loop music

app.py

@@ -2,11 +2,15 @@ import gradio as gr
 import requests
 import tempfile
 import os
+import numpy as np
+import soundfile as sf
+import librosa
 from langchain_openai import ChatOpenAI
 from langchain_core.runnables import RunnablePassthrough
 from langchain.prompts import PromptTemplate
+import math
 
-# Mood assessment questions
+# [Previous constants and LangChain setup remains the same...]
 MOOD_QUESTIONS = [
     "On a scale of 1-5, how would you rate your current energy level?",
     "On a scale of 1-5, how would you rate your current stress level?",
@@ -25,13 +29,11 @@ Based on the user's mood assessment:
 - Happiness level: {happiness}
 - Current emotions: {current_emotions}
 - Desired mood: {desired_mood}
-
 Musical preferences (if specified):
 - Preferred genre: {genre}
 - Preferred instruments: {instruments}
 - Preferred tempo: {tempo}
 - Preferred mood: {preferred_mood}
-
 Generate a detailed music prompt that would help the user transition from their current emotional state to their desired mood. 
 The prompt should incorporate the user's musical preferences if provided, while focusing on therapeutic musical qualities.
 Keep the description under 100 words and emphasize the healing aspects of the music.
@@ -45,26 +47,43 @@ prompt = PromptTemplate(
 
 music_chain = RunnablePassthrough() | prompt | llm
 
-def analyze_mood_and_generate_prompt(responses, preferences):
-    """Convert questionnaire responses and preferences into a music generation prompt using LangChain"""
+def loop_audio(input_path, target_duration=120):
+    """
+    Loop the input audio file to reach the target duration using librosa
+    
+    Args:
+        input_path (str): Path to the input audio file
+        target_duration (int): Desired duration in seconds
+    
+    Returns:
+        str: Path to the looped audio file
+    """
     try:
-        prompt_result = music_chain.invoke({
-            "energy": responses[0],
-            "stress": responses[1],
-            "happiness": responses[2],
-            "current_emotions": responses[3],
-            "desired_mood": responses[4],
-            "genre": preferences["genre"] or "any",
-            "instruments": preferences["instruments"] or "any",
-            "tempo": preferences["tempo"] or "any",
-            "preferred_mood": preferences["preferred_mood"] or "any"
-        })
-        return prompt_result.content
+        # Load the audio file
+        y, sr = librosa.load(input_path, sr=None)
+        
+        # Calculate current duration and required loops
+        current_duration = len(y) / sr
+        loops_needed = math.ceil(target_duration / current_duration)
+        
+        # Create the looped audio
+        y_looped = np.tile(y, loops_needed)
+        
+        # Trim to exact target duration if necessary
+        target_samples = int(target_duration * sr)
+        if len(y_looped) > target_samples:
+            y_looped = y_looped[:target_samples]
+        
+        # Save to a new temporary file
+        with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmp_file:
+            sf.write(tmp_file.name, y_looped, sr)
+            return tmp_file.name
+            
     except Exception as e:
-        return f"Error generating prompt: {str(e)}"
+        raise Exception(f"Error in audio looping: {str(e)}")
 
-def generate_music(prompt, duration=10):
-    """Generate music using the MusicGen API"""
+def generate_music(prompt, duration=120):
+    """Generate music using the MusicGen API and loop it to the desired duration"""
     API_URL = "https://api-inference.huggingface.co/models/facebook/musicgen-small"
     headers = {"Authorization": f"Bearer {os.getenv('HF_API_KEY')}"}
     
@@ -77,14 +96,40 @@ def generate_music(prompt, duration=10):
         if response.status_code != 200:
             return None, f"Error: API returned status code {response.status_code}"
             
+        # Save the initial generated audio
         with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmp_file:
             tmp_file.write(response.content)
-            tmp_file_path = tmp_file.name
+            initial_audio_path = tmp_file.name
+        
+        # Loop the audio to reach target duration
+        final_audio_path = loop_audio(initial_audio_path, duration)
+        
+        # Clean up the initial audio file
+        os.unlink(initial_audio_path)
             
-        return tmp_file_path, "Music generated successfully!"
+        return final_audio_path, "Music generated and looped successfully!"
     except Exception as e:
         return None, f"Error: {str(e)}"
 
+# [Rest of the code remains the same...]
+def analyze_mood_and_generate_prompt(responses, preferences):
+    """Convert questionnaire responses and preferences into a music generation prompt using LangChain"""
+    try:
+        prompt_result = music_chain.invoke({
+            "energy": responses[0],
+            "stress": responses[1],
+            "happiness": responses[2],
+            "current_emotions": responses[3],
+            "desired_mood": responses[4],
+            "genre": preferences["genre"] or "any",
+            "instruments": preferences["instruments"] or "any",
+            "tempo": preferences["tempo"] or "any",
+            "preferred_mood": preferences["preferred_mood"] or "any"
+        })
+        return prompt_result.content
+    except Exception as e:
+        return f"Error generating prompt: {str(e)}"
+
 def gradio_interface(energy, stress, happiness, current_emotions, desired_mood,
                     genre, instruments, tempo, preferred_mood):
     """Main interface function that processes questionnaire and generates music"""
@@ -113,7 +158,7 @@ with gr.Blocks() as demo:
     gr.Markdown("""
     # Therapeutic Music Generator
     Complete the mood assessment questionnaire and optionally specify your musical preferences to receive personalized music 
-    that helps you achieve your desired emotional state.
+    that helps you achieve your desired emotional state. Generated music will be 2 minutes long.
     """)
     
     with gr.Row():
@@ -195,5 +240,4 @@ with gr.Blocks() as demo:
     )
 
 if __name__ == "__main__":
-    demo.launch()
-    
+    demo.launch()