rm looping

app.py

@@ -2,15 +2,11 @@ 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
 
-# [Previous constants and LangChain setup remains the same...]
+# Mood assessment questions
 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?",
@@ -47,43 +43,26 @@ prompt = PromptTemplate(
 
 music_chain = RunnablePassthrough() | prompt | llm
 
-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
-    """
+def analyze_mood_and_generate_prompt(responses, preferences):
+    """Convert questionnaire responses and preferences into a music generation prompt using LangChain"""
     try:
-        # 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
-            
+        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:
-        raise Exception(f"Error in audio looping: {str(e)}")
+        return f"Error generating prompt: {str(e)}"
 
-def generate_music(prompt, duration=120):
-    """Generate music using the MusicGen API and loop it to the desired duration"""
+def generate_music(prompt, duration=10):
+    """Generate music using the MusicGen API"""
     API_URL = "https://api-inference.huggingface.co/models/facebook/musicgen-small"
     headers = {"Authorization": f"Bearer {os.getenv('HF_API_KEY')}"}
     
@@ -96,40 +75,14 @@ def generate_music(prompt, duration=120):
         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)
-            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)
+            tmp_file_path = tmp_file.name
             
-        return final_audio_path, "Music generated and looped successfully!"
+        return tmp_file_path, "Music generated 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"""
@@ -158,7 +111,7 @@ 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. Generated music will be 2 minutes long.
+    that helps you achieve your desired emotional state.
     """)
     
     with gr.Row():