev/null +++ b/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