app.py

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
from moviepy import editor as mp  # FIXED IMPORT
import time
import langdetect
import uuid


# Load LLM Model
model_path = "Qwen/Qwen2.5-7B-Instruct"
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.eval()

# Function to generate a unique filename
def generate_unique_filename(extension):
    return f"{uuid.uuid4()}{extension}"

# Function to download audio from a YouTube video
def download_youtube_audio(url):
    output_path = generate_unique_filename(".mp3")
    ydl_opts = {
        'format': 'bestaudio/best',
        'postprocessors': [{'key': 'FFmpegExtractAudio', 'preferredcodec': 'mp3'}],
        'outtmpl': output_path,
    }
    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
        ydl.download([url])
    return output_path if os.path.exists(output_path) else None

# Function to extract audio from a video file
def extract_audio(video_path):
    video = mp.VideoFileClip(video_path)
    audio_path = generate_unique_filename(".mp3")
    video.audio.write_audiofile(audio_path)
    return audio_path

# Function to transcribe audio using Whisper
def transcribe_audio(audio_path):
    model = whisper.load_model("base")
    result = model.transcribe(audio_path)
    return result["text"]

# Function to summarize text using LLM
def summarize_text(text):
    detected_language = langdetect.detect(text)
    prompt = f"Summarize the following text in 150-300 words in {detected_language}: {text[:300000]}..."
    response, _ = model.chat(tokenizer, prompt, history=[])
    return response

# Gradio function to process YouTube links
def process_youtube(url):
    if not url:
        return "Please enter a YouTube URL.", ""
    audio_path = download_youtube_audio(url)
    if not audio_path:
        return "Error downloading YouTube audio.", ""
    transcription = transcribe_audio(audio_path)
    summary = summarize_text(transcription)
    os.remove(audio_path)
    return transcription, summary

# Gradio function to process uploaded videos
def process_uploaded_video(video):
    if not video:
        return "No video uploaded.", ""
    audio_path = extract_audio(video)
    transcription = transcribe_audio(audio_path)
    summary = summarize_text(transcription)
    os.remove(audio_path)
    return transcription, summary

# Gradio UI
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🎥 Video Summarizer")
    
    with gr.Tabs():
        with gr.TabItem("📤 Upload Video"):
            video_input = gr.Video(label="Upload Video")
            video_button = gr.Button("Process Video")
        
        with gr.TabItem("🔗 YouTube Link"):
            url_input = gr.Textbox(label="Enter YouTube URL")
            url_button = gr.Button("Process URL")
    
    with gr.Row():
        transcription_output = gr.Textbox(label="📝 Transcription", lines=10)
        summary_output = gr.Textbox(label="📊 Summary", lines=10)
    
    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])

demo.launch()