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import torch
import gradio as gr
# Use a pipeline as a high-level helper
from transformers import pipeline
question_answer = pipeline("question-answering",
model="deepset/roberta-base-squad2")
# question_answer = pipeline("question-answering",
# model=model_path)
def read_file_content(file_obj):
"""
Reads the content of a file object and returns it.
Parameters:
file_obj (file object): The file object to read from.
Returns:
str: The content of the file.
"""
try:
with open(file_obj.name, 'r', encoding='utf-8') as file:
context = file.read()
return context
except Exception as e:
return f"An error occurred: {e}"
#
def get_answer(file, question):
context = read_file_content(file)
answer = question_answer(question=question, context=context)
return answer["answer"]
demo = gr.Interface(fn=get_answer,
inputs=[gr.File(label="Upload your file(.txt)"), gr.Textbox(label="Input your question",lines=1)],
outputs=[gr.Textbox(label="Answer text",lines=1)],
title="Document_Question_And_Answer Chatbot",
description="THIS APPLICATION WILL BE USED TO ANSER QUESTIONS BASED ON CONTEXT PROVIDED.")
demo.launch() |