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import streamlit as st
from transformers import pipeline
from langdetect import detect
import fitz # PyMuPDF
# Function to extract text from PDF
def extract_text_from_pdf(uploaded_file):
pdf_document = fitz.open(uploaded_file)
text = ""
for page_num in range(pdf_document.page_count):
page = pdf_document[page_num]
text += page.get_text()
return text
# Language Detection Function
def is_sindhi(text):
try:
language = detect(text)
return language == "sd" # Sindhi language code
except:
return False
# Streamlit UI
st.title("School Assistant - PDF Query and Language Detection")
# File Upload Section
uploaded_file = st.file_uploader("Upload a PDF", type=["pdf"])
# Question Input Section
question = st.text_input("Ask a question related to the PDF content:")
# Initialize Hugging Face QA pipeline
qa_pipeline = pipeline("question-answering")
if uploaded_file:
# Extract text from the uploaded PDF
pdf_text = extract_text_from_pdf(uploaded_file)
# Check if the extracted text is in Sindhi
if is_sindhi(pdf_text):
st.write("The document appears to be in Sindhi.")
else:
st.write("The document is not in Sindhi.")
# Show the extracted text preview
st.text_area("Extracted Text Preview", pdf_text[:1000], height=200)
if question:
# Query the model for an answer
answer = qa_pipeline(question=question, context=pdf_text)
st.write("Answer: ", answer['answer'])
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