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import gradio as gr |
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import os |
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from moviepy.editor import VideoFileClip |
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from transformers import pipeline |
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asr = pipeline(task="automatic-speech-recognition", model="distil-whisper/distil-small.en") |
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summarizer = pipeline("summarization", model="facebook/bart-large-cnn") |
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qa_pipeline = pipeline("question-answering", model="distilbert-base-cased-distilled-squad") |
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stored_transcript = "" |
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def transcribe_and_summarize(video_file): |
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global stored_transcript |
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if video_file is None: |
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return "Error: No file provided.", "" |
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try: |
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video = VideoFileClip(video_file) |
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audio_path = "temp_audio.wav" |
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video.audio.write_audiofile(audio_path, codec='pcm_s16le') |
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transcription_result = asr(audio_path, return_timestamps=True) |
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transcribed_text = " ".join([segment['text'] for segment in transcription_result['chunks']]) |
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stored_transcript = transcribed_text |
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if len(transcribed_text.split()) < 50: |
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summarized_text = "Text too short to summarize." |
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else: |
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summary_result = summarizer(transcribed_text, max_length=500, min_length=100, do_sample=False) |
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summarized_text = summary_result[0]['summary_text'] |
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return transcribed_text, summarized_text |
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except Exception as e: |
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return f"Error: {str(e)}", "" |
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def answer_question(question): |
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global stored_transcript |
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if not stored_transcript: |
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return "Please transcribe a video first." |
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result = qa_pipeline(question=question, context=stored_transcript) |
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return result['answer'] |
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with gr.Blocks(theme=gr.themes.Base()) as iface: |
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gr.Markdown("## 🎥 Video Transcriber, Summarizer & Q&A Tool") |
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gr.Markdown("Upload a video to get a transcript, summary, and ask questions about its content.") |
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with gr.Tab("1️⃣ Transcription & Summary"): |
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with gr.Row(): |
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video_input = gr.Video(label="📂 Upload Video (.mp4)", interactive=True) |
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with gr.Row(): |
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transcribe_btn = gr.Button("🚀 Transcribe and Summarize") |
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with gr.Row(): |
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transcribed_text = gr.Textbox(label="📝 Transcribed Text", lines=8, interactive=False) |
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summarized_text = gr.Textbox(label="📄 Summarized Text", lines=8, interactive=False) |
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transcribe_btn.click(fn=transcribe_and_summarize, inputs=video_input, outputs=[transcribed_text, summarized_text]) |
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with gr.Tab("2️⃣ Ask Questions"): |
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with gr.Row(): |
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question_input = gr.Textbox(label="❓ Ask a question based on the transcript", placeholder="E.g., What is the main topic?") |
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with gr.Row(): |
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ask_btn = gr.Button("🔍 Get Answer") |
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with gr.Row(): |
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answer_output = gr.Textbox(label="💬 Answer", interactive=False) |
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ask_btn.click(fn=answer_question, inputs=question_input, outputs=answer_output) |
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port = int(os.environ.get('PORT1', 7860)) |
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iface.launch(share=True, server_port=port) |
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