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| from flask import Flask, request, jsonify | |
| from flask_cors import CORS | |
| from transformers import pipeline | |
| import os | |
| import json | |
| import re | |
| import logging | |
| import google.generativeai as genai | |
| from tempfile import NamedTemporaryFile | |
| # ===== Fix HF Spaces cache write error ===== | |
| os.environ["TRANSFORMERS_CACHE"] = "./cache" | |
| os.environ["HF_HOME"] = "./hf_cache" | |
| # === Setup === | |
| app = Flask(__name__) | |
| CORS(app, resources={r"/predict": {"origins": "*"}}, supports_credentials=True) | |
| # Logging config | |
| logging.basicConfig(level=logging.INFO, format='[%(asctime)s] %(levelname)s - %(message)s') | |
| # === Load model === | |
| logging.info("Loading HuggingFace audio emotion recognition model...") | |
| pipe = pipeline("audio-classification", model="superb/wav2vec2-base-superb-er") | |
| logging.info("Model loaded successfully.") | |
| # === Gemini setup === | |
| logging.info("Setting up Gemini API...") | |
| genai.configure(api_key=os.getenv("GEMINI_API_KEY")) # Load from environment variable | |
| model = genai.GenerativeModel("gemini-1.5-pro") | |
| logging.info("Gemini API configured.") | |
| # === Gemini recommendation logic === | |
| def get_gemini_recommendations(emotion): | |
| logging.info(f"Generating recommendations for emotion: {emotion}") | |
| prompt = f""" | |
| Given the user's emotion: "{emotion}", suggest a JSON object with personalized: | |
| - 3 books | |
| - 3 songs/music | |
| - 3 movies | |
| Each item must include: | |
| - The title | |
| - A link (Spotify for music, Goodreads for books, IMDb for movies) | |
| Respond strictly in this JSON format: | |
| {{ | |
| "books": [ | |
| {{"title": "book1", "link": "https://goodreads.com/..."}} ... | |
| ], | |
| "music": [ | |
| ... | |
| ], | |
| "movies": [ | |
| ... | |
| ] | |
| }} | |
| Return only the JSON response and nothing else. | |
| """ | |
| try: | |
| response = model.generate_content(prompt) | |
| content = response.text.strip() | |
| match = re.search(r'\{.*\}', content, re.DOTALL) | |
| if match: | |
| return json.loads(match.group(0)) | |
| else: | |
| return {"books": [], "music": [], "movies": []} | |
| except Exception as e: | |
| logging.error(f"Gemini API error: {e}") | |
| return {"books": [], "music": [], "movies": []} | |
| # === Flask API route === | |
| def predict(): | |
| logging.info("Received REST prediction request.") | |
| try: | |
| if 'file' not in request.files: | |
| return jsonify({'error': 'No file part'}), 400 | |
| file = request.files['file'] | |
| if file.filename == '': | |
| return jsonify({'error': 'No selected file'}), 400 | |
| # Save file temporarily | |
| with NamedTemporaryFile(delete=False, suffix=".wav") as temp: | |
| file.save(temp.name) | |
| file_path = temp.name | |
| # Emotion prediction | |
| results = pipe(file_path) | |
| emotion = results[0]['label'].lower() | |
| # Get Gemini recommendations | |
| recommendations = get_gemini_recommendations(emotion) | |
| # Clean up temp file | |
| os.remove(file_path) | |
| return jsonify({ | |
| "emotion": emotion, | |
| "recommendations": recommendations | |
| }) | |
| except Exception as e: | |
| logging.error(f"Prediction error: {e}") | |
| return jsonify({'error': str(e)}), 500 | |
| # === Run Flask App === | |
| if __name__ == '__main__': | |
| logging.info("Starting EmoTune Flask API...") | |
| app.run(host="0.0.0.0", port=7860) | |
| from flask import Flask, request, jsonify | |
| from flask_cors import CORS | |
| from transformers import pipeline | |
| import os | |
| import json | |
| import re | |
| import logging | |
| import google.generativeai as genai | |
| from tempfile import NamedTemporaryFile | |
| # ===== Fix HF Spaces cache write error ===== | |
| os.environ["TRANSFORMERS_CACHE"] = "./cache" | |
| os.environ["HF_HOME"] = "./hf_cache" | |
| # === Setup === | |
| app = Flask(__name__) | |
| CORS(app, resources={r"/predict": {"origins": "*"}}, supports_credentials=True) | |
| # Logging config | |
| logging.basicConfig(level=logging.INFO, format='[%(asctime)s] %(levelname)s - %(message)s') | |
| # === Load model === | |
| logging.info("Loading HuggingFace audio emotion recognition model...") | |
| pipe = pipeline("audio-classification", model="superb/wav2vec2-base-superb-er") | |
| logging.info("Model loaded successfully.") | |
| # === Gemini setup === | |
| logging.info("Setting up Gemini API...") | |
| genai.configure(api_key=os.getenv("GEMINI_API_KEY")) # Load from environment variable | |
| model = genai.GenerativeModel("gemini-1.5-pro") | |
| logging.info("Gemini API configured.") | |
| # === Gemini recommendation logic === | |
| def get_gemini_recommendations(emotion): | |
| logging.info(f"Generating recommendations for emotion: {emotion}") | |
| prompt = f""" | |
| Given the user's emotion: "{emotion}", suggest a JSON object with personalized: | |
| - 3 books | |
| - 3 songs/music | |
| - 3 movies | |
| Each item must include: | |
| - The title | |
| - A link (Spotify for music, Goodreads for books, IMDb for movies) | |
| Respond strictly in this JSON format: | |
| {{ | |
| "books": [ | |
| {{"title": "book1", "link": "https://goodreads.com/..."}} ... | |
| ], | |
| "music": [ | |
| ... | |
| ], | |
| "movies": [ | |
| ... | |
| ] | |
| }} | |
| Return only the JSON response and nothing else. | |
| """ | |
| try: | |
| response = model.generate_content(prompt) | |
| content = response.text.strip() | |
| match = re.search(r'\{.*\}', content, re.DOTALL) | |
| if match: | |
| return json.loads(match.group(0)) | |
| else: | |
| return {"books": [], "music": [], "movies": []} | |
| except Exception as e: | |
| logging.error(f"Gemini API error: {e}") | |
| return {"books": [], "music": [], "movies": []} | |
| # === Flask API route === | |
| def predict(): | |
| logging.info("Received REST prediction request.") | |
| try: | |
| if 'file' not in request.files: | |
| return jsonify({'error': 'No file part'}), 400 | |
| file = request.files['file'] | |
| if file.filename == '': | |
| return jsonify({'error': 'No selected file'}), 400 | |
| # Save file temporarily | |
| with NamedTemporaryFile(delete=False, suffix=".wav") as temp: | |
| file.save(temp.name) | |
| file_path = temp.name | |
| # Emotion prediction | |
| results = pipe(file_path) | |
| emotion = results[0]['label'].lower() | |
| # Get Gemini recommendations | |
| recommendations = get_gemini_recommendations(emotion) | |
| # Clean up temp file | |
| os.remove(file_path) | |
| return jsonify({ | |
| "emotion": emotion, | |
| "recommendations": recommendations | |
| }) | |
| except Exception as e: | |
| logging.error(f"Prediction error: {e}") | |
| return jsonify({'error': str(e)}), 500 | |
| # === Run Flask App === | |
| if __name__ == '__main__': | |
| logging.info("Starting EmoTune Flask API...") | |
| app.run(host="0.0.0.0", port=7860) | |
| from flask import Flask, request, jsonify | |
| from flask_cors import CORS | |
| from transformers import pipeline | |
| import os | |
| import json | |
| import re | |
| import logging | |
| import google.generativeai as genai | |
| from tempfile import NamedTemporaryFile | |
| # ===== Fix HF Spaces cache write error ===== | |
| os.environ["TRANSFORMERS_CACHE"] = "./cache" | |
| os.environ["HF_HOME"] = "./hf_cache" | |
| # === Setup === | |
| app = Flask(__name__) | |
| CORS(app, resources={r"/predict": {"origins": "*"}}, supports_credentials=True) | |
| # Logging config | |
| logging.basicConfig(level=logging.INFO, format='[%(asctime)s] %(levelname)s - %(message)s') | |
| # === Load model === | |
| logging.info("Loading HuggingFace audio emotion recognition model...") | |
| pipe = pipeline("audio-classification", model="superb/wav2vec2-base-superb-er") | |
| logging.info("Model loaded successfully.") | |
| # === Gemini setup === | |
| logging.info("Setting up Gemini API...") | |
| genai.configure(api_key=os.getenv("GEMINI_API_KEY")) # Load from environment variable | |
| model = genai.GenerativeModel("gemini-1.5-pro") | |
| logging.info("Gemini API configured.") | |
| # === Gemini recommendation logic === | |
| def get_gemini_recommendations(emotion): | |
| logging.info(f"Generating recommendations for emotion: {emotion}") | |
| prompt = f""" | |
| Given the user's emotion: "{emotion}", suggest a JSON object with personalized: | |
| - 3 books | |
| - 3 songs/music | |
| - 3 movies | |
| Each item must include: | |
| - The title | |
| - A link (Spotify for music, Goodreads for books, IMDb for movies) | |
| Respond strictly in this JSON format: | |
| {{ | |
| "books": [ | |
| {{"title": "book1", "link": "https://goodreads.com/..."}} ... | |
| ], | |
| "music": [ | |
| ... | |
| ], | |
| "movies": [ | |
| ... | |
| ] | |
| }} | |
| Return only the JSON response and nothing else. | |
| """ | |
| try: | |
| response = model.generate_content(prompt) | |
| content = response.text.strip() | |
| match = re.search(r'\{.*\}', content, re.DOTALL) | |
| if match: | |
| return json.loads(match.group(0)) | |
| else: | |
| return {"books": [], "music": [], "movies": []} | |
| except Exception as e: | |
| logging.error(f"Gemini API error: {e}") | |
| return {"books": [], "music": [], "movies": []} | |
| # === Flask API route === | |
| def predict(): | |
| logging.info("Received REST prediction request.") | |
| try: | |
| if 'file' not in request.files: | |
| return jsonify({'error': 'No file part'}), 400 | |
| file = request.files['file'] | |
| if file.filename == '': | |
| return jsonify({'error': 'No selected file'}), 400 | |
| # Save file temporarily | |
| with NamedTemporaryFile(delete=False, suffix=".wav") as temp: | |
| file.save(temp.name) | |
| file_path = temp.name | |
| # Emotion prediction | |
| results = pipe(file_path) | |
| emotion = results[0]['label'].lower() | |
| # Get Gemini recommendations | |
| recommendations = get_gemini_recommendations(emotion) | |
| # Clean up temp file | |
| os.remove(file_path) | |
| return jsonify({ | |
| "emotion": emotion, | |
| "recommendations": recommendations | |
| }) | |
| except Exception as e: | |
| logging.error(f"Prediction error: {e}") | |
| return jsonify({'error': str(e)}), 500 | |
| # === Run Flask App === | |
| if __name__ == '__main__': | |
| logging.info("Starting EmoTune Flask API...") | |
| app.run(host="0.0.0.0", port=7860) | |
| from flask import Flask, request, jsonify | |
| from flask_cors import CORS | |
| from transformers import pipeline | |
| import os | |
| import json | |
| import re | |
| import logging | |
| import google.generativeai as genai | |
| from tempfile import NamedTemporaryFile | |
| # ===== Fix HF Spaces cache write error ===== | |
| os.environ["TRANSFORMERS_CACHE"] = "./cache" | |
| os.environ["HF_HOME"] = "./hf_cache" | |
| # === Setup === | |
| app = Flask(__name__) | |
| CORS(app, resources={r"/predict": {"origins": "*"}}, supports_credentials=True) | |
| # Logging config | |
| logging.basicConfig(level=logging.INFO, format='[%(asctime)s] %(levelname)s - %(message)s') | |
| # === Load model === | |
| logging.info("Loading HuggingFace audio emotion recognition model...") | |
| pipe = pipeline("audio-classification", model="superb/wav2vec2-base-superb-er") | |
| logging.info("Model loaded successfully.") | |
| # === Gemini setup === | |
| logging.info("Setting up Gemini API...") | |
| genai.configure(api_key=os.getenv("GEMINI_API_KEY")) # Load from environment variable | |
| model = genai.GenerativeModel("gemini-1.5-pro") | |
| logging.info("Gemini API configured.") | |
| # === Gemini recommendation logic === | |
| def get_gemini_recommendations(emotion): | |
| logging.info(f"Generating recommendations for emotion: {emotion}") | |
| prompt = f""" | |
| Given the user's emotion: "{emotion}", suggest a JSON object with personalized: | |
| - 3 books | |
| - 3 songs/music | |
| - 3 movies | |
| Each item must include: | |
| - The title | |
| - A link (Spotify for music, Goodreads for books, IMDb for movies) | |
| Respond strictly in this JSON format: | |
| {{ | |
| "books": [ | |
| {{"title": "book1", "link": "https://goodreads.com/..."}} ... | |
| ], | |
| "music": [ | |
| ... | |
| ], | |
| "movies": [ | |
| ... | |
| ] | |
| }} | |
| Return only the JSON response and nothing else. | |
| """ | |
| try: | |
| response = model.generate_content(prompt) | |
| content = response.text.strip() | |
| match = re.search(r'\{.*\}', content, re.DOTALL) | |
| if match: | |
| return json.loads(match.group(0)) | |
| else: | |
| return {"books": [], "music": [], "movies": []} | |
| except Exception as e: | |
| logging.error(f"Gemini API error: {e}") | |
| return {"books": [], "music": [], "movies": []} | |
| # === Flask API route === | |
| def predict(): | |
| logging.info("Received REST prediction request.") | |
| try: | |
| if 'file' not in request.files: | |
| return jsonify({'error': 'No file part'}), 400 | |
| file = request.files['file'] | |
| if file.filename == '': | |
| return jsonify({'error': 'No selected file'}), 400 | |
| # Save file temporarily | |
| with NamedTemporaryFile(delete=False, suffix=".wav") as temp: | |
| file.save(temp.name) | |
| file_path = temp.name | |
| # Emotion prediction | |
| results = pipe(file_path) | |
| emotion = results[0]['label'].lower() | |
| # Get Gemini recommendations | |
| recommendations = get_gemini_recommendations(emotion) | |
| # Clean up temp file | |
| os.remove(file_path) | |
| return jsonify({ | |
| "emotion": emotion, | |
| "recommendations": recommendations | |
| }) | |
| except Exception as e: | |
| logging.error(f"Prediction error: {e}") | |
| return jsonify({'error': str(e)}), 500 | |
| # === Run Flask App === | |
| if __name__ == '__main__': | |
| logging.info("Starting EmoTune Flask API...") | |
| app.run(host="0.0.0.0", port=7860) | |
| from flask import Flask, request, jsonify | |
| from flask_cors import CORS | |
| from transformers import pipeline | |
| import os | |
| import json | |
| import re | |
| import logging | |
| import google.generativeai as genai | |
| from tempfile import NamedTemporaryFile | |
| # ===== Fix HF Spaces cache write error ===== | |
| os.environ["TRANSFORMERS_CACHE"] = "./cache" | |
| os.environ["HF_HOME"] = "./hf_cache" | |
| # === Setup === | |
| app = Flask(__name__) | |
| CORS(app, resources={r"/predict": {"origins": "*"}}, supports_credentials=True) | |
| # Logging config | |
| logging.basicConfig(level=logging.INFO, format='[%(asctime)s] %(levelname)s - %(message)s') | |
| # === Load model === | |
| logging.info("Loading HuggingFace audio emotion recognition model...") | |
| pipe = pipeline("audio-classification", model="superb/wav2vec2-base-superb-er") | |
| logging.info("Model loaded successfully.") | |
| # === Gemini setup === | |
| logging.info("Setting up Gemini API...") | |
| genai.configure(api_key=os.getenv("GEMINI_API_KEY")) # Load from environment variable | |
| model = genai.GenerativeModel("gemini-1.5-pro") | |
| logging.info("Gemini API configured.") | |
| # === Gemini recommendation logic === | |
| def get_gemini_recommendations(emotion): | |
| logging.info(f"Generating recommendations for emotion: {emotion}") | |
| prompt = f""" | |
| Given the user's emotion: "{emotion}", suggest a JSON object with personalized: | |
| - 3 books | |
| - 3 songs/music | |
| - 3 movies | |
| Each item must include: | |
| - The title | |
| - A link (Spotify for music, Goodreads for books, IMDb for movies) | |
| Respond strictly in this JSON format: | |
| {{ | |
| "books": [ | |
| {{"title": "book1", "link": "https://goodreads.com/..."}} ... | |
| ], | |
| "music": [ | |
| ... | |
| ], | |
| "movies": [ | |
| ... | |
| ] | |
| }} | |
| Return only the JSON response and nothing else. | |
| """ | |
| try: | |
| response = model.generate_content(prompt) | |
| content = response.text.strip() | |
| match = re.search(r'\{.*\}', content, re.DOTALL) | |
| if match: | |
| return json.loads(match.group(0)) | |
| else: | |
| return {"books": [], "music": [], "movies": []} | |
| except Exception as e: | |
| logging.error(f"Gemini API error: {e}") | |
| return {"books": [], "music": [], "movies": []} | |
| # === Flask API route === | |
| def predict(): | |
| logging.info("Received REST prediction request.") | |
| try: | |
| if 'file' not in request.files: | |
| return jsonify({'error': 'No file part'}), 400 | |
| file = request.files['file'] | |
| if file.filename == '': | |
| return jsonify({'error': 'No selected file'}), 400 | |
| # Save file temporarily | |
| with NamedTemporaryFile(delete=False, suffix=".wav") as temp: | |
| file.save(temp.name) | |
| file_path = temp.name | |
| # Emotion prediction | |
| results = pipe(file_path) | |
| emotion = results[0]['label'].lower() | |
| # Get Gemini recommendations | |
| recommendations = get_gemini_recommendations(emotion) | |
| # Clean up temp file | |
| os.remove(file_path) | |
| return jsonify({ | |
| "emotion": emotion, | |
| "recommendations": recommendations | |
| }) | |
| except Exception as e: | |
| logging.error(f"Prediction error: {e}") | |
| return jsonify({'error': str(e)}), 500 | |
| # === Run Flask App === | |
| if __name__ == '__main__': | |
| logging.info("Starting EmoTune Flask API...") | |
| app.run(host="0.0.0.0", port=7860) | |
| from flask import Flask, request, jsonify | |
| from flask_cors import CORS | |
| from transformers import pipeline | |
| import os | |
| import json | |
| import re | |
| import logging | |
| import google.generativeai as genai | |
| from tempfile import NamedTemporaryFile | |
| # ===== Fix HF Spaces cache write error ===== | |
| os.environ["TRANSFORMERS_CACHE"] = "./cache" | |
| os.environ["HF_HOME"] = "./hf_cache" | |
| # === Setup === | |
| app = Flask(__name__) | |
| CORS(app, resources={r"/predict": {"origins": "*"}}, supports_credentials=True) | |
| # Logging config | |
| logging.basicConfig(level=logging.INFO, format='[%(asctime)s] %(levelname)s - %(message)s') | |
| # === Load model === | |
| logging.info("Loading HuggingFace audio emotion recognition model...") | |
| pipe = pipeline("audio-classification", model="superb/wav2vec2-base-superb-er") | |
| logging.info("Model loaded successfully.") | |
| # === Gemini setup === | |
| logging.info("Setting up Gemini API...") | |
| genai.configure(api_key=os.getenv("GEMINI_API_KEY")) # Load from environment variable | |
| model = genai.GenerativeModel("gemini-1.5-pro") | |
| logging.info("Gemini API configured.") | |
| # === Gemini recommendation logic === | |
| def get_gemini_recommendations(emotion): | |
| logging.info(f"Generating recommendations for emotion: {emotion}") | |
| prompt = f""" | |
| Given the user's emotion: "{emotion}", suggest a JSON object with personalized: | |
| - 3 books | |
| - 3 songs/music | |
| - 3 movies | |
| Each item must include: | |
| - The title | |
| - A link (Spotify for music, Goodreads for books, IMDb for movies) | |
| Respond strictly in this JSON format: | |
| {{ | |
| "books": [ | |
| {{"title": "book1", "link": "https://goodreads.com/..."}} ... | |
| ], | |
| "music": [ | |
| ... | |
| ], | |
| "movies": [ | |
| ... | |
| ] | |
| }} | |
| Return only the JSON response and nothing else. | |
| """ | |
| try: | |
| response = model.generate_content(prompt) | |
| content = response.text.strip() | |
| match = re.search(r'\{.*\}', content, re.DOTALL) | |
| if match: | |
| return json.loads(match.group(0)) | |
| else: | |
| return {"books": [], "music": [], "movies": []} | |
| except Exception as e: | |
| logging.error(f"Gemini API error: {e}") | |
| return {"books": [], "music": [], "movies": []} | |
| # === Flask API route === | |
| def predict(): | |
| logging.info("Received REST prediction request.") | |
| try: | |
| if 'file' not in request.files: | |
| return jsonify({'error': 'No file part'}), 400 | |
| file = request.files['file'] | |
| if file.filename == '': | |
| return jsonify({'error': 'No selected file'}), 400 | |
| # Save file temporarily | |
| with NamedTemporaryFile(delete=False, suffix=".wav") as temp: | |
| file.save(temp.name) | |
| file_path = temp.name | |
| # Emotion prediction | |
| results = pipe(file_path) | |
| emotion = results[0]['label'].lower() | |
| # Get Gemini recommendations | |
| recommendations = get_gemini_recommendations(emotion) | |
| # Clean up temp file | |
| os.remove(file_path) | |
| return jsonify({ | |
| "emotion": emotion, | |
| "recommendations": recommendations | |
| }) | |
| except Exception as e: | |
| logging.error(f"Prediction error: {e}") | |
| return jsonify({'error': str(e)}), 500 | |
| # === Run Flask App === | |
| if __name__ == '__main__': | |
| logging.info("Starting EmoTune Flask API...") | |
| app.run(host="0.0.0.0", port=7860) | |
| from flask import Flask, request, jsonify | |
| from flask_cors import CORS | |
| from transformers import pipeline | |
| import os | |
| import json | |
| import re | |
| import logging | |
| import google.generativeai as genai | |
| from tempfile import NamedTemporaryFile | |
| # ===== Fix HF Spaces cache write error ===== | |
| os.environ["TRANSFORMERS_CACHE"] = "./cache" | |
| os.environ["HF_HOME"] = "./hf_cache" | |
| # === Setup === | |
| app = Flask(__name__) | |
| CORS(app, resources={r"/predict": {"origins": "*"}}, supports_credentials=True) | |
| # Logging config | |
| logging.basicConfig(level=logging.INFO, format='[%(asctime)s] %(levelname)s - %(message)s') | |
| # === Load model === | |
| logging.info("Loading HuggingFace audio emotion recognition model...") | |
| pipe = pipeline("audio-classification", model="superb/wav2vec2-base-superb-er") | |
| logging.info("Model loaded successfully.") | |
| # === Gemini setup === | |
| logging.info("Setting up Gemini API...") | |
| genai.configure(api_key=os.getenv("GEMINI_API_KEY")) # Load from environment variable | |
| model = genai.GenerativeModel("gemini-1.5-pro") | |
| logging.info("Gemini API configured.") | |
| # === Gemini recommendation logic === | |
| def get_gemini_recommendations(emotion): | |
| logging.info(f"Generating recommendations for emotion: {emotion}") | |
| prompt = f""" | |
| Given the user's emotion: "{emotion}", suggest a JSON object with personalized: | |
| - 3 books | |
| - 3 songs/music | |
| - 3 movies | |
| Each item must include: | |
| - The title | |
| - A link (Spotify for music, Goodreads for books, IMDb for movies) | |
| Respond strictly in this JSON format: | |
| {{ | |
| "books": [ | |
| {{"title": "book1", "link": "https://goodreads.com/..."}} ... | |
| ], | |
| "music": [ | |
| ... | |
| ], | |
| "movies": [ | |
| ... | |
| ] | |
| }} | |
| Return only the JSON response and nothing else. | |
| """ | |
| try: | |
| response = model.generate_content(prompt) | |
| content = response.text.strip() | |
| match = re.search(r'\{.*\}', content, re.DOTALL) | |
| if match: | |
| return json.loads(match.group(0)) | |
| else: | |
| return {"books": [], "music": [], "movies": []} | |
| except Exception as e: | |
| logging.error(f"Gemini API error: {e}") | |
| return {"books": [], "music": [], "movies": []} | |
| # === Flask API route === | |
| def predict(): | |
| logging.info("Received REST prediction request.") | |
| try: | |
| if 'file' not in request.files: | |
| return jsonify({'error': 'No file part'}), 400 | |
| file = request.files['file'] | |
| if file.filename == '': | |
| return jsonify({'error': 'No selected file'}), 400 | |
| # Save file temporarily | |
| with NamedTemporaryFile(delete=False, suffix=".wav") as temp: | |
| file.save(temp.name) | |
| file_path = temp.name | |
| # Emotion prediction | |
| results = pipe(file_path) | |
| emotion = results[0]['label'].lower() | |
| # Get Gemini recommendations | |
| recommendations = get_gemini_recommendations(emotion) | |
| # Clean up temp file | |
| os.remove(file_path) | |
| return jsonify({ | |
| "emotion": emotion, | |
| "recommendations": recommendations | |
| }) | |
| except Exception as e: | |
| logging.error(f"Prediction error: {e}") | |
| return jsonify({'error': str(e)}), 500 | |
| # === Run Flask App === | |
| if __name__ == '__main__': | |
| logging.info("Starting EmoTune Flask API...") | |
| app.run(host="0.0.0.0", port=7860) | |
| from flask import Flask, request, jsonify | |
| from flask_cors import CORS | |
| from transformers import pipeline | |
| import os | |
| import json | |
| import re | |
| import logging | |
| import google.generativeai as genai | |
| from tempfile import NamedTemporaryFile | |
| # ===== Fix HF Spaces cache write error ===== | |
| os.environ["TRANSFORMERS_CACHE"] = "./cache" | |
| os.environ["HF_HOME"] = "./hf_cache" | |
| # === Setup === | |
| app = Flask(__name__) | |
| CORS(app, resources={r"/predict": {"origins": "*"}}, supports_credentials=True) | |
| # Logging config | |
| logging.basicConfig(level=logging.INFO, format='[%(asctime)s] %(levelname)s - %(message)s') | |
| # === Load model === | |
| logging.info("Loading HuggingFace audio emotion recognition model...") | |
| pipe = pipeline("audio-classification", model="superb/wav2vec2-base-superb-er") | |
| logging.info("Model loaded successfully.") | |
| # === Gemini setup === | |
| logging.info("Setting up Gemini API...") | |
| genai.configure(api_key=os.getenv("GEMINI_API_KEY")) # Load from environment variable | |
| model = genai.GenerativeModel("gemini-1.5-pro") | |
| logging.info("Gemini API configured.") | |
| # === Gemini recommendation logic === | |
| def get_gemini_recommendations(emotion): | |
| logging.info(f"Generating recommendations for emotion: {emotion}") | |
| prompt = f""" | |
| Given the user's emotion: "{emotion}", suggest a JSON object with personalized: | |
| - 3 books | |
| - 3 songs/music | |
| - 3 movies | |
| Each item must include: | |
| - The title | |
| - A link (Spotify for music, Goodreads for books, IMDb for movies) | |
| Respond strictly in this JSON format: | |
| {{ | |
| "books": [ | |
| {{"title": "book1", "link": "https://goodreads.com/..."}} ... | |
| ], | |
| "music": [ | |
| ... | |
| ], | |
| "movies": [ | |
| ... | |
| ] | |
| }} | |
| Return only the JSON response and nothing else. | |
| """ | |
| try: | |
| response = model.generate_content(prompt) | |
| content = response.text.strip() | |
| match = re.search(r'\{.*\}', content, re.DOTALL) | |
| if match: | |
| return json.loads(match.group(0)) | |
| else: | |
| return {"books": [], "music": [], "movies": []} | |
| except Exception as e: | |
| logging.error(f"Gemini API error: {e}") | |
| return {"books": [], "music": [], "movies": []} | |
| # === Flask API route === | |
| def predict(): | |
| logging.info("Received REST prediction request.") | |
| try: | |
| if 'file' not in request.files: | |
| return jsonify({'error': 'No file part'}), 400 | |
| file = request.files['file'] | |
| if file.filename == '': | |
| return jsonify({'error': 'No selected file'}), 400 | |
| # Save file temporarily | |
| with NamedTemporaryFile(delete=False, suffix=".wav") as temp: | |
| file.save(temp.name) | |
| file_path = temp.name | |
| # Emotion prediction | |
| results = pipe(file_path) | |
| emotion = results[0]['label'].lower() | |
| # Get Gemini recommendations | |
| recommendations = get_gemini_recommendations(emotion) | |
| # Clean up temp file | |
| os.remove(file_path) | |
| return jsonify({ | |
| "emotion": emotion, | |
| "recommendations": recommendations | |
| }) | |
| except Exception as e: | |
| logging.error(f"Prediction error: {e}") | |
| return jsonify({'error': str(e)}), 500 | |
| # === Run Flask App === | |
| if __name__ == '__main__': | |
| logging.info("Starting EmoTune Flask API...") | |
| app.run(host="0.0.0.0", port=7860) | |