Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+import fitz  # PyMuPDF
+
+# Load the QA model
+qa_model = pipeline("question-answering", "timpal0l/mdeberta-v3-base-squad2")
+
+# Function to extract text from a PDF file
+def extract_text_from_pdf(pdf_file):
+    doc = fitz.open(pdf_file)
+    text = ""
+    for page_num in range(doc.page_count):
+        page = doc[page_num]
+        text += page.get_text()
+    doc.close()
+    return text
+
+# Streamlit app
+def main():
+    st.title("PDF Question Answering App")
+
+    # Upload PDF file through Streamlit
+    uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
+
+    if uploaded_file is not None:
+        # Read the PDF file and extract text
+        pdf_text = extract_text_from_pdf(uploaded_file)
+
+        # Display the extracted text
+        st.subheader("Extracted Text from PDF")
+        st.text(pdf_text)
+
+        # Input for user question
+        question = st.text_input("Ask a question about the PDF:")
+
+        # Button to trigger question answering
+        if st.button("Get Answer"):
+            if question:
+                # Use the QA model to get the answer
+                answer = qa_model(question=question, context=pdf_text)
+                st.subheader("Answer:")
+                st.write(answer["answer"])
+            else:
+                st.warning("Please enter a question.")
+
+if __name__ == "__main__":
+    main()