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-medium"
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 genres and characteristics
    main_style = classification_results[0]['label']
    secondary_elements = [result['label'] for result in classification_results[1:3]]
    
    # Create a more specific prompt with example lyrics
    prompt = f"""Create song lyrics in {main_style} style with {', '.join(secondary_elements)} elements.

Here's an example of the lyric style:

[Verse 1]
Gentle bells ring in the night
Stars are shining pure and bright
Music fills the evening air
Magic moments we can share

Write new original lyrics following this style:

[Verse 1]"""
    return prompt

def format_lyrics(generated_text, song_structure):
    """Format the generated lyrics according to desired structure"""
    lines = []
    current_section = None
    verse_count = 0
    chorus_count = 0
    section_lines = []
    
    # Process the generated text line by line
    for line in generated_text.split('\n'):
        line = line.strip()
        
        # Skip empty lines and code blocks
        if not line or line.startswith('```') or line.startswith('###'):
            continue
            
        # Handle section markers
        if '[verse' in line.lower() or '[chorus' in line.lower():
            # Save previous section if exists
            if section_lines:
                while len(section_lines) < 4:  # Ensure 4 lines per section
                    section_lines.append("...")
                lines.extend(section_lines[:4])  # Only take first 4 lines if more
                section_lines = []
            
            # Add appropriate section marker
            if '[verse' in line.lower() and verse_count < song_structure['verses']:
                verse_count += 1
                lines.append(f"\n[Verse {verse_count}]")
                current_section = 'verse'
            elif '[chorus' in line.lower() and chorus_count < song_structure['choruses']:
                chorus_count += 1
                lines.append(f"\n[Chorus {chorus_count}]")
                current_section = 'chorus'
        else:
            # Add line to current section
            section_lines.append(line)
    
    # Handle the last section
    if section_lines:
        while len(section_lines) < 4:
            section_lines.append("...")
        lines.extend(section_lines[:4])
    
    # If we don't have enough sections, add them
    while verse_count < song_structure['verses'] or chorus_count < song_structure['choruses']:
        if verse_count < song_structure['verses']:
            verse_count += 1
            lines.append(f"\n[Verse {verse_count}]")
            lines.extend(["..." for _ in range(4)])
        if chorus_count < song_structure['choruses']:
            chorus_count += 1
            lines.append(f"\n[Chorus {chorus_count}]")
            lines.extend(["..." for _ in range(4)])
    
    return "\n".join(lines)

def generate_lyrics_with_retry(prompt, song_structure, max_retries=5, initial_wait=2):
    """Generate lyrics using GPT2 with retry logic"""
    wait_time = initial_wait
    
    for attempt in range(max_retries):
        try:
            print(f"\nAttempt {attempt + 1}: Generating lyrics...")
            
            response = requests.post(
                "https://api-inference.huggingface.co/models/distilgpt2",
                headers=headers,
                json={
                    "inputs": prompt,
                    "parameters": {
                        "max_new_tokens": 200,
                        "temperature": 0.7,  # Lower temperature for more coherent output
                        "top_p": 0.85,
                        "do_sample": True,
                        "return_full_text": False,
                        "num_return_sequences": 1,
                        "repetition_penalty": 1.2
                    }
                }
            )
            
            if response.status_code == 200:
                try:
                    result = response.json()
                    if isinstance(result, list) and len(result) > 0:
                        generated_text = result[0].get("generated_text", "")
                        if not generated_text:
                            continue
                        
                        # Clean up and format the text
                        lines = []
                        current_lines = []
                        
                        for line in generated_text.split('\n'):
                            line = line.strip()
                            # Skip empty lines, section markers, and non-lyric content
                            if not line or line.startswith(('```', '###', '[', 'Start', 'Write')):
                                continue
                            # Only include lines that look like lyrics (not too long, no punctuation at start)
                            if len(line.split()) <= 12 and not line[0] in '.,!?':
                                current_lines.append(line)
                        
                        if len(current_lines) >= 4:
                            # Format into song structure
                            lines.append("[Verse 1]")
                            lines.extend(current_lines[:4])
                            
                            if song_structure['choruses'] > 0:
                                chorus_lines = [
                                    "Hear the music in the air",
                                    "Feel the rhythm everywhere",
                                    "Let the melody take flight",
                                    "As we sing into the night"
                                ]
                                lines.append("\n[Chorus]")
                                lines.extend(chorus_lines)
                            
                            if song_structure['verses'] > 1 and len(current_lines) >= 8:
                                lines.append("\n[Verse 2]")
                                lines.extend(current_lines[4:8])
                            
                            return "\n".join(lines)
                        else:
                            print(f"Not enough valid lines generated (got {len(current_lines)}), retrying...")
                            
                except Exception as e:
                    print(f"Error processing response: {str(e)}")
                    if attempt < max_retries - 1:
                        continue
                    
            elif response.status_code == 503:
                print(f"Model loading, waiting {wait_time} seconds...")
                time.sleep(wait_time)
                wait_time *= 1.5
                continue
            else:
                print(f"Error response: {response.text}")
                if attempt < max_retries - 1:
                    continue
                return f"Error generating lyrics: {response.text}"
                
        except Exception as e:
            print(f"Exception during generation: {str(e)}")
            if attempt < max_retries - 1:
                time.sleep(wait_time)
                wait_time *= 1.5
                continue
            return f"Error after {max_retries} attempts: {str(e)}"
    
    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)