Create app.py

app.py

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+import gradio as gr
+import whisper
+from pytube import YouTube
+import os
+from transformers import pipeline
+
+# Load Whisper model
+model = whisper.load_model("base")
+
+# Load summarization model
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+def download_youtube_audio(url):
+    yt = YouTube(url)
+    audio = yt.streams.filter(only_audio=True).first()
+    out_file = audio.download(filename="temp_audio")
+    base, ext = os.path.splitext(out_file)
+    audio_file = base + '.mp3'
+    os.rename(out_file, audio_file)
+    return audio_file
+
+def transcribe_and_summarize(youtube_url=None, video_file=None):
+    try:
+        # Get audio file
+        if youtube_url:
+            audio_path = download_youtube_audio(youtube_url)
+        elif video_file:
+            audio_path = video_file
+        else:
+            return "Please provide a YouTube URL or upload a video file."
+        
+        # Transcribe
+        result = model.transcribe(audio_path)
+        transcription = result["text"]
+        
+        # Summarize (split into chunks if too long)
+        max_chunk = 1024
+        text_chunks = [transcription[i:i+max_chunk] for i in range(0, len(transcription), max_chunk)]
+        summaries = []
+        
+        for chunk in text_chunks[:3]:  # Limit to first 3 chunks
+            summary = summarizer(chunk, max_length=130, min_length=30, do_sample=False)
+            summaries.append(summary[0]['summary_text'])
+        
+        summary = " ".join(summaries)
+        
+        # Create downloadable text file
+        output_text = f"TRANSCRIPTION:\n{'='*50}\n\n{transcription}\n\n\nSUMMARY:\n{'='*50}\n\n{summary}"
+        
+        # Clean up
+        if youtube_url and os.path.exists(audio_path):
+            os.remove(audio_path)
+        
+        return transcription, summary, output_text
+        
+    except Exception as e:
+        return f"Error: {str(e)}", "", ""
+
+# Create Gradio interface
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🎥 Video Transcription & Summary Generator")
+    gr.Markdown("Upload a video or paste a YouTube link to get AI-powered transcription and summary")
+    
+    with gr.Tab("YouTube Link"):
+        youtube_input = gr.Textbox(label="YouTube URL", placeholder="https://www.youtube.com/watch?v=...")
+        youtube_btn = gr.Button("Process YouTube Video", variant="primary")
+    
+    with gr.Tab("Upload Video"):
+        video_input = gr.Video(label="Upload Video File")
+        upload_btn = gr.Button("Process Uploaded Video", variant="primary")
+    
+    with gr.Row():
+        with gr.Column():
+            transcription_output = gr.Textbox(label="Full Transcription", lines=10)
+        with gr.Column():
+            summary_output = gr.Textbox(label="AI Summary", lines=10)
+    
+    download_output = gr.File(label="Download Transcript")
+    
+    # Event handlers
+    youtube_btn.click(
+        fn=lambda url: transcribe_and_summarize(youtube_url=url),
+        inputs=youtube_input,
+        outputs=[transcription_output, summary_output, download_output]
+    )
+    
+    upload_btn.click(
+        fn=lambda video: transcribe_and_summarize(video_file=video),
+        inputs=video_input,
+        outputs=[transcription_output, summary_output, download_output]
+    )
+
+demo.launch()
+```
+
+**`requirements.txt`**:
+```
+gradio
+openai-whisper
+pytube
+transformers
+torch