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| import streamlit as st | |
| import cv2 | |
| from deepface import DeepFace | |
| import numpy as np | |
| from retinaface import RetinaFace | |
| from PIL import Image | |
| import random | |
| import time | |
| import json | |
| import os | |
| from functools import lru_cache | |
| import pandas as pd | |
| import plotly.express as px | |
| import threading | |
| import concurrent.futures | |
| import tensorflow as tf | |
| try: | |
| import torch | |
| TORCH_AVAILABLE = True | |
| except ImportError: | |
| TORCH_AVAILABLE = False | |
| def check_gpu_availability(): | |
| gpu_available = False | |
| gpu_info = "" | |
| tf_gpu = len(tf.config.list_physical_devices('GPU')) > 0 | |
| torch_gpu = False | |
| if TORCH_AVAILABLE: | |
| torch_gpu = torch.cuda.is_available() | |
| gpu_available = tf_gpu or torch_gpu | |
| if gpu_available: | |
| if tf_gpu: | |
| gpu_info += f"TensorFlow GPU: {tf.config.list_physical_devices('GPU')}\n" | |
| if torch_gpu: | |
| gpu_info += f"PyTorch GPU: {torch.cuda.get_device_name(0)}\n" | |
| return gpu_available, gpu_info | |
| def configure_gpu(): | |
| try: | |
| gpus = tf.config.experimental.list_physical_devices('GPU') | |
| if gpus: | |
| for gpu in gpus: | |
| tf.config.experimental.set_memory_growth(gpu, True) | |
| st.sidebar.success("GPU configured for optimal performance") | |
| except Exception as e: | |
| st.sidebar.warning(f"Could not configure GPU: {e}") | |
| class NumpyJSONEncoder(json.JSONEncoder): | |
| def default(self, obj): | |
| if isinstance(obj, np.integer): | |
| return int(obj) | |
| elif isinstance(obj, np.floating): | |
| return float(obj) | |
| elif isinstance(obj, np.ndarray): | |
| return obj.tolist() | |
| return super(NumpyJSONEncoder, self).default(obj) | |
| def save_user_preferences(prefs): | |
| try: | |
| with open(USER_PREF_PATH, "w") as f: | |
| json.dump(prefs, f, cls=NumpyJSONEncoder) | |
| except Exception as e: | |
| st.error(f"Failed to save preferences: {e}") | |
| GPU_AVAILABLE, GPU_INFO = check_gpu_availability() | |
| if GPU_AVAILABLE: | |
| configure_gpu() | |
| if 'model_cache' not in st.session_state: | |
| st.session_state['model_cache'] = {} | |
| def load_face_model(model_name, use_gpu=False): | |
| if model_name in st.session_state['model_cache']: | |
| return st.session_state['model_cache'][model_name] | |
| if use_gpu and GPU_AVAILABLE: | |
| if model_name == "VGG-Face": | |
| pass | |
| st.session_state['model_cache'][model_name] = model_name | |
| return model_name | |
| def preprocess_image(img_array): | |
| height, width = img_array.shape[:2] | |
| max_dimension = 640 | |
| if max(height, width) > max_dimension: | |
| scale = max_dimension / max(height, width) | |
| new_height = int(height * scale) | |
| new_width = int(width * scale) | |
| img_array = cv2.resize(img_array, (new_width, new_height)) | |
| gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY) | |
| equalized = cv2.equalizeHist(gray) | |
| enhanced = cv2.cvtColor(equalized, cv2.COLOR_GRAY2RGB) | |
| brightened = cv2.convertScaleAbs(img_array, alpha=1.5, beta=30) | |
| return {"original": img_array, "enhanced": enhanced, "brightened": brightened} | |
| def detect_faces_with_fallback(img_array): | |
| faces = None | |
| detection_method = "none" | |
| preprocessed = preprocess_image(img_array) | |
| try: | |
| faces = RetinaFace.detect_faces(preprocessed["original"]) | |
| if faces: | |
| detection_method = "retinaface_original" | |
| return faces, detection_method | |
| except Exception: | |
| pass | |
| try: | |
| faces = RetinaFace.detect_faces(preprocessed["enhanced"]) | |
| if faces: | |
| detection_method = "retinaface_enhanced" | |
| return faces, detection_method | |
| except Exception: | |
| pass | |
| try: | |
| faces = RetinaFace.detect_faces(preprocessed["brightened"]) | |
| if faces: | |
| detection_method = "retinaface_brightened" | |
| return faces, detection_method | |
| except Exception: | |
| pass | |
| try: | |
| face_cascade = cv2.CascadeClassifier( | |
| cv2.data.haarcascades + "haarcascade_frontalface_default.xml" | |
| ) | |
| gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY) | |
| opencv_faces = face_cascade.detectMultiScale(gray, 1.1, 4) | |
| if len(opencv_faces) > 0: | |
| faces = {} | |
| for i, (x, y, w, h) in enumerate(opencv_faces): | |
| faces[f"face_{i+1}"] = {"facial_area": (x, y, w, h), "score": 0.9} | |
| detection_method = "opencv_haar" | |
| return faces, detection_method | |
| except Exception: | |
| pass | |
| try: | |
| result = DeepFace.extract_faces( | |
| img_path=img_array, | |
| enforce_detection=False, | |
| detector_backend="opencv", | |
| align=True, | |
| ) | |
| if result and len(result) > 0: | |
| faces = {} | |
| for i, face_data in enumerate(result): | |
| facial_area = face_data.get("facial_area", {}) | |
| x = facial_area.get("x", 0) | |
| y = facial_area.get("y", 0) | |
| w = facial_area.get("w", 0) | |
| h = facial_area.get("h", 0) | |
| faces[f"face_{i+1}"] = {"facial_area": (x, y, w, h), "score": 0.8} | |
| detection_method = "deepface_detector" | |
| return faces, detection_method | |
| except Exception: | |
| pass | |
| if not faces: | |
| try: | |
| from deepface.detectors import MTCNN | |
| detector = MTCNN() | |
| dets = detector.detect_faces(img_array) | |
| if len(dets) > 0: | |
| faces = {} | |
| detection_method = "mtcnn_fallback" | |
| for i, d in enumerate(dets): | |
| x, y, w, h = d["box"] | |
| faces[f"face_{i+1}"] = { | |
| "facial_area": (x, y, w, h), | |
| "score": d.get("confidence", 0.8), | |
| } | |
| return faces, detection_method | |
| except Exception: | |
| pass | |
| return None, detection_method | |
| def analyze_with_models(img_array): | |
| results = [] | |
| models_to_try = ["VGG-Face", "Facenet", "DeepID"] if GPU_AVAILABLE else ["VGG-Face", "Facenet"] | |
| analyze_kwargs = { | |
| "actions": ["emotion"], | |
| "enforce_detection": False, | |
| "detector_backend": "skip", | |
| "prog_bar": False | |
| } | |
| if GPU_AVAILABLE: | |
| analyze_kwargs["use_gpu"] = True | |
| for model in models_to_try: | |
| try: | |
| model_obj = load_face_model(model, use_gpu=GPU_AVAILABLE) | |
| result = DeepFace.analyze( | |
| img_path=img_array, | |
| **analyze_kwargs | |
| ) | |
| if isinstance(result, list): | |
| result = result[0] | |
| if "emotion" in result: | |
| for key in result["emotion"]: | |
| if isinstance(result["emotion"][key], (np.floating, np.integer)): | |
| result["emotion"][key] = float(result["emotion"][key]) | |
| results.append(result) | |
| if len(results) >= 2: | |
| break | |
| except Exception as e: | |
| pass | |
| return results | |
| def weighted_average_results(results): | |
| if not results: | |
| return None | |
| avg_result = results[0].copy() | |
| weights = [1.0] * len(results) | |
| for i, res in enumerate(results[1:], start=1): | |
| if "emotion" in res and "emotion" in avg_result: | |
| for key in avg_result["emotion"]: | |
| if key in res["emotion"]: | |
| avg_result["emotion"][key] += float(res["emotion"][key]) * weights[i] | |
| total_weight = sum(weights) | |
| if "emotion" in avg_result: | |
| for key in avg_result["emotion"]: | |
| avg_result["emotion"][key] = float(avg_result["emotion"][key]) / total_weight | |
| dominant_key = max(avg_result["emotion"], key=avg_result["emotion"].get) | |
| avg_result["dominant_emotion"] = dominant_key | |
| return avg_result | |
| def analyze_emotion_with_models(img_array): | |
| max_dimension = 320 | |
| height, width = img_array.shape[:2] | |
| if max(height, width) > max_dimension: | |
| scale = max_dimension / max(height, width) | |
| small_img = cv2.resize(img_array, (int(width * scale), int(height * scale))) | |
| else: | |
| small_img = img_array.copy() | |
| faces, detection_method = detect_faces_with_fallback(small_img) | |
| if not faces: | |
| return ( | |
| None, | |
| 0, | |
| "No face was detected. Please use better lighting or adjust your face position.", | |
| ) | |
| try: | |
| if isinstance(faces, dict) and len(faces) > 0: | |
| face_key = list(faces.keys())[0] | |
| face = faces[face_key] | |
| x, y, w, h = face["facial_area"] | |
| if max(height, width) > max_dimension: | |
| scale_factor = max(height, width) / max_dimension | |
| x = int(x * scale_factor) | |
| y = int(y * scale_factor) | |
| w = int(w * scale_factor) | |
| h = int(h * scale_factor) | |
| x, y = max(0, x), max(0, y) | |
| w = min(w, img_array.shape[1] - x) | |
| h = min(h, img_array.shape[0] - y) | |
| if w > 0 and h > 0: | |
| face_img = img_array[y:y+h, x:x+w] | |
| else: | |
| face_img = img_array | |
| else: | |
| face_img = img_array | |
| results = analyze_with_models(face_img) | |
| if not results: | |
| return ( | |
| None, | |
| 0, | |
| "A face was detected, but we couldn't analyze the emotion. Please try a different angle or change your expression.", | |
| ) | |
| result_dict = weighted_average_results(results) | |
| if not result_dict or "emotion" not in result_dict: | |
| return ( | |
| None, | |
| 0, | |
| "We found your face, but couldn't process the emotion data. Please try again with better lighting.", | |
| ) | |
| dominant_emotion = result_dict["dominant_emotion"] | |
| confidence = result_dict["emotion"][dominant_emotion] * 100 | |
| confidence = float(confidence) | |
| if confidence < 35: | |
| return ( | |
| None, | |
| confidence, | |
| "We found your face, but the confidence was too low to determine your mood. Please try a different angle or select your mood manually." | |
| ) | |
| emotion_results = { | |
| "emotions": {k: float(v) for k, v in result_dict["emotion"].items()}, | |
| "dominant_emotion": dominant_emotion | |
| } | |
| return emotion_results, confidence, detection_method | |
| except Exception as e: | |
| st.error(f"Error in emotion analysis: {str(e)}") | |
| return ( | |
| None, | |
| 0, | |
| "An error occurred during emotion analysis. Please try again." | |
| ) | |
| st.set_page_config( | |
| page_title="Music Recommendation System", | |
| page_icon="🎼", | |
| layout="wide", | |
| initial_sidebar_state="expanded", | |
| ) | |
| USER_PREF_PATH = os.path.join(os.path.dirname(__file__), "user_preferences.json") | |
| hindi_music_recommendations = { | |
| "happy": [ | |
| { | |
| "title": "Badtameez Dil – Yeh Jawaani Hai Deewani", | |
| "url": "https://www.youtube.com/watch?v=II2EO3Nw4m0", | |
| }, | |
| { | |
| "title": "Gallan Goodiyaan – Dil Dhadakne Do", | |
| "url": "https://www.youtube.com/watch?v=jCEdTq3j-0U", | |
| }, | |
| { | |
| "title": "Nagada Sang Dhol – Goliyon Ki Raasleela Ram-Leela", | |
| "url": "https://www.youtube.com/watch?v=3X7x4Ye-tqo", | |
| }, | |
| { | |
| "title": "London Thumakda – Queen", | |
| "url": "https://www.youtube.com/watch?v=udra3Mfw2oo", | |
| }, | |
| { | |
| "title": "Balam Pichkari – Yeh Jawaani Hai Deewani", | |
| "url": "https://www.youtube.com/watch?v=0WtRNGubWGA", | |
| }, | |
| { | |
| "title": "Tune Maari Entriyaan – Gunday", | |
| "url": "https://www.youtube.com/watch?v=2I3NgxDAiqE", | |
| }, | |
| { | |
| "title": "Kar Gayi Chull – Kapoor & Sons", | |
| "url": "https://www.youtube.com/watch?v=NTHz9ephYTw", | |
| }, | |
| { | |
| "title": "Ghungroo – War", | |
| "url": "https://www.youtube.com/watch?v=qFkNATtc3mc", | |
| }, | |
| { | |
| "title": "Aankh Marey – Simmba", | |
| "url": "https://www.youtube.com/watch?v=O6OI2-p-gC4", | |
| }, | |
| { | |
| "title": "Morni Banke – Badhaai Ho", | |
| "url": "https://www.youtube.com/watch?v=h-v5tHtL_cA", | |
| }, | |
| { | |
| "title": "Dil Dhadakne Do – Title Track", | |
| "url": "https://www.youtube.com/watch?v=R5jQ8VudZbA", | |
| }, | |
| { | |
| "title": "Nashe Si Chadh Gayi – Befikre", | |
| "url": "https://www.youtube.com/watch?v=Wd2B8OAOc9c", | |
| }, | |
| { | |
| "title": "Tamma Tamma Again – Badrinath Ki Dulhania", | |
| "url": "https://www.youtube.com/watch?v=EEX_XM6SxmY", | |
| }, | |
| { | |
| "title": "Proper Patola – Namaste England", | |
| "url": "https://www.youtube.com/watch?v=Y1O_aIV1FNM", | |
| }, | |
| { | |
| "title": "Abhi Toh Party Shuru Hui Hai – Khoobsurat", | |
| "url": "https://www.youtube.com/watch?v=8LZgzAZ2lpQ", | |
| }, | |
| ], | |
| "sad": [ | |
| { | |
| "title": "Channa Mereya – Ae Dil Hai Mushkil", | |
| "url": "https://www.youtube.com/watch?v=284Ov7ysmfA", | |
| }, | |
| { | |
| "title": "Tum Hi Ho – Aashiqui 2", | |
| "url": "https://www.youtube.com/watch?v=Umqb9KENgmk", | |
| }, | |
| { | |
| "title": "Luka Chuppi – Rang De Basanti", | |
| "url": "https://www.youtube.com/watch?v=_ikZtcgAMxo", | |
| }, | |
| { | |
| "title": "Agar Tum Saath Ho – Tamasha", | |
| "url": "https://www.youtube.com/watch?v=sK7riqg2mr4", | |
| }, | |
| { | |
| "title": "Judaai – Badlapur", | |
| "url": "https://www.youtube.com/watch?v=zPpNZFgSzDo", | |
| }, | |
| { | |
| "title": "Kabhi Alvida Naa Kehna – KANK", | |
| "url": "https://www.youtube.com/watch?v=O8fIwHfZz2E", | |
| }, | |
| { | |
| "title": "Main Dhoondne Ko Zamaane Mein – Heartless", | |
| "url": "https://www.youtube.com/watch?v=5wqoxs9zG3w", | |
| }, | |
| { | |
| "title": "Tujhe Bhula Diya – Anjaana Anjaani", | |
| "url": "https://www.youtube.com/watch?v=_SK9K58Olqo", | |
| }, | |
| { | |
| "title": "Phir Le Aaya Dil – Barfi!", | |
| "url": "https://www.youtube.com/watch?v=ntC3sO-VeJY", | |
| }, | |
| { | |
| "title": "Bhula Dena – Aashiqui 2", | |
| "url": "https://www.youtube.com/watch?v=j4OVQJ6-R1U", | |
| }, | |
| { | |
| "title": "Kabira (Encore) – Yeh Jawaani Hai Deewani", | |
| "url": "https://www.youtube.com/watch?v=jHNNMj5bNQw", | |
| }, | |
| { | |
| "title": "Tera Ban Jaunga – Kabir Singh", | |
| "url": "https://www.youtube.com/watch?v=mQiiw7uRngA", | |
| }, | |
| { | |
| "title": "Humsafar – Badrinath Ki Dulhania", | |
| "url": "https://www.youtube.com/watch?v=8v-TWxPWIWc", | |
| }, | |
| { | |
| "title": "Agar Tum Mil Jao – Zeher", | |
| "url": "https://www.youtube.com/watch?v=C8tMQzcPXOY", | |
| }, | |
| { | |
| "title": "Dil Ke Paas – Wajah Tum Ho", | |
| "url": "https://www.youtube.com/watch?v=FOLUdLQBPuE", | |
| }, | |
| ], | |
| "angry": [ | |
| { | |
| "title": "Challa – Jab Tak Hai Jaan", | |
| "url": "https://www.youtube.com/watch?v=9a4izd3Rvdw", | |
| }, | |
| { | |
| "title": "Brothers Anthem – Brothers", | |
| "url": "https://www.youtube.com/watch?v=IjBAgWKW12Y", | |
| }, | |
| { | |
| "title": "Sultan – Sultan", | |
| "url": "https://www.youtube.com/watch?v=RYvUMglNznM", | |
| }, | |
| { | |
| "title": "Bulleya – Ae Dil Hai Mushkil", | |
| "url": "https://www.youtube.com/watch?v=hXh35CtnSyU", | |
| }, | |
| { | |
| "title": "Sadda Haq – Rockstar", | |
| "url": "https://www.youtube.com/watch?v=p9DQINKZxWE", | |
| }, | |
| { | |
| "title": "Jee Karda – Badlapur", | |
| "url": "https://www.youtube.com/watch?v=BN45QQ7R92M", | |
| }, | |
| { | |
| "title": "Dhan Te Nan – Kaminey", | |
| "url": "https://www.youtube.com/watch?v=m9RdKcnUvFU", | |
| }, | |
| { | |
| "title": "Bhaag DK Bose – Delhi Belly", | |
| "url": "https://www.youtube.com/watch?v=IQEDu8SPHao", | |
| }, | |
| { | |
| "title": "Sher Aaya Sher – Gully Boy", | |
| "url": "https://www.youtube.com/watch?v=hejXc_FSYb8", | |
| }, | |
| { | |
| "title": "Aala Re Aala – Simmba", | |
| "url": "https://www.youtube.com/watch?v=2wbVxHlOepM", | |
| }, | |
| { | |
| "title": "Zinda – Bhaag Milkha Bhaag", | |
| "url": "https://www.youtube.com/watch?v=RLzC55ai0eo", | |
| }, | |
| { | |
| "title": "Mardaani – Mardaani", | |
| "url": "https://www.youtube.com/watch?v=C1QOVnH0bKY", | |
| }, | |
| { | |
| "title": "Jai Ho – Slumdog Millionaire", | |
| "url": "https://www.youtube.com/watch?v=Yc5OyXmHD0w", | |
| }, | |
| { | |
| "title": "Malhari – Bajirao Mastani", | |
| "url": "https://www.youtube.com/watch?v=l_MyUGq7pgs", | |
| }, | |
| { | |
| "title": "Apna Time Aayega – Gully Boy", | |
| "url": "https://www.youtube.com/watch?v=SlHnlxDt2TQ", | |
| }, | |
| ], | |
| "fear": [ | |
| { | |
| "title": "Darr Ke Aage Jeet Hai – Mountain Dew", | |
| "url": "https://www.youtube.com/watch?v=xT7E-n1t3vI", | |
| }, | |
| { | |
| "title": "Hai Ram – Sarkar Raj", | |
| "url": "https://www.youtube.com/watch?v=zDQUylzavMQ", | |
| }, | |
| { | |
| "title": "Aaj Phir Jeene Ki Tamanna Hai – Guide", | |
| "url": "https://www.youtube.com/watch?v=2LG8LwEVlJE", | |
| }, | |
| { | |
| "title": "Main Hoon Don – Don", | |
| "url": "https://www.youtube.com/watch?v=xvNgjtgXOVo", | |
| }, | |
| { | |
| "title": "Khalbali – Rang De Basanti", | |
| "url": "https://www.youtube.com/watch?v=Yd-ngSyDZss", | |
| }, | |
| { | |
| "title": "Bhoot Hoon Main – Bhoot", | |
| "url": "https://www.youtube.com/watch?v=JNV4To5uzKA", | |
| }, | |
| { | |
| "title": "Bhool Bhulaiyaa – Title Track", | |
| "url": "https://www.youtube.com/watch?v=eN6AYHAT8UM", | |
| }, | |
| { | |
| "title": "Darr – Title Track", | |
| "url": "https://www.youtube.com/watch?v=BTAXAc1bJh8", | |
| }, | |
| { | |
| "title": "Pari – Title Track", | |
| "url": "https://www.youtube.com/watch?v=ZwyKOXwJsC0", | |
| }, | |
| { | |
| "title": "Bol Na Halke Halke – Jhoom Barabar Jhoom", | |
| "url": "https://www.youtube.com/watch?v=S9LZjGGeedw", | |
| }, | |
| { | |
| "title": "Raat Ka Nasha – Asoka", | |
| "url": "https://www.youtube.com/watch?v=OjaFNUA-UFE", | |
| }, | |
| { | |
| "title": "Phir Se Ud Chala – Rockstar", | |
| "url": "https://www.youtube.com/watch?v=2mWaqsC3U7k", | |
| }, | |
| { | |
| "title": "Roobaroo – Rang De Basanti", | |
| "url": "https://www.youtube.com/watch?v=BrfRB6aTZlM", | |
| }, | |
| { | |
| "title": "Khamoshiyan – Khamoshiyan", | |
| "url": "https://www.youtube.com/watch?v=FXiaIH49oAU", | |
| }, | |
| { | |
| "title": "Tum Ho Toh – Rock On!!", | |
| "url": "https://www.youtube.com/watch?v=hCsY8T0uBGA", | |
| }, | |
| ], | |
| "neutral": [ | |
| { | |
| "title": "Kabira – Yeh Jawaani Hai Deewani", | |
| "url": "https://www.youtube.com/watch?v=jHNNMj5bNQw", | |
| }, | |
| { | |
| "title": "Mitwa – Kabhi Alvida Naa Kehna", | |
| "url": "https://www.youtube.com/watch?v=Cv86br9MSBc", | |
| }, | |
| { | |
| "title": "Kun Faya Kun – Rockstar", | |
| "url": "https://www.youtube.com/watch?v=T94PHkuydcw", | |
| }, | |
| { | |
| "title": "Tum Se Hi – Jab We Met", | |
| "url": "https://www.youtube.com/watch?v=mt9xg0mmt28", | |
| }, | |
| { | |
| "title": "Iktara – Wake Up Sid", | |
| "url": "https://www.youtube.com/watch?v=fSS_R91Nimw", | |
| }, | |
| { | |
| "title": "Nazm Nazm – Bareilly Ki Barfi", | |
| "url": "https://www.youtube.com/watch?v=DK_UsATwoxI", | |
| }, | |
| { | |
| "title": "Ae Dil Hai Mushkil – ADHM", | |
| "url": "https://www.youtube.com/watch?v=6FURuLYrR_Q", | |
| }, | |
| { | |
| "title": "Raabta – Agent Vinod", | |
| "url": "https://www.youtube.com/watch?v=zAU_rsoS5ok", | |
| }, | |
| { | |
| "title": "Tera Hone Laga Hoon – Ajab Prem Ki Ghazab Kahani", | |
| "url": "https://www.youtube.com/watch?v=K0IvuwrSFaI", | |
| }, | |
| { | |
| "title": "Safarnama – Tamasha", | |
| "url": "https://www.youtube.com/watch?v=zLv0V_19L-A", | |
| }, | |
| { | |
| "title": "Pehli Nazar Mein – Race", | |
| "url": "https://www.youtube.com/watch?v=BadBAMnPX0I", | |
| }, | |
| { | |
| "title": "Saibo – Shor in the City", | |
| "url": "https://www.youtube.com/watch?v=zXLgYBSdv74", | |
| }, | |
| { | |
| "title": "O Re Piya – Aaja Nachle", | |
| "url": "https://www.youtube.com/watch?v=iv7lcUkFVSc", | |
| }, | |
| { | |
| "title": "Khairiyat – Chhichhore", | |
| "url": "https://www.youtube.com/watch?v=hoNb6HuNmU0", | |
| }, | |
| { | |
| "title": "Manwa Laage – Happy New Year", | |
| "url": "https://www.youtube.com/watch?v=d8IT-16kA8M", | |
| }, | |
| ], | |
| "disgust": [ | |
| { | |
| "title": "Beedi – Omkara", | |
| "url": "https://www.youtube.com/watch?v=XLJCtZKQGrY", | |
| }, | |
| { | |
| "title": "Emotional Atyachaar – Dev D", | |
| "url": "https://www.youtube.com/watch?v=Vng5mg0iY0k", | |
| }, | |
| { | |
| "title": "Gandi Baat – R... Rajkumar", | |
| "url": "https://www.youtube.com/watch?v=vvLBXO4MnKQ", | |
| }, | |
| { | |
| "title": "Bluffmaster – Bluffmaster", | |
| "url": "https://www.youtube.com/watch?v=t5UzO4gOYKc", | |
| }, | |
| { | |
| "title": "Dhoom Machale – Dhoom", | |
| "url": "https://www.youtube.com/watch?v=ymk2_5a2V8g", | |
| }, | |
| { | |
| "title": "Zor Ka Jhatka – Action Replayy", | |
| "url": "https://www.youtube.com/watch?v=UZV8Yb4hVgU", | |
| }, | |
| { | |
| "title": "Genda Phool – Delhi-6", | |
| "url": "https://www.youtube.com/watch?v=f0CvPQ3l-Xg", | |
| }, | |
| { | |
| "title": "Dum Maro Dum – Hare Rama Hare Krishna", | |
| "url": "https://www.youtube.com/watch?v=BZNT_Y-mAkE", | |
| }, | |
| { | |
| "title": "Chaar Botal Vodka – Ragini MMS 2", | |
| "url": "https://www.youtube.com/watch?v=x8F5dz8kv1w", | |
| }, | |
| { | |
| "title": "Kamli – Dhoom 3", | |
| "url": "https://www.youtube.com/watch?v=C8kSrkz8Hz8", | |
| }, | |
| { | |
| "title": "Munni Badnaam Hui – Dabangg", | |
| "url": "https://www.youtube.com/watch?v=Jn5hsfbhWx4", | |
| }, | |
| { | |
| "title": "Sheila Ki Jawani – Tees Maar Khan", | |
| "url": "https://www.youtube.com/watch?v=ZTmF2v59CtI", | |
| }, | |
| { | |
| "title": "Baby Doll – Ragini MMS 2", | |
| "url": "https://www.youtube.com/watch?v=yP9KiFTyBks", | |
| }, | |
| { | |
| "title": "Oo Antava – Pushpa", | |
| "url": "https://www.youtube.com/watch?v=kyNdRJR_NRs", | |
| }, | |
| { | |
| "title": "Laila Main Laila – Raees", | |
| "url": "https://www.youtube.com/watch?v=fMW7ze7-Gik", | |
| }, | |
| ], | |
| "surprise": [ | |
| { | |
| "title": "Kala Chashma – Baar Baar Dekho", | |
| "url": "https://www.youtube.com/watch?v=k4yXQkG2s1E", | |
| }, | |
| { | |
| "title": "Matargashti – Tamasha", | |
| "url": "https://www.youtube.com/watch?v=6vKucgAeF_Q", | |
| }, | |
| { | |
| "title": "Sooraj Dooba Hain – Roy", | |
| "url": "https://www.youtube.com/watch?v=nJZcbidTutE", | |
| }, | |
| { | |
| "title": "Ghagra – Yeh Jawaani Hai Deewani", | |
| "url": "https://www.youtube.com/watch?v=caoGNx1LF2Q", | |
| }, | |
| { | |
| "title": "Ishq Shava – Jab Tak Hai Jaan", | |
| "url": "https://www.youtube.com/watch?v=2kH4fLrwc0A", | |
| }, | |
| { | |
| "title": "Naina – Dangal", | |
| "url": "https://www.youtube.com/watch?v=BTtx6HMzunQ", | |
| }, | |
| { | |
| "title": "Haanikaarak Bapu – Dangal", | |
| "url": "https://www.youtube.com/watch?v=KyZZrClK0rM", | |
| }, | |
| { | |
| "title": "Malhari – Bajirao Mastani", | |
| "url": "https://www.youtube.com/watch?v=l_MyUGq7pgs", | |
| }, | |
| { | |
| "title": "Dilbar – Satyameva Jayate", | |
| "url": "https://www.youtube.com/watch?v=JFcgOboQZ08", | |
| }, | |
| { | |
| "title": "Bom Diggy Diggy – Sonu Ke Titu Ki Sweety", | |
| "url": "https://www.youtube.com/watch?v=yIIGQB6EMAM", | |
| }, | |
| { | |
| "title": "Cutiepie – Ae Dil Hai Mushkil", | |
| "url": "https://www.youtube.com/watch?v=f6vY6tYvKGA", | |
| }, | |
| { | |
| "title": "Coca Cola – Luka Chuppi", | |
| "url": "https://www.youtube.com/watch?v=_cPHiwPqbqo", | |
| }, | |
| { | |
| "title": "Sweety Tera Drama – Bareilly Ki Barfi", | |
| "url": "https://www.youtube.com/watch?v=1MU4wjcxoR4", | |
| }, | |
| { | |
| "title": "Dil Chori – Sonu Ke Titu Ki Sweety", | |
| "url": "https://www.youtube.com/watch?v=MU9oULiwmaU", | |
| }, | |
| { | |
| "title": "Koi Kahe Kehta Rahe – Dil Chahta Hai", | |
| "url": "https://www.youtube.com/watch?v=4vEBkbkzwR8", | |
| }, | |
| ], | |
| } | |
| def load_css(): | |
| st.markdown( | |
| """ | |
| <style> | |
| * { | |
| font-family: 'Segoe UI', Arial, sans-serif !important; | |
| } | |
| .main-header { | |
| font-size: 2.8rem; | |
| color: #ff5722; | |
| text-align: center; | |
| margin-bottom: 1.5rem; | |
| font-weight: bold; | |
| text-shadow: 2px 2px 4px rgba(0,0,0,0.1); | |
| background: linear-gradient(45deg, #ff5722, #FF9800); | |
| -webkit-background-clip: text; | |
| -webkit-text-fill-color: transparent; | |
| // animation: pulse 2s infinite; | |
| } | |
| @keyframes pulse { | |
| 0% { transform: scale(1); } | |
| 50% { transform: scale(1.03); } | |
| 100% { transform: scale(1); } | |
| } | |
| .hindi-title { | |
| font-size: 1.5rem; | |
| color: #333; | |
| font-weight: 600; | |
| margin-bottom: 0.5rem; | |
| } | |
| .subheader { | |
| font-size: 1.8rem; | |
| color: #ff5722; | |
| margin-bottom: 1rem; | |
| text-transform: uppercase; | |
| letter-spacing: 1px; | |
| border-bottom: 2px solid #ff5722; | |
| padding-bottom: 0.5rem; | |
| } | |
| .emotion-card { | |
| padding: 1.5rem; | |
| border-radius: 12px; | |
| margin-bottom: 1.5rem; | |
| background-color: #2596be; | |
| border-left: 5px solid #ff5722; | |
| box-shadow: 0 4px 6px rgba(0,0,0,0.05); | |
| transition: all 0.3s ease; | |
| } | |
| .emotion-card:hover { | |
| box-shadow: 0 6px 12px rgba(0,0,0,0.1); | |
| transform: translateY(-3px); | |
| } | |
| .recommendation-card { | |
| padding: 1.5rem; | |
| border-radius: 12px; | |
| margin: 1.5rem 0; | |
| background-color: #009933; | |
| border-left: 6px solid #ff5722; | |
| box-shadow: 0 4px 15px rgba(0,0,0,0.08); | |
| transition: transform 0.3s; | |
| } | |
| .recommendation-card:hover { | |
| transform: translateY(-5px); | |
| box-shadow: 0 8px 20px rgba(0,0,0,0.12); | |
| } | |
| .stButton>button { | |
| background-color: #ff5722; | |
| color: white; | |
| border-radius: 20px; | |
| font-size: 1rem; | |
| padding: 0.5rem 1.5rem; | |
| border: none; | |
| box-shadow: 0 2px 5px rgba(0,0,0,0.1); | |
| transition: all 0.3s; | |
| } | |
| .stButton>button:hover { | |
| background-color: #ffffff; | |
| box-shadow: 0 4px 8px rgba(0,0,0,0.15); | |
| transform: translateY(-2px); | |
| } | |
| .like-button { | |
| display: flex; | |
| justify-content: center; | |
| align-items: center; | |
| } | |
| .mood-button { | |
| text-align: center; | |
| margin-bottom: 12px; | |
| } | |
| .mood-emoji { | |
| font-size: 2.2rem; | |
| margin-bottom: 8px; | |
| display: block; | |
| } | |
| .mood-label { | |
| display: block; | |
| text-align: center; | |
| } | |
| .stSelectbox>div>div { | |
| background-color: #06c91a; | |
| border: 1px solid #ddd; | |
| border-radius: 8px; | |
| } | |
| .info-box { | |
| background-color: #009933; | |
| border-left: 4px solid #2196F3; | |
| padding: 1rem; | |
| border-radius: 8px; | |
| margin-bottom: 1rem; | |
| } | |
| .feature-card { | |
| background-color: #3399ff; | |
| border-radius: 10px; | |
| box-shadow: 0 4px 6px rgba(0,0,0,0.05); | |
| padding: 1.5rem; | |
| margin-bottom: 1rem; | |
| transition: all 0.3s ease; | |
| border-top: 4px solid #ff5722; | |
| } | |
| .feature-card:hover { | |
| transform: translateY(-5px); | |
| box-shadow: 0 8px 15px rgba(0,0,0,0.1); | |
| } | |
| .feature-icon { | |
| font-size: 2rem; | |
| margin-bottom: 0.5rem; | |
| color: #ff5722; | |
| } | |
| .sidebar-info { | |
| background-color: #ffffff; | |
| padding: 1rem; | |
| border-radius: 8px; | |
| margin-top: 1rem; | |
| } | |
| .sidebar-header { | |
| color: #ff5722; | |
| font-size: 1.2rem; | |
| font-weight: bold; | |
| margin-bottom: 0.5rem; | |
| } | |
| .step-container { | |
| display: flex; | |
| margin-bottom: 1rem; | |
| } | |
| .step-number { | |
| background-color: #ff5722; | |
| color: white; | |
| width: 30px; | |
| height: 30px; | |
| display: flex; | |
| align-items: center; | |
| justify-content: center; | |
| border-radius: 50%; | |
| margin-right: 10px; | |
| flex-shrink: 0; | |
| } | |
| .step-content { | |
| flex: 1; | |
| } | |
| .footer { | |
| text-align: center; | |
| margin-top: 3rem; | |
| padding-top: 1rem; | |
| border-top: 1px solid #eee; | |
| font-size: 0.9rem; | |
| color: #777; | |
| } | |
| @keyframes fadeIn { | |
| from { opacity: 0; transform: translateY(20px); } | |
| to { opacity: 1; transform: translateY(0); } | |
| } | |
| .animated-fade { | |
| animation: fadeIn 0.5s ease-out; | |
| } | |
| .stVideo { | |
| border-radius: 10px; | |
| overflow: hidden; | |
| box-shadow: 0 4px 8px rgba(0,0,0,0.1); | |
| } | |
| .hide-on-secondary-pages { | |
| display: none; | |
| } | |
| .show-on-home { | |
| display: block; | |
| } | |
| .custom-mood-btn { | |
| width: 100%; | |
| border-radius: 12px; | |
| padding: 10px; | |
| background-color: #ffffff; | |
| color: #333; | |
| border: 1px solid #ddd; | |
| cursor: pointer; | |
| display: flex; | |
| flex-direction: column; | |
| align-items: center; | |
| justify-content: center; | |
| transition: all 0.3s ease; | |
| margin-bottom: 10px; | |
| } | |
| .custom-mood-btn:hover { | |
| background-color: #ffffff; | |
| transform: translateY(-3px); | |
| box-shadow: 0 4px 8px rgba(0,0,0,0.1); | |
| } | |
| </style> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| def load_user_preferences(): | |
| if not os.path.exists(USER_PREF_PATH): | |
| return {"liked_songs": [], "emotion_history": []} | |
| try: | |
| with open(USER_PREF_PATH, "r") as f: | |
| return json.load(f) | |
| except: | |
| return {"liked_songs": [], "emotion_history": []} | |
| def load_face_model(model_name): | |
| return model_name | |
| def preprocess_image(img_array): | |
| gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY) | |
| equalized = cv2.equalizeHist(gray) | |
| enhanced = cv2.cvtColor(equalized, cv2.COLOR_GRAY2RGB) | |
| brightened = cv2.convertScaleAbs(img_array, alpha=1.5, beta=30) | |
| return {"original": img_array, "enhanced": enhanced, "brightened": brightened} | |
| def detect_faces_with_fallback(img_array): | |
| faces = None | |
| detection_method = "none" | |
| preprocessed = preprocess_image(img_array) | |
| try: | |
| faces = RetinaFace.detect_faces(preprocessed["original"]) | |
| if faces: | |
| detection_method = "retinaface_original" | |
| return faces, detection_method | |
| except Exception: | |
| pass | |
| try: | |
| faces = RetinaFace.detect_faces(preprocessed["enhanced"]) | |
| if faces: | |
| detection_method = "retinaface_enhanced" | |
| return faces, detection_method | |
| except Exception: | |
| pass | |
| try: | |
| faces = RetinaFace.detect_faces(preprocessed["brightened"]) | |
| if faces: | |
| detection_method = "retinaface_brightened" | |
| return faces, detection_method | |
| except Exception: | |
| pass | |
| try: | |
| face_cascade = cv2.CascadeClassifier( | |
| cv2.data.haarcascades + "haarcascade_frontalface_default.xml" | |
| ) | |
| gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY) | |
| opencv_faces = face_cascade.detectMultiScale(gray, 1.1, 4) | |
| if len(opencv_faces) > 0: | |
| faces = {} | |
| for i, (x, y, w, h) in enumerate(opencv_faces): | |
| faces[f"face_{i+1}"] = {"facial_area": (x, y, w, h), "score": 0.9} | |
| detection_method = "opencv_haar" | |
| return faces, detection_method | |
| except Exception: | |
| pass | |
| try: | |
| result = DeepFace.extract_faces( | |
| img_path=img_array, | |
| enforce_detection=False, | |
| detector_backend="opencv", | |
| align=True, | |
| ) | |
| if result and len(result) > 0: | |
| faces = {} | |
| for i, face_data in enumerate(result): | |
| facial_area = face_data.get("facial_area", {}) | |
| x = facial_area.get("x", 0) | |
| y = facial_area.get("y", 0) | |
| w = facial_area.get("w", 0) | |
| h = facial_area.get("h", 0) | |
| faces[f"face_{i+1}"] = {"facial_area": (x, y, w, h), "score": 0.8} | |
| detection_method = "deepface_detector" | |
| return faces, detection_method | |
| except Exception: | |
| pass | |
| if not faces: | |
| try: | |
| from deepface.detectors import MTCNN | |
| detector = MTCNN() | |
| dets = detector.detect_faces(img_array) | |
| if len(dets) > 0: | |
| faces = {} | |
| detection_method = "mtcnn_fallback" | |
| for i, d in enumerate(dets): | |
| x, y, w, h = d["box"] | |
| faces[f"face_{i+1}"] = { | |
| "facial_area": (x, y, w, h), | |
| "score": d.get("confidence", 0.8), | |
| } | |
| return faces, detection_method | |
| except Exception: | |
| pass | |
| return None, detection_method | |
| def analyze_with_models(img_array): | |
| results = [] | |
| models = ["VGG-Face", "Facenet", "OpenFace", "DeepID", "ArcFace", "Dlib"] | |
| for model in models: | |
| try: | |
| result = DeepFace.analyze( | |
| img_path=img_array, | |
| actions=("age", "gender", "race", "emotion"), | |
| enforce_detection=False, | |
| detector_backend="retinaface", | |
| ) | |
| if isinstance(result, list): | |
| result = result[0] | |
| results.append(result) | |
| except Exception as e: | |
| st.error(f"Error Analyzing With Model {model}: {str(e)}") | |
| return results | |
| def weighted_average_results(results): | |
| if not results: | |
| return None | |
| avg_result = results[0].copy() | |
| weights = [1.0] * len(results) | |
| for i, res in enumerate(results[1:], start=1): | |
| if "age" in res and "age" in avg_result: | |
| avg_result["age"] += res["age"] * weights[i] | |
| if "gender" in res and "gender" in avg_result: | |
| if res["gender"] == "Woman": | |
| avg_result["gender"] = "Woman" | |
| if "race" in res and "race" in avg_result: | |
| for key in avg_result["race"]: | |
| if key in res["race"]: | |
| avg_result["race"][key] += res["race"][key] * weights[i] | |
| if "emotion" in res and "emotion" in avg_result: | |
| for key in avg_result["emotion"]: | |
| if key in res["emotion"]: | |
| avg_result["emotion"][key] += res["emotion"][key] * weights[i] | |
| total_weight = sum(weights) | |
| if "age" in avg_result: | |
| avg_result["age"] /= total_weight | |
| if "race" in avg_result: | |
| for key in avg_result["race"]: | |
| avg_result["race"][key] /= total_weight | |
| if "emotion" in avg_result: | |
| for key in avg_result["emotion"]: | |
| avg_result["emotion"][key] /= total_weight | |
| avg_result["dominant_emotion"] = max(avg_result["emotion"], key=avg_result["emotion"].get) | |
| return avg_result | |
| def analyze_emotion_with_models(img_array): | |
| faces, detection_method = detect_faces_with_fallback(img_array) | |
| if not faces: | |
| return ( | |
| None, | |
| 0, | |
| "No face was detected. Please use better lighting or adjust your face position.", | |
| ) | |
| try: | |
| if isinstance(faces, dict) and len(faces) > 0: | |
| face_key = list(faces.keys())[0] | |
| face = faces[face_key] | |
| x, y, w, h = face["facial_area"] | |
| x, y = max(0, x), max(0, y) | |
| w = min(w, img_array.shape[1] - x) | |
| h = min(h, img_array.shape[0] - y) | |
| if w > 0 and h > 0: | |
| face_img = img_array[y : y + h, x : x + w] | |
| else: | |
| face_img = img_array | |
| else: | |
| face_img = img_array | |
| results = analyze_with_models(face_img) | |
| if not results: | |
| return ( | |
| None, | |
| 0, | |
| "A face was detected, but we couldn't analyze the emotion. Please try a different angle or change your expression.", | |
| ) | |
| result_dict = weighted_average_results(results) | |
| if not result_dict or "emotion" not in result_dict: | |
| return ( | |
| None, | |
| 0, | |
| "We found your face, but couldn't process the emotion data. Please try again with better lighting.", | |
| ) | |
| dominant_emotion = result_dict["dominant_emotion"] | |
| confidence = result_dict["emotion"][dominant_emotion] * 100 | |
| if confidence < 35: | |
| return ( | |
| None, | |
| confidence, | |
| "We found your face, but the confidence was too low to determine your mood. Please try a different angle or select your mood manually." | |
| ) | |
| emotion_results = {"emotions": result_dict["emotion"], "dominant_emotion": dominant_emotion} | |
| return emotion_results, confidence, detection_method | |
| except Exception as e: | |
| st.error(f"Error in emotion analysis: {str(e)}") | |
| return ( | |
| None, | |
| 0, | |
| "An error occurred during emotion analysis. Please try again." | |
| ) | |
| def music_recommendation(): | |
| st.markdown( | |
| '<p class="subheader">Music that matches your mood</p>', unsafe_allow_html=True | |
| ) | |
| st.markdown( | |
| '<div class="info-box">📝 <b>How it works:</b> Choose your mood below or take a photo so we can see how you feel. We will suggest songs that match your mood.</div>', | |
| unsafe_allow_html=True, | |
| ) | |
| user_prefs = load_user_preferences() | |
| st.markdown("### Select Your Mood:") | |
| st.markdown('<div class="animated-fade">', unsafe_allow_html=True) | |
| st.write("Click on how you feel right now:") | |
| all_moods = { | |
| "happy": {"emoji": "😊", "label": "Happy"}, | |
| "sad": {"emoji": "😢", "label": "Sad"}, | |
| "angry": {"emoji": "😠", "label": "Angry"}, | |
| "fear": {"emoji": "😨", "label": "Fear"}, | |
| "neutral": {"emoji": "😐", "label": "Neutral"}, | |
| "disgust": {"emoji": "🤢", "label": "Disgust"}, | |
| "surprise": {"emoji": "😲", "label": "Surprise"}, | |
| } | |
| mood_selected = None | |
| cols_row1 = st.columns(4) | |
| mood_keys = list(all_moods.keys()) | |
| for i in range(4): | |
| mood = mood_keys[i] | |
| mood_info = all_moods[mood] | |
| with cols_row1[i]: | |
| st.markdown('<div class="mood-button">', unsafe_allow_html=True) | |
| if st.button( | |
| f"{mood_info['emoji']} {mood_info['label']}", key=f"mood_{mood}" | |
| ): | |
| mood_selected = mood | |
| st.markdown("</div>", unsafe_allow_html=True) | |
| cols_row2 = st.columns(3) | |
| for i in range(4, len(mood_keys)): | |
| col_idx = i - 4 | |
| mood = mood_keys[i] | |
| mood_info = all_moods[mood] | |
| with cols_row2[col_idx]: | |
| st.markdown('<div class="mood-button">', unsafe_allow_html=True) | |
| if st.button( | |
| f"{mood_info['emoji']} {mood_info['label']}", key=f"mood_{mood}" | |
| ): | |
| mood_selected = mood | |
| st.markdown("</div>", unsafe_allow_html=True) | |
| st.markdown("</div>", unsafe_allow_html=True) | |
| st.markdown("<hr style='margin: 2rem 0; opacity: 0.3;'>", unsafe_allow_html=True) | |
| st.markdown('<div class="animated-fade">', unsafe_allow_html=True) | |
| st.markdown("### Or Take or Upload a Photo") | |
| st.write("Let our AI check your face to see how you're feeling.") | |
| col1, col2 = st.columns([1, 1]) | |
| with col1: | |
| use_camera = st.checkbox("Use Camera", value=False) | |
| img_file = None | |
| if use_camera: | |
| img_file = st.camera_input("Take a photo", key="camera_input") | |
| st.write("OR") | |
| uploaded_file = st.file_uploader("Upload a photo", type=["jpg", "jpeg", "png"]) | |
| with st.expander("📷 Tips to help detect faces better:"): | |
| st.markdown( | |
| """ | |
| - Make sure your face is well-lit | |
| - Look directly at the camera | |
| - Remove sunglasses or hats | |
| - Keep your face in the center of the frame | |
| - If your face isn't detected, try a different angle | |
| """ | |
| ) | |
| st.markdown("</div>", unsafe_allow_html=True) | |
| if mood_selected: | |
| timestamp = time.strftime("%Y-%m-%d %H:%M:%S") | |
| user_prefs["emotion_history"].append( | |
| { | |
| "emotion": mood_selected, | |
| "confidence": 100.0, | |
| "timestamp": timestamp, | |
| "selection": "manual", | |
| } | |
| ) | |
| save_user_preferences(user_prefs) | |
| st.markdown('<div class="animated-fade">', unsafe_allow_html=True) | |
| st.markdown( | |
| f'<div class="emotion-card"><h3>{all_moods[mood_selected]["emoji"]} Your Selected Mood</h3>' | |
| f"<p>You're feeling: <strong>{mood_selected.capitalize()}</strong></p></div>", | |
| unsafe_allow_html=True, | |
| ) | |
| st.markdown("</div>", unsafe_allow_html=True) | |
| show_hindi_recommendations(mood_selected, user_prefs) | |
| image_source = img_file or uploaded_file | |
| if image_source is not None: | |
| with st.spinner("Our AI is checking your mood..."): | |
| img = Image.open(image_source) | |
| img_array = np.array(img) | |
| progress_bar = st.progress(0) | |
| progress_bar.progress(25) | |
| emotion_results, confidence, detection_method = analyze_emotion_with_models( | |
| img_array | |
| ) | |
| progress_bar.progress(75) | |
| with col2: | |
| st.image(img, caption="Your photo", use_container_width=True) | |
| if emotion_results: | |
| if confidence > 25: | |
| dominant_emotion = emotion_results["dominant_emotion"] | |
| emotion_emojis = { | |
| "happy": "😊", | |
| "sad": "😢", | |
| "angry": "😠", | |
| "fear": "😨", | |
| "neutral": "😐", | |
| "disgust": "🤢", | |
| "surprise": "😲", | |
| } | |
| emoji = emotion_emojis.get(dominant_emotion, "") | |
| st.markdown('<div class="animated-fade">', unsafe_allow_html=True) | |
| st.markdown( | |
| f'<div class="emotion-card"><h3>{emoji} Your mood check</h3>' | |
| f"<p>We detected that you're feeling: <strong>{dominant_emotion.capitalize()}</strong> " | |
| f"(Confidence: {confidence:.1f}%)</p></div>", | |
| unsafe_allow_html=True, | |
| ) | |
| emotions_df = pd.DataFrame( | |
| { | |
| "Emotion": list(emotion_results["emotions"].keys()), | |
| "Confidence": list(emotion_results["emotions"].values()), | |
| } | |
| ) | |
| fig = px.bar( | |
| emotions_df, | |
| x="Emotion", | |
| y="Confidence", | |
| color="Emotion", | |
| title="Your emotion results.", | |
| labels={ | |
| "Confidence": "Confidence %", | |
| "Emotion": "Emotions Detected", | |
| }, | |
| ) | |
| fig.update_layout( | |
| plot_bgcolor="rgba(0,0,0,0)", | |
| paper_bgcolor="rgba(0,0,0,0)", | |
| font=dict(family="Segoe UI, Arial, sans-serif"), | |
| ) | |
| st.plotly_chart(fig, use_container_width=True) | |
| st.markdown("</div>", unsafe_allow_html=True) | |
| progress_bar.progress(100) | |
| timestamp = time.strftime("%Y-%m-%d %H:%M:%S") | |
| user_prefs["emotion_history"].append( | |
| { | |
| "emotion": dominant_emotion, | |
| "confidence": confidence, | |
| "timestamp": timestamp, | |
| "selection": "ai", | |
| } | |
| ) | |
| if len(user_prefs["emotion_history"]) > 50: | |
| user_prefs["emotion_history"] = user_prefs["emotion_history"][ | |
| -50: | |
| ] | |
| save_user_preferences(user_prefs) | |
| show_hindi_recommendations(dominant_emotion, user_prefs) | |
| else: | |
| st.warning( | |
| f"We found your face but the confidence ({confidence:.1f}%) " | |
| "is too low to determine your mood. Please try again or select manually." | |
| ) | |
| else: | |
| if detection_method != "none": | |
| st.warning( | |
| "We found your face, but we couldn't clearly determine your mood." | |
| ) | |
| else: | |
| st.warning( | |
| "We didn't detect a face in your picture. Please try again." | |
| ) | |
| st.markdown('<div class="animated-fade">', unsafe_allow_html=True) | |
| st.markdown("### Let's choose your mood manually instead.") | |
| st.write( | |
| "Don't worry! You can still get great song recommendations by telling us how you feel:" | |
| ) | |
| available_emotions = list(hindi_music_recommendations.keys()) | |
| selected_emotion = st.selectbox( | |
| "How are you feeling right now?", | |
| available_emotions, | |
| format_func=lambda x: x.capitalize(), | |
| ) | |
| if st.button("Get Music Recommendations", key="manual_mood"): | |
| timestamp = time.strftime("%Y-%m-%d %H:%M:%S") | |
| user_prefs["emotion_history"].append( | |
| { | |
| "emotion": selected_emotion, | |
| "confidence": 100.0, | |
| "timestamp": timestamp, | |
| "selection": "manual", | |
| } | |
| ) | |
| if len(user_prefs["emotion_history"]) > 50: | |
| user_prefs["emotion_history"] = user_prefs["emotion_history"][ | |
| -50: | |
| ] | |
| save_user_preferences(user_prefs) | |
| show_hindi_recommendations(selected_emotion, user_prefs) | |
| st.markdown("</div>", unsafe_allow_html=True) | |
| def show_hindi_recommendations(emotion, user_prefs): | |
| emotion_emojis = { | |
| "happy": "😊", | |
| "sad": "😢", | |
| "angry": "😠", | |
| "fear": "😨", | |
| "neutral": "😐", | |
| "disgust": "🤢", | |
| "surprise": "😲", | |
| } | |
| emoji = emotion_emojis.get(emotion, "") | |
| st.markdown('<div class="animated-fade">', unsafe_allow_html=True) | |
| st.markdown( | |
| f"<h3>{emoji} Songs for your {emotion.capitalize()} Mood</h3>", | |
| unsafe_allow_html=True, | |
| ) | |
| st.write( | |
| "Here are songs that match your current mood. Click the heart to save your favorites." | |
| ) | |
| recommendations = get_personalized_hindi_recommendations(emotion, user_prefs) | |
| if not recommendations: | |
| st.warning("No songs were found for this mood. Please choose another one.") | |
| return | |
| for i, song in enumerate(recommendations[:3]): | |
| with st.container(): | |
| st.markdown( | |
| f""" | |
| <div class="recommendation-card"> | |
| <h4 class="hindi-title">{song['title']}</h4> | |
| </div> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| try: | |
| st.video(song["url"]) | |
| except Exception: | |
| st.error(f"Sorry, we couldn't load this video: {song['title']}") | |
| continue | |
| col1, col2 = st.columns([1, 6]) | |
| with col1: | |
| st.markdown('<div class="like-button">', unsafe_allow_html=True) | |
| if st.button("😍", key=f"like_{emotion}_{i}"): | |
| if song not in user_prefs["liked_songs"]: | |
| user_prefs["liked_songs"].append(song) | |
| st.success("Added to your favorites!") | |
| save_user_preferences(user_prefs) | |
| st.markdown("</div>", unsafe_allow_html=True) | |
| if st.button("🔄 Find Different Songs", key=f"refresh_{emotion}"): | |
| st.rerun() | |
| st.markdown("</div>", unsafe_allow_html=True) | |
| st.markdown( | |
| "<div class='footer'>Songs chosen based on your mood and what you like.</div>", | |
| unsafe_allow_html=True, | |
| ) | |
| def get_personalized_hindi_recommendations(emotion, user_prefs): | |
| if emotion not in hindi_music_recommendations: | |
| return [] | |
| all_recommendations = hindi_music_recommendations[emotion].copy() | |
| if not all_recommendations: | |
| return [] | |
| liked_urls = [song["url"] for song in user_prefs["liked_songs"]] | |
| new_recommendations = [ | |
| song for song in all_recommendations if song["url"] not in liked_urls | |
| ] | |
| previously_liked = [ | |
| song for song in all_recommendations if song["url"] in liked_urls | |
| ] | |
| personalized = new_recommendations + previously_liked | |
| if len(personalized) < 3: | |
| other_emotions = [e for e in hindi_music_recommendations.keys() if e != emotion] | |
| for other_emotion in other_emotions: | |
| other_songs = [ | |
| song | |
| for song in hindi_music_recommendations[other_emotion] | |
| if song["url"] not in liked_urls | |
| ] | |
| personalized.extend(other_songs) | |
| if len(personalized) >= 5: | |
| break | |
| if len(new_recommendations) > 3: | |
| random.shuffle(new_recommendations) | |
| result = new_recommendations[:3] | |
| else: | |
| result = new_recommendations[:] | |
| if previously_liked and len(result) < 3: | |
| random.shuffle(previously_liked) | |
| result.extend(previously_liked[: 3 - len(result)]) | |
| return result | |
| def show_mood_history(): | |
| st.markdown('<p class="subheader">Your Mood History</p>', unsafe_allow_html=True) | |
| st.markdown( | |
| '<div class="info-box">📊 <b>Your Personal Mood Tracker:</b> Watch how your feelings change over time. This helps us pick better songs for you.</div>', | |
| unsafe_allow_html=True, | |
| ) | |
| user_prefs = load_user_preferences() | |
| history = user_prefs.get("emotion_history", []) | |
| if not history: | |
| st.info( | |
| "You don't have any mood history yet. Try the Music Recommendation feature to start building your profile." | |
| ) | |
| return | |
| df = pd.DataFrame(history) | |
| df["timestamp"] = pd.to_datetime(df["timestamp"]) | |
| df["source"] = df.apply( | |
| lambda row: "Manual Selection" | |
| if row.get("selection") == "manual" | |
| else "AI Detection", | |
| axis=1, | |
| ) | |
| st.markdown('<div class="animated-fade">', unsafe_allow_html=True) | |
| st.subheader("Your Mood Over Time") | |
| fig = px.line( | |
| df, | |
| x="timestamp", | |
| y="confidence", | |
| color="emotion", | |
| symbol="source", | |
| title="Your Mood History", | |
| labels={ | |
| "timestamp": "Time", | |
| "confidence": "Confidence %", | |
| "emotion": "Emotion", | |
| }, | |
| ) | |
| fig.update_layout( | |
| plot_bgcolor="rgba(0,0,0,0)", | |
| paper_bgcolor="rgba(0,0,0,0)", | |
| font=dict(family="Segoe UI, Arial, sans-serif"), | |
| ) | |
| st.plotly_chart(fig, use_container_width=True) | |
| emotion_counts = df["emotion"].value_counts().reset_index() | |
| emotion_counts.columns = ["Emotion", "Count"] | |
| fig2 = px.pie( | |
| emotion_counts, values="Count", names="Emotion", title="Your Most Common Moods" | |
| ) | |
| fig2.update_layout( | |
| plot_bgcolor="rgba(0,0,0,0)", | |
| paper_bgcolor="rgba(0,0,0,0)", | |
| font=dict(family="Segoe UI, Arial, sans-serif"), | |
| ) | |
| st.plotly_chart(fig2, use_container_width=True) | |
| st.markdown("</div>", unsafe_allow_html=True) | |
| st.markdown('<div class="animated-fade">', unsafe_allow_html=True) | |
| st.subheader("Your Recent Mood Detections") | |
| recent_df = df.sort_values("timestamp", ascending=False).head(10) | |
| recent_df["Date"] = recent_df["timestamp"].dt.strftime("%Y-%m-%d") | |
| recent_df["Time"] = recent_df["timestamp"].dt.strftime("%H:%M:%S") | |
| recent_df = recent_df.rename( | |
| columns={ | |
| "emotion": "Mood", | |
| "confidence": "Confidence %", | |
| "source": "Detection Method", | |
| } | |
| ) | |
| st.dataframe( | |
| recent_df[["Date", "Time", "Mood", "Confidence %", "Detection Method"]], | |
| use_container_width=True, | |
| hide_index=True, | |
| ) | |
| st.markdown("</div>", unsafe_allow_html=True) | |
| def main(): | |
| load_css() | |
| if GPU_AVAILABLE: | |
| st.sidebar.markdown("---") | |
| st.sidebar.markdown('<div class="sidebar-header">🔥 GPU Acceleration Active</div>', unsafe_allow_html=True) | |
| st.sidebar.info(f"Using GPU for faster processing:\n{GPU_INFO}") | |
| menu = ["Home", "Music Recommendation", "Mood History"] | |
| with st.sidebar: | |
| st.markdown( | |
| '<div class="sidebar-header" style="text-align: center;">Menu</div>', unsafe_allow_html=True | |
| ) | |
| choice = st.selectbox("Choose a Page", menu) | |
| st.markdown("---") | |
| st.markdown( | |
| '<div class="sidebar-header">How It Works</div>', unsafe_allow_html=True | |
| ) | |
| st.markdown( | |
| """ | |
| <div class="step-container"> | |
| <div class="step-number">1</div> | |
| <div class="step-content">Take a photo or select your mood</div> | |
| </div> | |
| <div class="step-container"> | |
| <div class="step-number">2</div> | |
| <div class="step-content">Our AI analyzes your feelings</div> | |
| </div> | |
| <div class="step-container"> | |
| <div class="step-number">3</div> | |
| <div class="step-content">Get songs that match your mood</div> | |
| </div> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| st.markdown("---") | |
| st.markdown('<div class="sidebar-header">About</div>', unsafe_allow_html=True) | |
| st.write( | |
| "We use AI to understand your emotions and suggest music that fits your mood." | |
| ) | |
| if choice == "Home": | |
| st.markdown( | |
| '<h1 class="main-header">Music Recommendation System</h1>', | |
| unsafe_allow_html=True, | |
| ) | |
| st.markdown( | |
| """ | |
| <div class="info-box"> | |
| Welcome to your personal music recommendation system! This app uses AI to detect your mood and suggest songs that match how you feel right now. | |
| </div> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| st.markdown('<div class="animated-fade">', unsafe_allow_html=True) | |
| st.markdown('<p class="subheader">Features</p>', unsafe_allow_html=True) | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.markdown( | |
| """ | |
| <div class="feature-card"> | |
| <div class="feature-icon">🔍</div> | |
| <h3>Mood Detection</h3> | |
| <p>Our AI detects your emotions from a single photo.</p> | |
| </div> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| st.markdown( | |
| """ | |
| <div class="feature-card"> | |
| <div class="feature-icon">🎼</div> | |
| <h3>Music Recommendations</h3> | |
| <p>Get song recommendations based on your mood.</p> | |
| </div> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| with col2: | |
| st.markdown( | |
| """ | |
| <div class="feature-card"> | |
| <div class="feature-icon">😍</div> | |
| <h3>Save Favorites</h3> | |
| <p>Choose songs you love to create your collection.</p> | |
| </div> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| st.markdown( | |
| """ | |
| <div class="feature-card"> | |
| <div class="feature-icon">📊</div> | |
| <h3>Track Your Moods</h3> | |
| <p>Watch your feelings change over time with charts.</p> | |
| </div> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| st.markdown("### Available Moods") | |
| mood_col1, mood_col2 = st.columns(2) | |
| with mood_col1: | |
| st.markdown( | |
| """ | |
| - 😊 **Happy** - Cheerful songs to boost your mood. | |
| - 😢 **Sad** - Emotional songs for your deep moments. | |
| - 😠 **Angry** - Strong tracks to match your energy. | |
| """ | |
| ) | |
| with mood_col2: | |
| st.markdown( | |
| """ | |
| - 😐 **Neutral** - Calming songs for everyday listening. | |
| - 😲 **Surprise** - Tracks to spark your curiosity. | |
| - 🤢 **Disgust** - Cheerful songs to lift your mood. | |
| """ | |
| ) | |
| st.markdown("</div>", unsafe_allow_html=True) | |
| col1, col2 = st.columns([1, 1]) | |
| with col1: | |
| if st.button("🎼 Get Music Recommendations"): | |
| st.session_state.choice = "Music Recommendation" | |
| st.rerun() | |
| with col2: | |
| if st.button("📊 View Your Mood History"): | |
| st.session_state.choice = "Mood History" | |
| st.rerun() | |
| elif choice == "Music Recommendation": | |
| music_recommendation() | |
| elif choice == "Mood History": | |
| show_mood_history() | |
| if __name__ == "__main__": | |
| main() | |