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import gradio as gr
import re
import spacy
from transformers import pipeline
# Load spaCy's English model
nlp = spacy.load("en_core_web_sm")
def preprocess_text(text):
doc = nlp(text.lower()) # Tokenize and lowercase the text
tokens = [token.text for token in doc if not token.is_punct] # Remove punctuation
return tokens
# Load the multilingual model for question answering
qa_model = pipeline("question-answering", model="deepset/xlm-roberta-large-squad2")
# Function to generate the answer based on question and uploaded context
def answer_question(question, context):
try:
preprocessed_context = preprocess_text(context)
result = qa_model(question=question, context=" ".join(preprocessed_context))
return result['answer']
except Exception as e:
return f"Error: {str(e)}"
# Gradio interface
def qa_app(text_file, question):
try:
with open(text_file.name, 'r') as file:
context = file.read()
return answer_question(question, context)
except Exception as e:
return f"Error reading file: {str(e)}"
# Create Gradio interface with updated syntax
iface = gr.Interface(
fn=qa_app, # The function that processes input
inputs=[gr.File(label="Upload your text file"), gr.Textbox(label="Enter your question")],
outputs="text",
title="Multilingual Question Answering",
description="Upload a text file and ask a question based on its content."
)
# Launch the Gradio app
iface.launch()
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