Update app.py

app.py

@@ -1,6 +1,7 @@
 import streamlit as st
 from PIL import Image
 import time
+import streamlit_analytics
 from dotenv import load_dotenv
 import pickle
 from huggingface_hub import Repository
@@ -13,7 +14,6 @@ from langchain.llms import OpenAI
 from langchain.chains.question_answering import load_qa_chain
 from langchain.callbacks import get_openai_callback
 import os
-import traceback
 
 import pandas as pd
 import pydeck as pdk
@@ -26,6 +26,8 @@ if 'chat_history_page1' not in st.session_state:
 if 'chat_history_page2' not in st.session_state:
     st.session_state['chat_history_page2'] = []
 
+
+
 # Step 1: Clone the Dataset Repository
 repo = Repository(
     local_dir="Private_Book",  # Local directory to clone the repository
@@ -36,15 +38,17 @@ repo = Repository(
 repo.git_pull()  # Pull the latest changes (if any)
 
 # Step 2: Load the PDF File
-pdf_path = "Private_Book/grunddaten-krankenhaeuser-2016.pdf"  # Replace with your PDF file path
+pdf_path = "Private_Book/141123_Kombi.pdf"  # Replace with your PDF file path
 
 # Step 2: Load the PDF File
 pdf_path2 = "Private_Book/Deutsche_Kodierrichtlinien_23.pdf"  # Replace with your PDF file path
 
+
 api_key = os.getenv("OPENAI_API_KEY")
 # Retrieve the API key from st.secrets
 
 
+
 # Updated caching mechanism using st.cache_data
 @st.cache_data(persist="disk")  # Using persist="disk" to save cache across sessions
 def load_vector_store(file_path, store_name, force_reload=False):
@@ -70,12 +74,6 @@ def load_vector_store(file_path, store_name, force_reload=False):
     
         return VectorStore
 
-    except Exception as e:
-        st.error(f"An error occurred: {e}")
-        traceback.print_exc()
-        return None
-
-        
 # Utility function to load text from a PDF
 def load_pdf_text(file_path):
     pdf_reader = PdfReader(file_path)
@@ -95,6 +93,7 @@ def display_chat_history(chat_history):
 
 
 
+
 def page1():
     try:
         hide_streamlit_style = """
@@ -105,73 +104,102 @@ def page1():
                 """
         st.markdown(hide_streamlit_style, unsafe_allow_html=True)
     
-        col1, col2 = st.columns([3, 1])
+         # Create columns for layout
+        col1, col2 = st.columns([3, 1])  # Adjust the ratio to your liking
 
         with col1:
-            st.title("Welcome to BinDocs ChatBot!")
+            st.title("Welcome to BinDocs AI!")
 
         with col2:
+            # Load and display the image in the right column, which will be the top-right corner of the page
             image = Image.open('BinDoc Logo (Quadratisch).png')
             st.image(image, use_column_width='always')
 
-        if not os.path.exists(pdf_path):
-            st.error("File not found. Please check the file path.")
-            return
+ 
+        # Start tracking user interactions
+        with streamlit_analytics.track():
+            if not os.path.exists(pdf_path):
+                st.error("File not found. Please check the file path.")
+                return
+    
+            VectorStore = load_vector_store(pdf_path, "vector_store_page1", force_reload=False)
 
-        VectorStore = load_vector_store(pdf_path, "vector_store_page1", force_reload=False)
-        display_chat_history(st.session_state['chat_history_page1'])
+            display_chat_history(st.session_state['chat_history_page1'])
+    
+            st.write("<!-- Start Spacer -->", unsafe_allow_html=True)
+            st.write("<div style='flex: 1;'></div>", unsafe_allow_html=True)
+            st.write("<!-- End Spacer -->", unsafe_allow_html=True)
+    
+            new_messages_placeholder = st.empty()
     
-        st.write("<!-- Start Spacer -->", unsafe_allow_html=True)
-        st.write("<div style='flex: 1;'></div>", unsafe_allow_html=True)
-        st.write("<!-- End Spacer -->", unsafe_allow_html=True)
+            query = st.text_input("Geben Sie hier Ihre Frage ein / Enter your question here:")
+
+            add_vertical_space(2)  # Adjust as per the desired spacing
+            
+            # Create two columns for the buttons
+            col1, col2 = st.columns(2)
+            
+            with col1:
+                if st.button("Was kann ich mit dem Prognose-Analyse-Tool machen?"):
+                    query = "Was kann ich mit dem Prognose-Analyse-Tool machen?"
+                if st.button("Was sagt mir die Farbe der Balken der Bevölkerungsentwicklung?"):
+                    query = "Was sagt mir die Farbe der Balken der Bevölkerungsentwicklung?"
+                if st.button("Ich habe mein Meta-Password vergessen, wie kann ich es zurücksetzen?"):
+                    query = "Ich habe mein Meta-Password vergessen, wie kann ich es zurücksetzen?"
 
-        query = st.text_input("Ask questions about your PDF file (in any preferred language):")
-        add_vertical_space(2)
+            
+            with col2:
+                if st.button("Dies ist eine reine Test Frage, welche aber eine ausreichende Länge hat."):
+                    query = "Dies ist eine reine Test Frage, welche aber eine ausreichende Länge hat."
+                if st.button("Was sagt mir denn generell die wundervolle Bevölkerungsentwicklung?"):
+                    query = "Was sagt mir denn generell die wundervolle Bevölkerungsentwicklung?"
+                if st.button("Ob ich hier wohl viel schreibe, dass die Fragen vom Layout her passen?"):
+                    query = "Ob ich hier wohl viel schreibe, dass die Fragen vom Layout her passen?"
 
-        col1, col2 = st.columns(2)
         
-        with col1:
-            if st.button("Was kann ich mit dem Prognose-Analyse-Tool machen?"):
-                query = "Was kann ich mit dem Prognose-Analyse-Tool machen?"
-            if st.button("Was sagt mir die Farbe der Balken der Bevölkerungsentwicklung?"):
-                query = "Was sagt mir die Farbe der Balken der Bevölkerungsentwicklung?"
-            if st.button("Ich habe mein Meta-Password vergessen, wie kann ich es zurücksetzen?"):
-                query = "Ich habe mein Meta-Password vergessen, wie kann ich es zurücksetzen?"
+            if query:
+                st.session_state['chat_history_page1'].append(("User", query, "new"))
 
-        with col2:
-            if st.button("Dies ist eine reine Test Frage, welche aber eine ausreichende Länge hat."):
-                query = "Dies ist eine reine Test Frage, welche aber eine ausreichende Länge hat."
-            if st.button("Was sagt mir denn generell die wundervolle Bevölkerungsentwicklung?"):
-                query = "Was sagt mir denn generell die wundervolle Bevölkerungsentwicklung?"
-            if st.button("Ob ich hier wohl viel schreibe, dass die Fragen vom Layout her passen?"):
-                query = "Ob ich hier wohl viel schreibe, dass die Fragen vom Layout her passen?"
-
-        if query:
-            st.session_state['chat_history_page1'].append(("User", query, "new"))
-            start_time = time.time()
-            with st.spinner('Bot is thinking...'):
-                chain = load_chatbot()
-                docs = VectorStore.similarity_search(query=query, k=3)
-                with get_openai_callback() as cb:
-                    response = chain.run(input_documents=docs, question=query)
-
-            end_time = time.time()
-            duration = end_time - start_time
-            st.text(f"Response time: {duration:.2f} seconds")
-
-            st.session_state['chat_history_page1'].append(("Bot", response, "new"))
-
-            new_messages = st.session_state['chat_history_page1'][-2:]
-            for chat in new_messages:
-                background_color = "#ffeecf"
-                st.markdown(f"<div style='background-color: {background_color}; padding: 10px; border-radius: 10px; margin: 10px;'>{chat[0]}: {chat[1]}</div>", unsafe_allow_html=True)
-
-            query = ""
+                # Start timing
+                start_time = time.time()
+                
+                with st.spinner('Bot is thinking...'):
+                    # Use the VectorStore loaded at the start from the session state
+                    chain = load_chatbot()
+                    docs = VectorStore.similarity_search(query=query, k=3)
+                    with get_openai_callback() as cb:
+                        response = chain.run(input_documents=docs, question=query)
+
+                        
+                # Stop timing
+                end_time = time.time()
+                
+                # Calculate duration
+                duration = end_time - start_time
 
+                # You can use Streamlit's text function to display the timing
+                st.text(f"Response time: {duration:.2f} seconds")
+    
+                st.session_state['chat_history_page1'].append(("Bot", response, "new"))
+    
+    
+                # Display new messages at the bottom
+                new_messages = st.session_state['chat_history_page1'][-2:]
+                for chat in new_messages:
+                    background_color = "#ffeecf" if chat[2] == "new" else "#ffeecf" if chat[0] == "User" else "#ffeecf"
+                    new_messages_placeholder.markdown(f"<div style='background-color: {background_color}; padding: 10px; border-radius: 10px; margin: 10px;'>{chat[0]}: {chat[1]}</div>", unsafe_allow_html=True)
+    
+    
+                # Clear the input field after the query is made
+                query = ""
+    
+            # Mark all messages as old after displaying
             st.session_state['chat_history_page1'] = [(sender, msg, "old") for sender, msg, _ in st.session_state['chat_history_page1']]
 
     except Exception as e:
         st.error(f"Upsi, an unexpected error occurred: {e}")
+        # Optionally log the exception details to a file or error tracking service
+
 
 
 
@@ -284,7 +312,9 @@ def page2():
         st.error(f"Upsi, an unexpected error occurred: {e}")
         # Optionally log the exception details to a file or error tracking service
 
-   
+
+
+
 
 def main():
     # Sidebar content