app.py

import requests
import gradio as gr
import os
import torch
import json
import time
import tempfile
import shutil
import librosa
from transformers import AutoTokenizer, AutoModelForCausalLM

# Check if CUDA is available and set the device accordingly
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# API URLs and headers
AUDIO_API_URL = "https://api-inference.huggingface.co/models/MIT/ast-finetuned-audioset-10-10-0.4593"
LYRICS_API_URL = "https://api-inference.huggingface.co/models/gpt2-xl"
headers = {"Authorization": f"Bearer {os.environ.get('HF_TOKEN')}"}

def get_audio_duration(audio_path):
    """Get the duration of the audio file in seconds"""
    try:
        duration = librosa.get_duration(path=audio_path)
        return duration
    except Exception as e:
        print(f"Error getting audio duration: {e}")
        return None

def calculate_song_structure(duration):
    """Calculate song structure based on audio duration"""
    if duration is None:
        return {"verses": 2, "choruses": 1, "tokens": 200}  # Default structure
        
    # Basic rules for song structure:
    # - Short clips (< 30s): 1 verse, 1 chorus
    # - Medium clips (30s-2min): 2 verses, 1-2 choruses
    # - Longer clips (>2min): 3 verses, 2-3 choruses
    
    if duration < 30:
        return {
            "verses": 1,
            "choruses": 1,
            "tokens": 150
        }
    elif duration < 120:
        return {
            "verses": 2,
            "choruses": 2,
            "tokens": 200
        }
    else:
        return {
            "verses": 3,
            "choruses": 3,
            "tokens": 300
        }

def create_lyrics_prompt(classification_results, song_structure):
    """Create a prompt for lyrics generation based on classification results and desired structure"""
    # Get the top genre and its characteristics
    top_result = classification_results[0]
    genre = top_result['label']
    confidence = float(top_result['score'].strip('%')) / 100
    
    # Get additional musical elements
    additional_elements = [r['label'] for r in classification_results[1:3]]
    
    # Create a structured prompt based on song length
    prompt = f"""Write song lyrics in the style of {genre}.
Theme: A {genre} song with elements of {' and '.join(additional_elements)}
Structure: {song_structure['verses']} verses and {song_structure['choruses']} choruses

Format the lyrics with [Verse 1], [Chorus], [Verse 2], etc.
Make each verse 4-6 lines and chorus 4 lines.

[Verse 1]"""
    return prompt

def format_lyrics(generated_text, song_structure):
    """Format the generated lyrics according to desired structure"""
    lines = generated_text.split('\n')
    cleaned_lines = []
    current_section = "[Verse 1]"
    verse_count = 0
    chorus_count = 0
    
    for line in lines:
        line = line.strip()
        if not line or line.startswith('###') or line.startswith('```'):
            continue
            
        # Handle section markers
        if line.lower().startswith('[verse'):
            if verse_count < song_structure['verses']:
                verse_count += 1
                current_section = f"[Verse {verse_count}]"
                cleaned_lines.append(f"\n{current_section}")
                continue
        elif line.lower().startswith('[chorus'):
            if chorus_count < song_structure['choruses']:
                chorus_count += 1
                current_section = f"[Chorus {chorus_count}]"
                cleaned_lines.append(f"\n{current_section}")
                continue
        
        # Add the line if we haven't exceeded our structure limits
        if (current_section.startswith('[Verse') and verse_count <= song_structure['verses']) or \
           (current_section.startswith('[Chorus') and chorus_count <= song_structure['choruses']):
            cleaned_lines.append(line)
        
        # Add chorus after first verse if not present
        if len(cleaned_lines) == 5 and chorus_count == 0:  # After 4 lines of verse + section header
            chorus_count += 1
            cleaned_lines.append(f"\n[Chorus 1]")
    
    return "\n".join(cleaned_lines)

def generate_lyrics_with_retry(prompt, song_structure, max_retries=5, initial_wait=2):
    """Generate lyrics using GPT2-XL with retry logic"""
    wait_time = initial_wait
    
    for attempt in range(max_retries):
        try:
            response = requests.post(
                LYRICS_API_URL,
                headers=headers,
                json={
                    "inputs": prompt,
                    "parameters": {
                        "max_new_tokens": song_structure['tokens'],
                        "temperature": 0.9,
                        "top_p": 0.95,
                        "do_sample": True,
                        "return_full_text": False,
                        "stop": ["[End]", "\n\n\n"]
                    }
                }
            )
            
            print(f"Response status: {response.status_code}")
            print(f"Response content: {response.content.decode('utf-8', errors='ignore')}")
            
            if response.status_code == 200:
                result = response.json()
                if isinstance(result, list) and len(result) > 0:
                    generated_text = result[0].get("generated_text", "")
                    return format_lyrics(generated_text, song_structure)
                return "Error: No text generated"
            elif response.status_code == 503:
                print(f"Model loading, attempt {attempt + 1}/{max_retries}. Waiting {wait_time} seconds...")
                time.sleep(wait_time)
                wait_time *= 1.5  # Increase wait time for next attempt
                continue
            else:
                return f"Error generating lyrics: {response.text}"
                
        except Exception as e:
            if attempt == max_retries - 1:  # Last attempt
                return f"Error after {max_retries} attempts: {str(e)}"
            time.sleep(wait_time)
            wait_time *= 1.5
    
    return "Failed to generate lyrics after multiple attempts. Please try again."

def format_results(classification_results, lyrics, prompt):
    """Format the results for display"""
    # Format classification results
    classification_text = "Classification Results:\n"
    for i, result in enumerate(classification_results):
        classification_text += f"{i+1}. {result['label']}: {result['score']}\n"
    
    # Format final output
    output = f"""
{classification_text}
\n---Generated Lyrics---\n
{lyrics}
"""
    return output

def classify_and_generate(audio_file):
    """
    Classify the audio and generate matching lyrics
    """
    if audio_file is None:
        return "Please upload an audio file."
    
    try:
        token = os.environ.get('HF_TOKEN')
        if not token:
            return "Error: HF_TOKEN environment variable is not set. Please set your Hugging Face API token."
        
        # Get audio duration and calculate structure
        if isinstance(audio_file, tuple):
            audio_path = audio_file[0]
        else:
            audio_path = audio_file
            
        duration = get_audio_duration(audio_path)
        song_structure = calculate_song_structure(duration)
        print(f"Audio duration: {duration:.2f}s, Structure: {song_structure}")
        
        # Create a temporary file to handle the audio data
        with tempfile.NamedTemporaryFile(delete=False, suffix='.mp3') as temp_audio:
            # Copy the audio file to our temporary file
            shutil.copy2(audio_path, temp_audio.name)
            
            # Read the temporary file
            with open(temp_audio.name, "rb") as f:
                data = f.read()
        
        print("Sending request to Audio Classification API...")
        response = requests.post(AUDIO_API_URL, headers=headers, data=data)
        
        # Clean up the temporary file
        try:
            os.unlink(temp_audio.name)
        except:
            pass
        
        if response.status_code == 200:
            classification_results = response.json()
            # Format classification results
            formatted_results = []
            for result in classification_results:
                formatted_results.append({
                    'label': result['label'],
                    'score': f"{result['score']*100:.2f}%"
                })
            
            # Generate lyrics based on classification with retry logic
            print("Generating lyrics based on classification...")
            prompt = create_lyrics_prompt(formatted_results, song_structure)
            lyrics = generate_lyrics_with_retry(prompt, song_structure)
            
            # Format and return results
            return format_results(formatted_results, lyrics, prompt)
            
        elif response.status_code == 401:
            return "Error: Invalid or missing API token. Please check your Hugging Face API token."
        elif response.status_code == 503:
            return "Error: Model is loading. Please try again in a few seconds."
        else:
            return f"Error: API returned status code {response.status_code}\nResponse: {response.text}"
            
    except Exception as e:
        import traceback
        error_details = traceback.format_exc()
        return f"Error processing request: {str(e)}\nDetails:\n{error_details}"

# Create Gradio interface
iface = gr.Interface(
    fn=classify_and_generate,
    inputs=gr.Audio(type="filepath", label="Upload Audio File"),
    outputs=gr.Textbox(
        label="Results",
        lines=15,
        placeholder="Upload an audio file to see classification results and generated lyrics..."
    ),
    title="Music Genre Classifier + Lyric Generator",
    description="Upload an audio file to classify its genre and generate matching lyrics using AI.",
    examples=[],
)

# Launch the interface
if __name__ == "__main__":
    iface.launch(server_name="0.0.0.0", server_port=7860)