Delete app.py

app.py

@@ -1,60 +0,0 @@
-import gradio as gr
-import whisper
-import yt_dlp
-import os
-from transformers import pipeline
-
-# Load Whisper model
-whisper_model = whisper.load_model("base")
-
-# Load Summarization pipeline (you can replace with a LLaMA fine-tuned model if you have one)
-summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
-
-def download_youtube_audio(youtube_url):
-    filename = "audio.mp3"
-    ydl_opts = {
-        'format': 'bestaudio/best',
-        'outtmpl': filename,
-        'quiet': True,
-        'postprocessors': [{
-            'key': 'FFmpegExtractAudio',
-            'preferredcodec': 'mp3',
-            'preferredquality': '192',
-        }],
-    }
-
-    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-        ydl.download([youtube_url])
-
-    return filename
-
-def transcribe_and_summarize(youtube_url):
-    try:
-        # Step 1: Download audio
-        audio_file = download_youtube_audio(youtube_url)
-
-        # Step 2: Transcribe
-        result = whisper_model.transcribe(audio_file)
-        transcript = result["text"]
-
-        # Step 3: Summarize
-        summary = summarizer(transcript, max_length=200, min_length=50, do_sample=False)[0]["summary_text"]
-
-        # Clean up
-        os.remove(audio_file)
-
-        return f"📄 **Transcript:**\n\n{transcript}\n\n🧠 **Summary:**\n\n{summary}"
-
-    except Exception as e:
-        return f"❌ Error: {str(e)}"
-
-# Gradio UI
-demo = gr.Interface(
-    fn=transcribe_and_summarize,
-    inputs=gr.Textbox(label="Enter YouTube Video URL"),
-    outputs=gr.Markdown(label="Transcript and Summary"),
-    title="🎥 YouTube Video Summarizer",
-    description="Enter a YouTube video link. This app will transcribe it and summarize using AI."
-)
-
-demo.launch()