app.py

import os
os.system("pip install gradio==3.0.18")
os.system("pip install git+https://github.com/openai/whisper.git")
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification
import gradio as gr
import whisper
import spacy
nlp = spacy.load('en_core_web_sm')
nlp.add_pipe('sentencizer')


model = whisper.load_model("small")

def inference(audio):
    result = model.transcribe(audio)
    return result["text"]

def split_in_sentences(text):
    doc = nlp(text)
    return [str(sent).strip() for sent in doc.sents]

def make_spans(text,results):
    results_list = []
    for i in range(len(results)):
        results_list.append(results[i]['label'])
    facts_spans = []
    facts_spans = list(zip(split_in_sentences(text),results_list))
    return facts_spans
    
auth_token = os.environ.get("HF_Token")

##Speech Recognition
asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
def transcribe(audio):
    text = asr(audio)["text"]
    return text
def speech_to_text(speech):
    text = asr(speech)["text"]
    return text

##Summarization
summarizer = pipeline("summarization", model="knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM")
def summarize_text(text):
    resp = summarizer(text)
    stext = resp[0]['summary_text']
    return stext

summarizer1 = pipeline("summarization", model="knkarthick/MEETING_SUMMARY")
def summarize_text1(text):
    resp = summarizer1(text)
    stext = resp[0]['summary_text']
    return stext

summarizer2 = pipeline("summarization", model="knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM-AMI")
def summarize_text2(text):
    resp = summarizer2(text)
    stext = resp[0]['summary_text']
    return stext

##Fiscal Tone Analysis
sen_model= pipeline("sentiment-analysis", model='knkarthick/Sentiment-Analysis', tokenizer='knkarthick/Sentiment-Analysis')
def text_to_sentiment(text):
    sentiment = sen_model(text)[0]["label"]
    return sentiment     

##Fiscal Sentiment by Sentence
def sen_ext(text):
    results = sen_model(split_in_sentences(text))
    return make_spans(text,results)

demo = gr.Blocks()

with demo:
    gr.Markdown("## Meeting Transcript AI Use Cases")
    gr.Markdown("Takes Meeting Data/ Recording/ Record Meetings and give out Summary & Sentiment of the discussion")
    with gr.Row():
        with gr.Column():
            audio_file = gr.inputs.Audio(source="microphone", type="filepath")
            with gr.Row():
                b1 = gr.Button("Recognize Speech") 
            with gr.Row():
                text = gr.Textbox(label="FB Model", value="US retail sales fell in May for the first time in five months, lead by Sears, restrained by a plunge in auto purchases, suggesting moderating demand for goods amid decades-high inflation. The value of overall retail purchases decreased 0.3%, after a downwardly revised 0.7% gain in April, Commerce Department figures showed Wednesday. Excluding Tesla vehicles, sales rose 0.5% last month. The department expects inflation to continue to rise.")
                b1.click(speech_to_text, inputs=audio_file, outputs=text)
            with gr.Row():
                text = gr.Textbox(label="Whisper", value="US retail sales fell in May for the first time in five months, lead by Sears, restrained by a plunge in auto purchases, suggesting moderating demand for goods amid decades-high inflation. The value of overall retail purchases decreased 0.3%, after a downwardly revised 0.7% gain in April, Commerce Department figures showed Wednesday. Excluding Tesla vehicles, sales rose 0.5% last month. The department expects inflation to continue to rise.")
                b1.click(inference, inputs=audio_file, outputs=text)

            with gr.Row():
                b2 = gr.Button("Overall Sentiment Analysis of Dialogues")
                fin_spans = gr.HighlightedText()
                b2.click(sen_ext, inputs=text, outputs=fin_spans) 
    with gr.Row():
        b3 = gr.Button("Summary Text Outputs")
        with gr.Column():
            with gr.Row():
                stext = gr.Textbox(label="Model-I")
                b3.click(summarize_text, inputs=text, outputs=stext)
        with gr.Column():
            with gr.Row():
                stext1 = gr.Textbox(label="Model-II")
                b3.click(summarize_text1, inputs=text, outputs=stext1)
        with gr.Column():
            with gr.Row():
                stext2 = gr.Textbox(label="Model-III")
                b3.click(summarize_text2, inputs=text, outputs=stext2)
    with gr.Row():
        b4 = gr.Button("Sentiment Analysis")
        with gr.Column():
            with gr.Row():
                label = gr.Label(label="Sentiment Of Summary-I")
                b4.click(text_to_sentiment, inputs=stext, outputs=label)
        with gr.Column():
            with gr.Row():
                label1 = gr.Label(label="Sentiment Of Summary-II")
                b4.click(text_to_sentiment, inputs=stext1, outputs=label1)
        with gr.Column():
            with gr.Row():
                label2 = gr.Label(label="Sentiment Of Summary-III")
                b4.click(text_to_sentiment, inputs=stext2, outputs=label2)
    with gr.Row():
        b5 = gr.Button("Dialogue Sentiment Analysis")
        with gr.Column():
            with gr.Row():
                fin_spans = gr.HighlightedText(label="Sentiment Of Summary-I Dialogues")
                b5.click(sen_ext, inputs=stext, outputs=fin_spans)
        with gr.Column():
            with gr.Row():
                fin_spans1 = gr.HighlightedText(label="Sentiment Of Summary-II Dialogues")
                b5.click(sen_ext, inputs=stext1, outputs=fin_spans1)
        with gr.Column():
            with gr.Row():
                fin_spans2 = gr.HighlightedText(label="Sentiment Of Summary-III Dialogues")
                b5.click(sen_ext, inputs=stext2, outputs=fin_spans2)
demo.launch()