main

app.py

@@ -4,9 +4,19 @@ import io
 from gemini_kit import get_llm
 from langchain_core.messages import HumanMessage
 
+# Initialize session state for the PDF text and messages
+if 'pdf' not in st.session_state:
+    st.session_state.pdf = ""
+
+if 'messages' not in st.session_state:
+    st.session_state.messages = []
+if 'extract' not in st.session_state:
+    st.session_state.extract = True
 def upload_pdf():
+    print(st.session_state.extract)
+    print(st.session_state.pdf[:10])
     uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
-    if uploaded_file is not None:
+    if (uploaded_file is not None) & st.session_state.extract:
         st.write("Waiting for pdf to be extracted ...")
 
         pdf_reader = PdfReader(io.BytesIO(uploaded_file.read()))
@@ -14,58 +24,51 @@ def upload_pdf():
         for page_num in range(len(pdf_reader.pages)):
             page = pdf_reader.pages[page_num]
             text += page.extract_text()
-        return text
-    return None
-def chatbot_ui(knowledge):
-    if "messages" not in st.session_state:
-        st.session_state.messages = []
-
+        
+        # Store the extracted text in session state
+        st.session_state.pdf = text
+        st.session_state.extract = False
+        st.write("PDF Text Extracted. You can chat now!!")
+    if uploaded_file is None:
+        st.session_state.extract = True
+def chatbot_ui():
     user_input = st.text_input("You: ", "")
     if user_input:
         st.session_state.messages.append({"user": user_input})
-        # Here you can add your own logic to generate the response
-        if knowledge:
-            response = generate_response(knowledge,user_input)
+        if st.session_state.pdf:
+            response = generate_response(st.session_state.pdf, user_input)
         else:
             response = "Please upload a PDF to get started."
-        # print(response)
         st.session_state.messages.append({"Assistant": response})
-    chat = st.button("clear chat")
+
+    chat = st.button("Clear Chat")
     if chat:
         st.session_state.messages = []
-        st.session_state.chat = []
+
     for message in st.session_state.messages:
         if "user" in message:
             st.markdown(f"**You:** {message['user']}")
         else:
             st.markdown(f"**Assistant:** {message['Assistant']}")
 
-def generate_response(knowledge,user_input):
-    # Placeholder for response generation logic
-    message = f"This is the text extracted from the pdf: {knowledge}. The user query is {user_input}."
-    # print(message)
+def generate_response(pdf, user_input):
+    message = f"This is the text extracted from the pdf: {pdf}. The user query is {user_input}."
     llm = get_llm()
-    
     try:
         response = llm.invoke(message).content
     except Exception as e:
-        response ="Error occurred. This might due to exhaustion of LLM quota. or your pdf might be much bigger. The exact error: "+str(e)
-    print(response)
+        response = "Error occurred. This might be due to exhaustion of LLM quota or your PDF might be much bigger. The exact error: " + str(e)
     return response
 
 def main():
     st.title("NCERT PDF Based AI Assistant")
     
-    # PDF Upload Section
     st.header("Upload a PDF")
-    knowledge = upload_pdf()
-    if knowledge:
-        st.write("PDF Text Extracted. you can chat now!!")
-        # st.write(pdf_text)
+    # Call upload_pdf() only when the file uploader is interacted with
+    upload_pdf()
 
-    # Chatbot Section
     st.header("Chatbot")
-    chatbot_ui(knowledge)
+    chatbot_ui()
 
 if __name__ == "__main__":
     main()