Create app.py

first iteration

app.py

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+import gradio as gr
+import pytube
+from transformers import pipeline
+
+# Initialize pipelines
+asr = pipeline("automatic-speech-recognition", model="openai/whisper-base", chunk_length_s=30)
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+def summarize_youtube(url):
+    # Download audio
+    yt = pytube.YouTube(url)
+    stream = yt.streams.filter(only_audio=True).first()
+    stream.download(filename="audio.mp3")
+
+    # Transcribe
+    result = asr("audio.mp3")
+    transcript = result["text"]
+
+    # Summarize
+    summary = summarizer(transcript, max_length=150, min_length=50, do_sample=False)[0]["summary_text"]
+
+    # Embed video
+    v_id = url.split("v=")[-1]
+    embed_html = f'<iframe width="560" height="315" src="https://www.youtube.com/embed/{v_id}" frameborder="0" allowfullscreen></iframe>'
+    
+    return embed_html, transcript, summary
+
+# Build Gradio app
+with gr.Blocks() as demo:
+    gr.Markdown("## 🎓 Multi‑lingual YouTube Summarizer (Hindi / Hinglish / English)")
+    url_input = gr.Textbox(label="YouTube URL")
+    vid, txt, summ = gr.HTML(), gr.Textbox(label="Transcript"), gr.Textbox(label="Summary")
+    btn = gr.Button("Summarize")
+    btn.click(summarize_youtube, inputs=url_input, outputs=[vid, txt, summ])
+
+demo.launch()