from flask import Flask, request, jsonify from flask_cors import CORS from deepface import DeepFace import base64 import io from PIL import Image import numpy as np import logging import traceback import os import spotipy from spotipy.oauth2 import SpotifyClientCredentials # Suppress TensorFlow warnings os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0' # Configure logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s' ) logger = logging.getLogger(__name__) app = Flask(__name__) CORS(app) # Initialize Spotify try: # Set your Spotify credentials here or use environment variables SPOTIFY_CLIENT_ID = os.getenv('SPOTIFY_CLIENT_ID', 'your_client_id_here') SPOTIFY_CLIENT_SECRET = os.getenv('SPOTIFY_CLIENT_SECRET', 'your_client_secret_here') client_credentials_manager = SpotifyClientCredentials( client_id=SPOTIFY_CLIENT_ID, client_secret=SPOTIFY_CLIENT_SECRET ) spotify = spotipy.Spotify(client_credentials_manager=client_credentials_manager) logger.info("Spotify API initialized successfully") except Exception as e: logger.error(f"Failed to initialize Spotify API: {str(e)}") spotify = None # Emotion to Spotify search queries EMOTION_TO_MOOD = { 'happy': ['happy hits', 'feel good', 'party music', 'upbeat pop'], 'sad': ['sad songs', 'emotional ballads', 'melancholic', 'rainy day'], 'angry': ['workout motivation', 'rock anthems', 'intense metal', 'aggressive'], 'neutral': ['chill vibes', 'focus music', 'ambient', 'lo-fi beats'], 'surprise': ['party hits', 'dance pop', 'exciting', 'upbeat'], 'fear': ['calming music', 'meditation', 'peaceful', 'relaxation'], 'disgust': ['energizing', 'workout', 'rock music', 'alternative'] } @app.route('/', methods=['GET']) def home(): return jsonify({ "message": "Emotion Detection + Music Recommendation API", "endpoints": { "/emotion": "POST - Detect emotion from image", "/health": "GET - Health check" }, "music_provider": "Spotify" }) @app.route('/health', methods=['GET']) def health(): return jsonify({ "status": "healthy", "spotify_available": spotify is not None }), 200 @app.route('/emotion', methods=['POST', 'OPTIONS']) def detect_emotion(): if request.method == 'OPTIONS': return jsonify({}), 200 try: logger.info("Received emotion detection request") data = request.get_json() if not data or 'image' not in data: logger.error("No image data in request") return jsonify({ "success": False, "error": "No image data provided" }), 400 image_data = data['image'] logger.info(f"Received image data, length: {len(image_data)}") if ',' in image_data: image_data = image_data.split(',')[1] try: image_bytes = base64.b64decode(image_data) logger.info(f"Decoded image bytes: {len(image_bytes)} bytes") except Exception as e: logger.error(f"Base64 decode error: {str(e)}") return jsonify({ "success": False, "error": "Invalid base64 image data" }), 400 try: image = Image.open(io.BytesIO(image_bytes)) logger.info(f"Image opened - Format: {image.format}, Size: {image.size}, Mode: {image.mode}") except Exception as e: logger.error(f"PIL image open error: {str(e)}") return jsonify({ "success": False, "error": "Invalid image format" }), 400 if image.mode != 'RGB': logger.info(f"Converting image from {image.mode} to RGB") image = image.convert('RGB') img_array = np.array(image) logger.info(f"Numpy array shape: {img_array.shape}, dtype: {img_array.dtype}") try: logger.info("Starting DeepFace analysis...") result = DeepFace.analyze( img_array, actions=['emotion'], enforce_detection=False, silent=True, detector_backend='opencv' ) logger.info("DeepFace analysis completed successfully") except Exception as e: logger.error(f"DeepFace analysis error: {str(e)}") logger.error(traceback.format_exc()) return jsonify({ "success": False, "error": f"Emotion detection failed: {str(e)}" }), 500 if isinstance(result, list): result = result[0] dominant_emotion = result['dominant_emotion'] emotion_scores = result['emotion'] emotion_scores_serializable = { emotion: float(score) for emotion, score in emotion_scores.items() } logger.info(f"Dominant emotion: {dominant_emotion}") music_recommendations = [] if spotify: try: music_recommendations = get_music_from_spotify(dominant_emotion) logger.info(f"Found {len(music_recommendations)} music recommendations") except Exception as e: logger.warning(f"Failed to get music recommendations: {str(e)}") return jsonify({ "success": True, "dominant_emotion": dominant_emotion, "all_emotions": emotion_scores_serializable, "confidence": float(emotion_scores[dominant_emotion]), "music_recommendations": music_recommendations, "suggested_moods": EMOTION_TO_MOOD.get(dominant_emotion, []) }) except Exception as e: logger.error(f"Unexpected error: {str(e)}") logger.error(traceback.format_exc()) return jsonify({ "success": False, "error": f"Internal server error: {str(e)}" }), 500 def get_music_from_spotify(emotion): """Get music playlists from Spotify based on emotion""" try: mood_keywords = EMOTION_TO_MOOD.get(emotion, ['chill']) recommendations = [] for keyword in mood_keywords[:3]: try: results = spotify.search(q=keyword, type='playlist', limit=3) for playlist in results['playlists']['items']: if playlist: # Get playlist image thumbnail = '' if playlist.get('images') and len(playlist['images']) > 0: thumbnail = playlist['images'][0]['url'] recommendations.append({ 'title': playlist.get('name', ''), 'playlist_id': playlist.get('id', ''), 'playlist_url': playlist.get('external_urls', {}).get('spotify', ''), 'thumbnail': thumbnail, 'author': playlist.get('owner', {}).get('display_name', 'Spotify'), 'item_count': playlist.get('tracks', {}).get('total', 0), 'mood': keyword, 'provider': 'spotify' }) if len(recommendations) >= 6: break except Exception as e: logger.warning(f"Search failed for keyword '{keyword}': {str(e)}") continue return recommendations[:6] except Exception as e: logger.error(f"Error getting music recommendations: {str(e)}") return [] def start(): logger.info("Starting Emotion Detection + Music Recommendation API on port 7860...") app.run(host='0.0.0.0', port=7860) if __name__ == '__main__': start()