Update app.py

app.py

@@ -1,185 +1,77 @@
 import streamlit as st
-from transformers import pipeline
 import google.generativeai as genai
 from pytube import Search
-import speech_recognition as sr
-import tempfile
-from audio_recorder_streamlit import audio_recorder
-import numpy as np
-import wave
-import io
-import logging
 
-# Set up logging
-logging.basicConfig(level=logging.DEBUG)
-
-# Load sentiment analysis model using PyTorch backend
-mood_classifier = pipeline("sentiment-analysis", framework="pt")
+# Streamlit App
+st.title("🎶 Mood-Based Indian Song Player")
 
-def convert_audio_to_wav(audio_bytes):
-    # Create a wave file in memory
-    wav_buffer = io.BytesIO()
-    
-    with wave.open(wav_buffer, 'wb') as wav_file:
-        wav_file.setnchannels(1)  # Mono
-        wav_file.setsampwidth(2)  # 2 bytes per sample
-        wav_file.setframerate(44100)  # Sample rate
-        wav_file.writeframes(audio_bytes)
-    
-    return wav_buffer.getvalue()
+# Sidebar for user input
+st.sidebar.header("Enter Your Input")
+input_method = "Text"  # Only text input is available
 
-def detect_mood(text):
-    result = mood_classifier(text)[0]
-    if result['label'] == 'POSITIVE':
-        return "happy"
-    elif result['label'] == 'NEGATIVE':
-        return "sad"
-    else:
-        return "calm"
+# Input for Gemini API key
+gemini_api_key = st.sidebar.text_input("Enter your Gemini API Key:", type="password")
 
-def speech_to_text():
-    # Initialize recognizer
-    r = sr.Recognizer()
-    
-    # Add audio recording widget
-    audio_bytes = audio_recorder(
-        text="Click to record your mood",
-        recording_color="#e8b62c",
-        neutral_color="#6aa36f",
-        pause_threshold=2.0  # Automatically stop after 2 seconds of silence
-    )
-    
-    if audio_bytes:
-        try:
-            # Create a temporary WAV file
-            with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_audio:
-                # Convert and write audio bytes to WAV format
-                wav_bytes = convert_audio_to_wav(audio_bytes)
-                temp_audio.write(wav_bytes)
-                temp_audio.flush()
-                
-                # Use the temporary file for speech recognition
-                with sr.AudioFile(temp_audio.name) as source:
-                    # Record the audio file
-                    audio = r.record(source)
-                    
-                    try:
-                        # Attempt speech recognition
-                        text = r.recognize_google(audio)
-                        st.success("Speech recognized successfully!")
-                        return text
-                    except sr.UnknownValueError:
-                        st.error("Could not understand the audio. Please try speaking clearly and try again.")
-                        return None
-                    except sr.RequestError as e:
-                        st.error(f"Could not request results from speech recognition service; {e}")
-                        return None
-        except Exception as e:
-            st.error(f"Error processing audio: {e}")
-            return None
-    return None
+# Text input for user's mood or context
+user_input = st.text_input("Enter your mood or context:")
 
-def get_song_recommendations(mood, api_key):
+# Function to get song recommendations using Gemini API
+def get_song_recommendations(user_input, api_key):
     try:
         genai.configure(api_key=api_key)
         model = genai.GenerativeModel('gemini-pro')
-        # System prompt to guide the AI
-        system_prompt = """
-        You are a music recommendation assistant specialized in Indian songs. Your task is to suggest popular Indian songs based on the user's mood.
-        - If the mood is "happy," suggest upbeat and joyful Bollywood or Indian pop songs.
-        - If the mood is "sad," suggest emotional and soulful Indian songs.
-        - If the mood is "energetic," suggest high-energy dance or workout songs.
-        - If the mood is "romantic," suggest romantic Bollywood or Indian love songs.
-        - If the mood is "calm," suggest soothing Indian classical or instrumental music.
-        Always suggest 3 songs and provide the song title and artist name in the format:
+        # Prompt to guide the AI
+        prompt = f"""
+        Based on the input: '{user_input}', determine the mood or context and suggest 3 popular Indian songs that match this mood or context.
+        Output the mood followed by the song recommendations in the following format:
+        Mood: [mood]
         1. Song Title - Artist Name
         2. Song Title - Artist Name
         3. Song Title - Artist Name
         """
-        
-        # User prompt
-        user_prompt = f"Suggest 3 popular Indian {mood} songs."
-        
-        # Combine system and user prompts
-        response = model.generate_content([system_prompt, user_prompt])
+        response = model.generate_content(prompt)
         return response.text
     except Exception as e:
         st.error(f"Error using Gemini API: {e}")
         return None
 
+# Function to search YouTube for a song
 def search_youtube(query):
     search = Search(query)
-    return search.results[0].watch_url
-
-# Streamlit App
-st.title("🎵 Mood-Based Indian Song Player")
-
-# Sidebar for user input
-st.sidebar.header("How are you feeling today?")
-mood_options = ["happy", "sad", "energetic", "romantic", "calm"]
-
-# Input for Gemini API key
-gemini_api_key = st.sidebar.text_input("Enter your Gemini API Key:", type="password")
-
-# Add option to choose between text and speech input
-input_method = st.sidebar.radio("Choose input method:", ["Text", "Speech"])
-
-# Initialize user_mood as None
-user_mood = None
-user_text = None
-
-if input_method == "Text":
-    # Text input
-    user_text = st.sidebar.text_input("Enter text describing your mood:")
-    if st.sidebar.button("Submit"):
-        if user_text:
-            user_mood = detect_mood(user_text)
-            st.write(f"Based on your text, I detect your mood as: **{user_mood}**")
-        else:
-            st.sidebar.warning("Please enter your mood description.")
-            user_mood = None
-else:
-    # Speech input
-    st.write("📢 Click the button below and tell me about your day...")
-    user_text = speech_to_text()
-    
-    if user_text:
-        st.info(f"You said: '{user_text}'")
-        user_mood = detect_mood(user_text)
-        st.write(f"Based on what you said, I detect your mood as: **{user_mood}**")
-
-# Playlist
-if 'playlist' not in st.session_state:
-    st.session_state.playlist = []
+    if search.results:
+        return search.results[0].watch_url
+    else:
+        return None
 
 # Main content
-if user_mood and gemini_api_key:
-    st.write(f"🎭 Detected Mood: **{user_mood}**")
-    
-    st.write("🎵 Recommended Songs:")
-    recommendations = get_song_recommendations(user_mood, gemini_api_key)
-    if recommendations:
-        st.write(recommendations)
-        
-        song_names = recommendations.split("\n")
-        for song in song_names:
-            if song.strip():
-                st.write(f"🔍 Searching for: **{song}**")
-                video_url = search_youtube(song)
-                st.video(video_url)
-                
-                # Add to playlist button
-                if st.button(f"Add '{song}' to Playlist"):
-                    st.session_state.playlist.append((song, video_url))
-                    st.success(f"Added '{song}' to your playlist!")
+if user_input and gemini_api_key:
+    # Get combined mood detection and song recommendations from Gemini API
+    response = get_song_recommendations(user_input, gemini_api_key)
+    if response:
+        # Parse mood and song recommendations
+        lines = response.split('\n')
+        if len(lines) > 1 and lines[0].startswith("Mood:"):
+            mood = lines[0].replace("Mood: ", "")
+            st.write(f"Detected Mood/Context: {mood}")
+            songs = [line.strip() for line in lines[1:4] if line.strip()]
+            st.write("🎵 Recommended Songs:")
+            for song in songs:
+                st.write(song)
+                # Search YouTube and provide playback options
+                query = f"{song} song"
+                video_url = search_youtube(query)
+                if video_url:
+                    st.video(video_url)
+                    if st.button(f"Add '{song}' to Playlist"):
+                        # Add to playlist logic here
+                        st.success(f"Added '{song}' to your playlist!")
+                else:
+                    st.warning(f"Could not find '{song}' on YouTube.")
+        else:
+            st.error("Gemini API response format is incorrect.")
     else:
-        st.error("Failed to get song recommendations. Please check your API key.")
+        st.warning("Please provide input to get song recommendations.")
+elif not user_input:
+    st.warning("Please enter your mood or context.")
 elif not gemini_api_key:
-    st.warning("Please enter your Gemini API key to get started.")
-
-# Display the playlist
-st.sidebar.header("Your Playlist")
-for idx, (song, url) in enumerate(st.session_state.playlist):
-    st.sidebar.write(f"{idx + 1}. {song}")
-    if st.sidebar.button(f"Play {song}", key=f"play_{idx}"):
-        st.video(url)
+    st.warning("Please enter your Gemini API key to get started.")