Delete app.py

app.py

@@ -1,130 +0,0 @@
-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
-
-
-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()