Update app.py

app.py

@@ -17,16 +17,18 @@ if torch.cuda.is_available():
 
 # Function to extract text from PDF
 def extract_text_from_pdf(pdf_path):
-    pdf_Text = ""
+    pdf_text = ""
     with open(pdf_path, "rb") as file:
         reader = PdfReader(file)
         for page_num in range(len(reader.pages)):
             page = reader.pages[page_num]
             text = page.extract_text()
-            pdf_Text += text + "\n"
-    return pdf_Text
+            pdf_text += text + "\n"
+    return pdf_text
 
 # Streamlit app
+st.write("**Created by: Engr. Hamesh Raj** [LinkedIn](https://www.linkedin.com/in/datascientisthameshraj/)")
+
 st.title("📄 PDF Question Answering")
 
 # Sidebar for PDF upload
@@ -41,6 +43,7 @@ if uploaded_file is not None:
     # Input field for the user's question
     user_query = st.text_input("Enter your question:")
 
+    # Display the submit button below the input field
     if st.button("Submit") and user_query:
         # Format the input text
         input_text = f"{user_query}\n\n### Response:\n"
@@ -63,5 +66,20 @@ if uploaded_file is not None:
         answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
         # Display question and answer
-        st.write(f"**Q: {user_query}**")
-        st.write(f"**A: {answer.strip()}**")
+        st.write(f"**Q{len(st.session_state) + 1}: {user_query}**")
+        st.write(f"**A{len(st.session_state) + 1}: {answer.strip()}**")
+
+        # Store in session state for chat history
+        if "history" not in st.session_state:
+            st.session_state.history = []
+        
+        st.session_state.history.append({
+            "question": user_query,
+            "answer": answer.strip()
+        })
+
+# Display chat history
+if "history" in st.session_state:
+    for i, qa in enumerate(st.session_state.history):
+        st.write(f"**Q{i + 1}: {qa['question']}**")
+        st.write(f"**A{i + 1}: {qa['answer']}**")