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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()
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