Update app.py

app.py

@@ -13,25 +13,25 @@ TOKEN=os.getenv('HF_TOKEN')
 subprocess.run(["huggingface-cli", "login", "--token", TOKEN, "--add-to-git-credential"])
 st.sidebar.title("Welcome to MBAL Chatbot")
 class PDFChatbot:
-   def __init__(self):
+    def __init__(self):
        self.azure_client = openai.OpenAI()
        self.conversation_history = []
        self.pdf_content = ""
 
-   def get_relevant_context(self, user_question: str) -> List[str]:
+    def get_relevant_context(self, user_question: str) -> List[str]:
         """Split text into smaller chunks for better processing."""
         db = FAISS.load_local("mbal_faiss_db", embeddings=HuggingFaceEmbeddings(model_name='bkai-foundation-models/vietnamese-bi-encoder'), allow_dangerous_deserialization=True)
         relevant_chunks = db.similarity_search(user_question, k=3)
         relevant_chunks = [chunk.page_content for chunk in relevant_chunks]
         return "\n\n".join(relevant_chunks)
-   def chat_with_pdf(self, user_question: str, pdf_content: str) -> str:
+    def chat_with_pdf(self, user_question: str, pdf_content: str) -> str:
        """Generate response using Azure OpenAI based on PDF content and user question."""
-       try:
-           # Split PDF content into chunks
-           # Get relevant context for the question
-           relevant_context = self.get_relevant_context(user_question)
-           # Prepare messages for the chat
-           messages = [
+        try:
+            # Split PDF content into chunks
+            # Get relevant context for the question
+            relevant_context = self.get_relevant_context(user_question)
+            # Prepare messages for the chat
+            messages = [
                {
                    "role": "system",
                    "content": """You are an experienced insurance agent assistant who helps customers understand their insurance policies and coverage details. Follow these guidelines:
@@ -53,64 +53,64 @@ class PDFChatbot:
 {relevant_context}
 Customer Question: {user_question}
 Please provide a helpful response based on the insurance document content above."""
-               }
-           ]
+                }
+            ]
            # Add conversation history
-           for msg in self.conversation_history[-2:]:  # Keep last 6 messages for context
+            for msg in self.conversation_history[-2:]:  # Keep last 6 messages for context
                messages.append(msg)
-           # Get response from Azure OpenAI
-           response = self.azure_client.chat.completions.create(
+            # Get response from Azure OpenAI
+            response = self.azure_client.chat.completions.create(
                model="gpt-4o-mini",
                messages=messages,
                max_tokens=300, #TODO
                temperature=0.7
-           )
-           bot_response = response.choices[0].message.content
-           # Update conversation history
-           self.conversation_history.append({"role": "user", "content": user_question})
-           self.conversation_history.append({"role": "assistant", "content": bot_response})
-           return bot_response
-       except Exception as e:
-           return f"Error generating response: {str(e)}"
+            )
+            bot_response = response.choices[0].message.content
+            # Update conversation history
+            self.conversation_history.append({"role": "user", "content": user_question})
+            self.conversation_history.append({"role": "assistant", "content": bot_response})
+            return bot_response
+        except Exception as e:
+            return f"Error generating response: {str(e)}"
 def main():
-   # st.set_page_config(page_title="Insurance PDF Chatbot", page_icon="🛡️", layout="wide")
-   st.title("🛡️ Insurance Policy Assistant")
-   st.markdown("Upload your insurance policy PDF and ask questions about your coverage, claims, deductibles, and more!")
-   # Initialize chatbot
-   if 'chatbot' not in st.session_state:
-       st.session_state.chatbot = PDFChatbot()
-       st.session_state.pdf_processed = False
-       st.session_state.chat_history = []
-   # Sidebar for PDF upload and settings
+    # st.set_page_config(page_title="Insurance PDF Chatbot", page_icon="🛡️", layout="wide")
+    st.title("🛡️ Insurance Policy Assistant")
+    st.markdown("Upload your insurance policy PDF and ask questions about your coverage, claims, deductibles, and more!")
+    # Initialize chatbot
+    if 'chatbot' not in st.session_state:
+        st.session_state.chatbot = PDFChatbot()
+        st.session_state.pdf_processed = False
+        st.session_state.chat_history = []
+    # Sidebar for PDF upload and settings
    
-       # Clear conversation
-       if st.button("Xóa lịch sử"):
-           st.session_state.chatbot.conversation_history = []
-           st.session_state.chat_history = []
-           st.rerun()
+# Clear conversation
+        if st.button("Xóa lịch sử"):
+            st.session_state.chatbot.conversation_history = []
+            st.session_state.chat_history = []
+            st.rerun()
    # Main chat interface
-   if st.session_state.pdf_processed:
-       st.header("💬 Ask About Your Insurance Policy")
-       # Display chat history
-       for i, (question, answer) in enumerate(st.session_state.chat_history):
-           with st.container():
-               st.markdown(f"**You:** {question}")
-               st.markdown(f"**Insurance Assistant:** {answer}")
-               st.divider()
+    if st.session_state.pdf_processed:
+        st.header("💬 Ask About Your Insurance Policy")
+        # Display chat history
+        for i, (question, answer) in enumerate(st.session_state.chat_history):
+            with st.container():
+                st.markdown(f"**You:** {question}")
+                st.markdown(f"**Insurance Assistant:** {answer}")
+                st.divider()
        # Chat input
-       user_question = st.chat_input("Hãy đặt những câu hỏi về hợp đồng bảo hiểm cơ bản...")
-       if user_question:
-           with st.spinner("Analyzing your policy..."):
-               # Get response from chatbot
-               response = st.session_state.chatbot.chat_with_pdf(
+        user_question = st.chat_input("Hãy đặt những câu hỏi về hợp đồng bảo hiểm cơ bản...")
+        if user_question:
+            with st.spinner("Analyzing your policy..."):
+                # Get response from chatbot
+                response = st.session_state.chatbot.chat_with_pdf(
                    user_question,
                    st.session_state.chatbot.pdf_content
-               )
-               # Add to chat history
-               st.session_state.chat_history.append((user_question, response))
-               # Display the new response
-               st.markdown(f"**You:** {user_question}")
-               st.markdown(f"**Insurance Assistant:** {response}")
+                )
+                # Add to chat history
+                st.session_state.chat_history.append((user_question, response))
+                # Display the new response
+                st.markdown(f"**You:** {user_question}")
+                st.markdown(f"**Insurance Assistant:** {response}")
     else:
         # Show example questions
         st.subheader("Các câu hỏi bạn có thể hỏi:")
@@ -138,4 +138,4 @@ def main():
         """)
 
 if __name__ == "__main__":
-   main()
+    main()