app.py

# -*- coding: utf-8 -*-
"""Untitled44.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1PxYl919o_aJGWdZoMRTVFD4XESysiWAH
"""


import gradio as gr
import random
import time
from transformers import pipeline,AutoModelForQuestionAnswering,AutoTokenizer
import pdfplumber

def extract_text_from_pdf(pdf_path):
    text = ""
    with pdfplumber.open(pdf_path) as pdf:
        for page in pdf.pages:
            text += page.extract_text()
    return text

pdf_path = "Ketan gandhi-chatbotdata.pdf"
pdf_text = extract_text_from_pdf(pdf_path)
model_name="deepset/roberta-base-squad2"
qa_pipeline = pipeline("question-answering", model=AutoModelForQuestionAnswering.from_pretrained(model_name),tokenizer=AutoTokenizer.from_pretrained(model_name))

with gr.Blocks() as demo:
    chatbot = gr.Chatbot()
    msg = gr.Textbox(placeholder="Ask me anything related to Ketan Gandhi😎")
    clear = gr.ClearButton([msg, chatbot])
    def respond(message, chat_history):
        question = message
        answer = qa_pipeline(question=question, context=pdf_text)
        confidence_threshold=0.3
        if answer['score']>confidence_threshold:
          bot_message = answer['answer']
          chat_history.append((message, bot_message))
        else:
          chat_history.append((message,"I may not be the right bot to answer this......😅"))
        time.sleep(2)
        return "", chat_history
    def vote(data: gr.LikeData):
        if data.liked:
            print("You upvoted this response: " + data.value)
        else:
            print("You downvoted this response: " + data.value)

    msg.submit(respond, [msg, chatbot], [msg, chatbot])
    chatbot.like(vote, None, None)


if __name__ == "__main__":
    demo.launch()