import gradio as gr
import torch
import yt_dlp
import os
import subprocess
import json
from threading import Thread
from transformers import AutoTokenizer, AutoModelForCausalLM
import spaces
import time
import langdetect
import uuid

HF_TOKEN = os.environ.get("HF_TOKEN")
print("Starting the program...")

model_path = "Qwen/Qwen2.5-7B-Instruct"
print(f"Loading model {model_path}...")
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, trust_remote_code=True).cuda()
model = model.eval()
print("Model successfully loaded.")

def generate_unique_filename(extension):
    return f"{uuid.uuid4()}{extension}"

def cleanup_files(*files):
    for file in files:
        if file and os.path.exists(file):
            os.remove(file)
            print(f"Removed file: {file}")

def download_youtube_audio(url):
    print(f"Downloading audio from YouTube: {url}")
    output_path = generate_unique_filename(".wav")
    ydl_opts = {
        'format': 'bestaudio/best',
        'postprocessors': [{
            'key': 'FFmpegExtractAudio',
            'preferredcodec': 'wav',
        }],
        'outtmpl': output_path,
        'keepvideo': True,
    }
    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
        ydl.download([url])
    
    if os.path.exists(output_path + ".wav"):
        os.rename(output_path + ".wav", output_path)
    
    return output_path

@spaces.GPU(duration=90)
def transcribe_audio(file_path):
    print(f"Starting transcription of file: {file_path}")
    temp_audio = None
    if file_path.endswith(('.mp4', '.avi', '.mov', '.flv')):
        print("Video file detected. Extracting audio using ffmpeg...")
        temp_audio = generate_unique_filename(".wav")
        command = ["ffmpeg", "-i", file_path, "-q:a", "0", "-map", "a", temp_audio]
        subprocess.run(command, check=True)
        file_path = temp_audio
    
    output_file = generate_unique_filename(".json")
    command = [
        "insanely-fast-whisper",
        "--file-name", file_path,
        "--device-id", "0",
        "--model-name", "openai/whisper-large-v3",
        "--task", "transcribe",
        "--timestamp", "chunk",
        "--transcript-path", output_file
    ]
    subprocess.run(command, check=True)
    
    with open(output_file, "r") as f:
        transcription = json.load(f)
    
    result = transcription.get("text", " ".join([chunk["text"] for chunk in transcription.get("chunks", [])]))
    
    cleanup_files(output_file)
    if temp_audio:
        cleanup_files(temp_audio)
    
    return result

def generate_summary_stream(transcription):
    detected_language = langdetect.detect(transcription)
    prompt = f"""Summarize the following video transcription in 150-300 words in {detected_language}:
    {transcription[:300000]}..."""
    
    response, history = model.chat(tokenizer, prompt, history=[])
    return response

def process_youtube(url):
    if not url:
        return "Please enter a YouTube URL.", None
    audio_file = download_youtube_audio(url)
    transcription = transcribe_audio(audio_file)
    cleanup_files(audio_file)
    return transcription, None

def process_uploaded_video(video_path):
    transcription = transcribe_audio(video_path)
    return transcription, None

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # 🎥 Video Transcription and Smart Summary
    Upload a video or provide a YouTube link to get a transcription and AI-generated summary.
    """)
    
    with gr.Tabs():
        with gr.TabItem("📤 Video Upload"):
            video_input = gr.Video()
            video_button = gr.Button("🚀 Process Video")
        
        with gr.TabItem("🔗 YouTube Link"):
            url_input = gr.Textbox(placeholder="https://www.youtube.com/watch?v=...")
            url_button = gr.Button("🚀 Process URL")
    
    transcription_output = gr.Textbox(label="📝 Transcription", lines=10, show_copy_button=True)
    summary_output = gr.Textbox(label="📊 Summary", lines=10, show_copy_button=True)
    summary_button = gr.Button("📝 Generate Summary")
    
    video_button.click(process_uploaded_video, inputs=[video_input], outputs=[transcription_output, summary_output])
    url_button.click(process_youtube, inputs=[url_input], outputs=[transcription_output, summary_output])
    summary_button.click(generate_summary_stream, inputs=[transcription_output], outputs=[summary_output])

demo.launch()