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| # -*- coding: utf-8 -*- | |
| # !pip install gradio --quiet | |
| # !pip install transformers --quiet | |
| """<h1> Sentiment Analysis""" | |
| def get_sentiment(text): | |
| return sentiment(text)[0]['label'], sentiment(text)[0]['score'] | |
| from transformers import pipeline | |
| import gradio as gr | |
| sentiment = pipeline("sentiment-analysis", model='distilbert/distilbert-base-uncased-finetuned-sst-2-english') | |
| sentiment_analysis = gr.Interface(fn=get_sentiment, | |
| inputs=gr.Textbox(lines=1, label="Enter the review:"), | |
| outputs=[gr.Text(label='Sentiment:'), | |
| gr.Text(label='Score:')]) | |
| """<h1> Summarization""" | |
| def get_summary(text): | |
| return summarizer(text, max_length=130, min_length=30, do_sample=False)[0]['summary_text'] | |
| summarizer = pipeline("summarization", model='sshleifer/distilbart-cnn-12-6') | |
| summarization = gr.Interface(fn=get_summary, | |
| inputs=gr.Textbox(lines=1, label="Enter the text:"), | |
| outputs=gr.Text(label="Summary:")) | |
| """<h1> Speech To Text""" | |
| def speechToText(audio): | |
| extractTtext = pipeline(model='openai/whisper-tiny') | |
| return extractTtext(audio)['text'] | |
| speech_to_text = gr.Interface(fn=speechToText, | |
| inputs=gr.Audio(sources="upload", type="filepath"), | |
| outputs="text") | |
| """<h1> ChatBot""" | |
| def get_chatbot(text): | |
| pipe = pipeline("text-generation", model="distilbert/distilgpt2") | |
| response = pipe(text) | |
| return response[0]['generated_text'] | |
| chatbot = gr.Interface(fn=get_chatbot, | |
| inputs=gr.Textbox(lines=1, label="Enter the text:"), | |
| outputs=gr.Text(label="Response:")) | |
| """<h1> Creating the Tabbed Interface""" | |
| demo = gr.TabbedInterface([sentiment_analysis, summarization, speech_to_text, chatbot], ["Sentiment Analysis", "Summarization", 'Speech to Text', 'Chat Bot']) | |
| if __name__ == "__main__": | |
| demo.launch() |