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import gradio as gr
from transformers import pipeline
import chardet
# Initialize the question-answering pipeline
#qa_pipeline = pipeline("question-answering",model="deepset/roberta-base-squad2")
qa_pipeline = pipeline("question-answering",model="distilbert-base-cased-distilled-squad")
def answer_question(context, question):
result = qa_pipeline(question=question, context=context)
return result['answer']
def process(context_file, question):
# Read the context from the uploaded file
#with open(context_file.name, 'r') as file:
#context = file.read()
with open(context_file.name, 'rb') as file:
raw_data = file.read()
result = chardet.detect(raw_data)
encoding = result['encoding']
# Fallback to a default encoding if detection fails
if encoding is None:
encoding = 'utf-8' # You can change this to another default encoding
context = raw_data.decode(encoding, errors='replace') # Replace errors with a placeholder
answer = answer_question(context, question)
return answer
# Gradio interface
demo = gr.Interface(
fn=process,
inputs=[gr.File(label="Upload Context File"), gr.Textbox(label="Question")],
outputs=[gr.Textbox(label="Answer")],
title="Question Answering",
description="Upload a file with context and ask a question. The answer will be displayed."
)
if __name__ == "__main__":
demo.launch()
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