import gradio as gr
from audio_processing import process_audio, load_models
from transformers import pipeline
import spaces
import torch
import logging
import traceback
import sys

# Set up logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.StreamHandler(sys.stdout),
        logging.FileHandler('app.log')
    ]
)
logger = logging.getLogger(__name__)

# Check if CUDA is available
cuda_available = torch.cuda.is_available()
device = "cuda" if cuda_available else "cpu"
logger.info(f"Using device: {device}")

# Load Whisper model
print("Loading Whisper model...")
try:
    load_models()  # Load Whisper model
except Exception as e:
    logger.error(f"Error loading Whisper model: {str(e)}")
    raise

print("Whisper model loaded successfully.")

def load_summarization_model():
    logger.info("Loading summarization model...")
    try:
        summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6", device=0 if cuda_available else -1)
    except Exception as e:
        logger.warning(f"Failed to load summarization model on GPU. Falling back to CPU. Error: {str(e)}")
        summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6", device=-1)
    logger.info("Summarization model loaded.")
    return summarizer

def process_with_fallback(func, *args, **kwargs):
    try:
        return func(*args, **kwargs)
    except Exception as e:
        logger.error(f"Error during processing: {str(e)}")
        logger.error(traceback.format_exc())
        if "CUDA" in str(e) or "GPU" in str(e):
            logger.info("Falling back to CPU processing...")
            # Modify kwargs to force CPU processing
            kwargs['use_gpu'] = False
            return func(*args, **kwargs)
        else:
            raise

@spaces.GPU(duration=600)
def transcribe_audio(audio_file, translate, model_size, use_diarization):
    return process_with_fallback(process_audio, audio_file, translate=translate, model_size=model_size, use_diarization=use_diarization)

@spaces.GPU(duration=600)
def summarize_text(text):
    summarizer = load_summarization_model()
    try:
        summary = summarizer(text, max_length=150, min_length=50, do_sample=False)[0]['summary_text']
    except Exception as e:
        logger.error(f"Error during summarization: {str(e)}")
        logger.error(traceback.format_exc())
        summary = "Error occurred during summarization. Please try again."
    return summary

@spaces.GPU(duration=600)
def process_and_summarize(audio_file, translate, model_size, use_diarization, do_summarize):
    transcription, full_text = transcribe_audio(audio_file, translate, model_size, use_diarization)
    summary = summarize_text(full_text) if do_summarize else ""
    return transcription, summary

# Main interface
with gr.Blocks() as iface:
    gr.Markdown("# WhisperX Audio Transcription, Translation, and Summarization (with ZeroGPU support)")
    
    audio_input = gr.Audio(type="filepath")
    translate_checkbox = gr.Checkbox(label="Enable Translation")
    summarize_checkbox = gr.Checkbox(label="Enable Summarization", interactive=False)
    model_dropdown = gr.Dropdown(choices=["tiny", "base", "small", "medium", "large", "large-v2", "large-v3"], label="Whisper Model Size", value="small")
    diarization_checkbox = gr.Checkbox(label="Enable Speaker Diarization")
    process_button = gr.Button("Process Audio")
    transcription_output = gr.Textbox(label="Transcription/Translation")
    summary_output = gr.Textbox(label="Summary")

    def update_summarize_checkbox(translate):
        return gr.Checkbox(interactive=translate)

    translate_checkbox.change(update_summarize_checkbox, inputs=[translate_checkbox], outputs=[summarize_checkbox])
    
    process_button.click(
        process_and_summarize,
        inputs=[audio_input, translate_checkbox, model_dropdown, diarization_checkbox, summarize_checkbox],
        outputs=[transcription_output, summary_output]
    )

    gr.Markdown(
        f"""
        ## System Information
        - Device: {device}
        - CUDA Available: {"Yes" if cuda_available else "No"}
        
        ## ZeroGPU Support
        This application supports ZeroGPU for Hugging Face Spaces pro users. 
        GPU-intensive tasks are automatically optimized for better performance when available.
        """
    )

iface.launch()