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