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import gradio as gr |
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import requests |
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from transcribe import transcribe |
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from sentiment_analysis import sentiment_analyser |
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from summary import summarizer |
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from topic import topic_gen |
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from data import data |
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def main(audio_file, number_of_speakers): |
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text_data = transcribe(audio_file, number_of_speakers) |
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print(text_data) |
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topic = topic_gen(text_data)[0]["generated_text"] |
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summary = summarizer(text_data)[0]["summary_text"] |
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sent_analy = sentiment_analyser(text_data) |
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sent_analysis = sent_analy[0]["label"] + " (" + str(float(sent_analy[0]["score"]) * 100) + "%)" |
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return topic, summary, sent_analysis |
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with gr.Blocks() as demo: |
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gr.Markdown("# Shravan - Unlocking Value from Call Data") |
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with gr.Box(): |
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with gr.Row(): |
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with gr.Column(): |
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audio_file = gr.Audio(label="Upload an Audio file (.wav)", source="upload", type="filepath") |
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number_of_speakers = gr.Number(label="Number of Speakers", value=2) |
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with gr.Row(): |
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btn_clear = gr.ClearButton(value="Clear", components=[audio_file, number_of_speakers]) |
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btn_submit = gr.Button(value="Submit") |
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with gr.Column(): |
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topic = gr.Textbox(label="Title", placeholder="Title for Conversation") |
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summary = gr.Textbox(label="Short Summary", placeholder="Short Summary for Conversation") |
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sentiment_analysis = gr.Textbox(label="Sentiment Analysis", placeholder="Sentiment Analysis for Conversation") |
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btn_submit.click(fn=main, inputs=[audio_file, number_of_speakers], outputs=[topic, summary, sentiment_analysis]) |
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gr.Markdown("## Examples") |
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gr.Examples( |
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examples=[ |
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["./examples/sample4.wav", 2], |
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], |
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inputs=[audio_file, number_of_speakers], |
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outputs=[topic, summary, sentiment_analysis], |
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fn=main, |
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) |
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gr.Markdown( |
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""" |
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NOTE: The Tool takes around 5mins to run. So be patient! ;) |
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See [https://github.com/peb-peb/shravan](https://github.com/peb-peb/shravan) for more details. |
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""" |
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) |
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demo.launch() |
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