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 structure and style guidance
    prompt = f"""Create {song_structure['verses']} verses and {song_structure['choruses']} choruses in {main_style} style with {', '.join(secondary_elements)} elements.


[Verse 1]"""

    return prompt

def format_lyrics(generated_text, song_structure):
    """Format the generated lyrics according to desired structure"""
    lines = []
    verse_count = 0
    chorus_count = 0
    current_section = []
    
    # Split text into lines and process
    text_lines = generated_text.split('\n')
    for line in text_lines:
        line = line.strip()
        
        # Skip empty lines and metadata
        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 it exists
            if current_section:
                # Pad section to 4 lines if needed
                while len(current_section) < 4:
                    current_section.append("...")
                lines.extend(current_section[:4])
                current_section = []
            
            # Add new section marker
            if '[verse' in line.lower() and verse_count < song_structure['verses']:
                verse_count += 1
                lines.append(f"\n[Verse {verse_count}]")
            elif '[chorus' in line.lower() and chorus_count < song_structure['choruses']:
                chorus_count += 1
                lines.append(f"\n[Chorus {chorus_count}]")
        else:
            # Add line to current section if it looks like lyrics
            if len(line.split()) <= 12 and not line[0] in '.,!?':
                current_section.append(line)
    
    # Handle last section
    if current_section:
        while len(current_section) < 4:
            current_section.append("...")
        lines.extend(current_section[:4])
    
    # Add any missing sections
    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 create_default_lyrics(song_structure):
    """Create default lyrics when generation fails"""
    lyrics = []
    
    # Add verses
    for i in range(song_structure['verses']):
        lyrics.append(f"\n[Verse {i+1}]")
        lyrics.extend([

        ])
    
    # Add choruses
    for i in range(song_structure['choruses']):
        lyrics.append(f"\n[Chorus {i+1}]")
        lyrics.extend([

        ])
    
    return "\n".join(lyrics)

def generate_lyrics_with_retry(prompt, song_structure, max_retries=5, initial_wait=2):
    """Generate lyrics using GPT2 with improved retry logic and error handling"""
    wait_time = initial_wait
    
    for attempt in range(max_retries):
        try:
            print(f"\nAttempt {attempt + 1}: Generating lyrics...")
            
            response = requests.post(
                LYRICS_API_URL,
                headers=headers,
                json={
                    "inputs": prompt,
                    "parameters": {
                        "max_new_tokens": song_structure['tokens'],
                        "temperature": 0.8,
                        "top_p": 0.9,
                        "do_sample": True,
                        "return_full_text": True,
                        "num_return_sequences": 1,
                        "repetition_penalty": 1.1
                    }
                }
            )
            
            if response.status_code == 200:
                result = response.json()
                
                # Handle different response formats
                if isinstance(result, list):
                    generated_text = result[0].get('generated_text', '')
                elif isinstance(result, dict):
                    generated_text = result.get('generated_text', '')
                else:
                    generated_text = str(result)
                
                if not generated_text:
                    print("Empty response received, retrying...")
                    time.sleep(wait_time)
                    continue
                
                # Process the generated text into verses and chorus
                formatted_lyrics = format_lyrics(generated_text, song_structure)
                
                # Verify we have enough content
                if formatted_lyrics.count('[Verse') >= song_structure['verses'] and \
                   formatted_lyrics.count('[Chorus') >= song_structure['choruses']:
                    return formatted_lyrics
                else:
                    print("Not enough sections generated, retrying...")
                    
            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:
                    time.sleep(wait_time)
                    continue
        
        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
            
        time.sleep(wait_time)
        wait_time = min(wait_time * 1.5, 10)  # Cap maximum wait time at 10 seconds
    
    # If we failed to generate after all retries, return a default structure
    return create_default_lyrics(song_structure)

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_with_retry(data, max_retries=5, initial_wait=2):
    """Classify audio with retry logic for 503 errors"""
    wait_time = initial_wait
    
    for attempt in range(max_retries):
        try:
            print(f"\nAttempt {attempt + 1}: Classifying audio...")
            response = requests.post(AUDIO_API_URL, headers=headers, data=data)
            
            if response.status_code == 200:
                return response.json()
            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:
                    time.sleep(wait_time)
                    continue
                return None
                
        except Exception as e:
            print(f"Exception during classification: {str(e)}")
            if attempt < max_retries - 1:
                time.sleep(wait_time)
                wait_time *= 1.5
                continue
            return None
            
        time.sleep(wait_time)
        wait_time = min(wait_time * 1.5, 10)
    
    return None

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...")
        classification_results = classify_with_retry(data)
        
        # Clean up the temporary file
        try:
            os.unlink(temp_audio.name)
        except:
            pass
        
        if classification_results is None:
            return "Error: Failed to classify audio after multiple retries. Please try again."
            
        # 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)
            
    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)