Upload creating_a_demo.py

creating_a_demo.py

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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()