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import streamlit as st
import spacy
from transformers import pipeline
# Load spaCy's English model
nlp = spacy.load("en_core_web_sm")
# Basic preprocessing: lowercasing, removing special characters
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 pre-trained question-answering model
qa_model = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
# Function to answer question
def answer_question(question, context):
result = qa_model(question=question, context=context)
return result['answer']
# Streamlit App Layout
st.title("Question Answering App")
st.write("Upload a text file, ask a question, and get an answer from the text!")
# File uploader
uploaded_file = st.file_uploader("Upload a text file", type=["txt"])
if uploaded_file is not None:
# Read file
data = uploaded_file.read().decode('utf-8')
# Show the content of the file
st.write("### File Content")
st.write(data)
# Preprocess the text data
processed_data = preprocess_text(data)
# Ask question
question = st.text_input("Enter your question")
if st.button("Get Answer"):
if question:
# Get the answer from the QA model
answer = answer_question(question, data)
st.write(f"**Answer:** {answer}")
else:
st.write("Please enter a question.") |