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Update app.py
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app.py
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import gradio as gr
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from transformers import pipeline
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# ืืฆืืจืช
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transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base")
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summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
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def
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# ืืืืจืช ืืืฉืง Gradio
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interface = gr.Interface(
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fn=
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inputs=gr.Audio(type="filepath"), #
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outputs="text",
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title="ืืืืจ
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description="ืืขืื ืงืืืฅ ืืืืื ืฉื ืืจืฆื ืืงืื ืกืืืื ืงืฆืจ ืฉื ืืชืืื."
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)
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if __name__ == "__main__":
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import gradio as gr
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from transformers import pipeline
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from pydub import AudioSegment
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import os
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import tempfile
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# ืืฆืืจืช pipeline ืืชืืืื ืืืกืืืื
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transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base")
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summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
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def summarize_audio_or_video(file_path):
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try:
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# ืืืืงื ืื ืืงืืืฅ ืืื ืืืืื ืืืืจืช ืืืืื ืืืืืื ืืืืืช ืืฆืืจื
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if file_path.endswith((".mp4", ".mov", ".avi", ".mkv")):
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audio_file = convert_video_to_audio(file_path)
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else:
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audio_file = file_path
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# ืชืืืื ืืืืืื
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transcript = transcriber(audio_file)["text"]
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# ืืฆืืจืช ืกืืืื ืฉื ืืชืืืื
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summary = summarizer(transcript, max_length=50, min_length=25, do_sample=False)[0]["summary_text"]
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# ืืืืงืช ืงืืืฅ ืืืืืื ืืืืืช ืืฆืืจื (ืื ืืื ืืืืื)
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if audio_file != file_path:
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os.remove(audio_file)
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return summary
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except Exception as e:
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return f"ืฉืืืื ืืขืืืื ืืงืืืฅ: {str(e)}"
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def convert_video_to_audio(video_file):
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# ืืฆืืจืช ืงืืืฅ ืืืืื ืืื ื
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temp_audio = tempfile.mktemp(suffix=".wav")
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video = AudioSegment.from_file(video_file)
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video.export(temp_audio, format="wav")
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return temp_audio
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# ืืืืจืช ืืืฉืง Gradio
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interface = gr.Interface(
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fn=summarize_audio_or_video,
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inputs=gr.Audio(type="filepath"), # ืืชืืื ืืืืืื ืืืืืื
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outputs="text",
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title="ืืืืจ ืืืืื/ืืืืื ืืกืืืื",
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description="ืืขืื ืงืืืฅ ืืืืื ืื ืืืืื ืฉื ืืจืฆื ืืงืื ืกืืืื ืงืฆืจ ืฉื ืืชืืื."
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)
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if __name__ == "__main__":
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