Update app.py

app.py

@@ -2,7 +2,6 @@ import streamlit as st
 from PIL import Image
 import random
 import time
-import streamlit_analytics
 from dotenv import load_dotenv
 import pickle
 from huggingface_hub import Repository
@@ -107,7 +106,6 @@ def load_vector_store(file_path, store_name, force_reload=False):
             repo.git_add(f"{store_name}.pkl")  # Use just the file name
             repo.git_commit(f"Update vector store: {store_name}")
             repo.git_push()
-            #st.text("Committed and pushed vector store to repository.")
         except Exception as e:
             st.error(f"Error during Git operations: {e}")
         finally:
@@ -319,10 +317,6 @@ def page1():
             # You can use Streamlit's text function to display the timing
             st.text(f"Response time: {duration:.2f} seconds")
 
-            # Display the current working directory after save_conversation
-            current_dir = os.getcwd()
-            st.text(f"Current working directory before save_conversation: {current_dir}")
-
             st.session_state['chat_history_page1'].append(("Bot", response, "new"))
 
 
@@ -337,8 +331,8 @@ def page1():
 
             
             # Display the current working directory after save_conversation
-            current_dir = os.getcwd()
-            st.text(f"Current working directory after save_conversation: {current_dir}")
+            #current_dir = os.getcwd()
+            #st.text(f"Current working directory after save_conversation: {current_dir}")
             
             # Clear the input field after the query is made
             query = ""
@@ -373,90 +367,88 @@ def page2():
             image = Image.open('BinDoc Logo (Quadratisch).png')
             st.image(image, use_column_width='always')
 
- 
-        # Start tracking user interactions
-        with streamlit_analytics.track():
             
-            if not os.path.exists(pdf_path2):
-                st.error("File not found. Please check the file path.")
-                return
-    
-            VectorStore = load_vector_store(pdf_path2, "Buch_2301", force_reload=False)
-    
+        if not os.path.exists(pdf_path2):
+            st.error("File not found. Please check the file path.")
+            return
+
+        VectorStore = load_vector_store(pdf_path2, "Buch_2301", force_reload=False)
+
+
+
+        display_chat_history(st.session_state['chat_history_page2'])
+
+        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()
+
+        query = st.text_input("Ask questions about your PDF file (in any preferred language):")
+
+        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("Nenne mir 5 wichtige Personalkennzahlen im Krankenhaus."):
+                query = "Nenne mir 5 wichtige Personalkennzahlen im Krankenhaus."
+            if st.button("Wie ist die durchschnittliche Bettenauslastung eines Krankenhauses?"):
+                query = ("Wie ist die durchschnittliche Bettenauslastung eines Krankenhauses?")
+            if st.button("Welches sind die häufigsten DRGs, die von den Krankenhäusern abgerechnet werden?"):
+                query = "Welches sind die häufigsten DRGs, die von den Krankenhäusern abgerechnet werden? "
+
+        
+        with col2:
+            if st.button("Wie viel Casemixpunkte werden im Median von einer ärztlichen Vollkraft erbracht?"):
+                query = "Wie viel Casemixpunkte werden im Median von einer ärztlichen Vollkraft erbracht?"
+            if st.button("Bitte erstelle mir einer Übersicht der wichtiger Strukturkennzahlen eines Krankenhauses der Grund- und Regelversorgung."):
+                query = "Bitte erstelle mir einer Übersicht der wichtiger Strukturkennzahlen eines Krankenhauses der Grund- und Regelversorgung."
+            if st.button("Wie viele Patienten eines Grund- und Regelversorgers kommen aus welcher Fahrzeitzone?"):
+                query = "Wie viele Patienten eines Grund- und Regelversorgers kommen aus welcher Fahrzeitzone?"
 
     
-            display_chat_history(st.session_state['chat_history_page2'])
-    
-            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()
-    
-            query = st.text_input("Ask questions about your PDF file (in any preferred language):")
 
-            add_vertical_space(2)  # Adjust as per the desired spacing
+        if query:
+            full_query = ask_bot(query)
+            st.session_state['chat_history_page2'].append(("User", query, "new"))
+
+            # Start timing
+            start_time = time.time()
             
-            # Create two columns for the buttons
-            col1, col2 = st.columns(2)
+            with st.spinner('Bot is thinking...'):
+                chain = load_chatbot()
+                docs = VectorStore.similarity_search(query=query, k=5)
+                with get_openai_callback() as cb:
+                    response = chain.run(input_documents=docs, question=full_query)
+                    response = handle_no_answer(response)  # Process the response through the new function
+                    
+            # Stop timing
+            end_time = time.time()
             
-            with col1:
-                if st.button("Nenne mir 5 wichtige Personalkennzahlen im Krankenhaus."):
-                    query = "Nenne mir 5 wichtige Personalkennzahlen im Krankenhaus."
-                if st.button("Wie ist die durchschnittliche Bettenauslastung eines Krankenhauses?"):
-                    query = ("Wie ist die durchschnittliche Bettenauslastung eines Krankenhauses?")
-                if st.button("Welches sind die häufigsten DRGs, die von den Krankenhäusern abgerechnet werden?"):
-                    query = "Welches sind die häufigsten DRGs, die von den Krankenhäusern abgerechnet werden? "
+            # Calculate duration
+            duration = end_time - start_time
 
-            
-            with col2:
-                if st.button("Wie viel Casemixpunkte werden im Median von einer ärztlichen Vollkraft erbracht?"):
-                    query = "Wie viel Casemixpunkte werden im Median von einer ärztlichen Vollkraft erbracht?"
-                if st.button("Bitte erstelle mir einer Übersicht der wichtiger Strukturkennzahlen eines Krankenhauses der Grund- und Regelversorgung."):
-                    query = "Bitte erstelle mir einer Übersicht der wichtiger Strukturkennzahlen eines Krankenhauses der Grund- und Regelversorgung."
-                if st.button("Wie viele Patienten eines Grund- und Regelversorgers kommen aus welcher Fahrzeitzone?"):
-                    query = "Wie viele Patienten eines Grund- und Regelversorgers kommen aus welcher Fahrzeitzone?"
+            # You can use Streamlit's text function to display the timing
+            st.text(f"Response time: {duration:.2f} seconds")
 
-        
+            st.session_state['chat_history_page2'].append(("Bot", response, "new"))
 
-            if query:
-                full_query = ask_bot(query)
-                st.session_state['chat_history_page2'].append(("User", query, "new"))
+            save_conversation(st.session_state['chat_history_page2'], st.session_state['session_id'], 2)
 
-                # Start timing
-                start_time = time.time()
-                
-                with st.spinner('Bot is thinking...'):
-                    chain = load_chatbot()
-                    docs = VectorStore.similarity_search(query=query, k=5)
-                    with get_openai_callback() as cb:
-                        response = chain.run(input_documents=docs, question=full_query)
-                        response = handle_no_answer(response)  # Process the response through the new function
-                        
-                # Stop timing
-                end_time = time.time()
-                
-                # Calculate duration
-                duration = end_time - start_time
+            # Display new messages at the bottom
+            new_messages = st.session_state['chat_history_page2'][-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)
 
-                # You can use Streamlit's text function to display the timing
-                st.text(f"Response time: {duration:.2f} seconds")
-    
-                st.session_state['chat_history_page2'].append(("Bot", response, "new"))
-    
-    
-                # Display new messages at the bottom
-                new_messages = st.session_state['chat_history_page2'][-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_page2'] = [(sender, msg, "old") for sender, msg, _ in st.session_state['chat_history_page2']]
+
+            # Clear the input field after the query is made
+            query = ""
+
+        # Mark all messages as old after displaying
+        st.session_state['chat_history_page2'] = [(sender, msg, "old") for sender, msg, _ in st.session_state['chat_history_page2']]
 
     except Exception as e:
         st.error(f"Upsi, an unexpected error occurred: {e}")
@@ -485,90 +477,88 @@ def page3():
             image = Image.open('BinDoc Logo (Quadratisch).png')
             st.image(image, use_column_width='always')
 
- 
-        # Start tracking user interactions
-        with streamlit_analytics.track():
             
-            if not os.path.exists(pdf_path2):
-                st.error("File not found. Please check the file path.")
-                return
-    
-            VectorStore = load_vector_store(pdf_path3, "Kosten_Str_2301", force_reload=False)
-    
+        if not os.path.exists(pdf_path2):
+            st.error("File not found. Please check the file path.")
+            return
+
+        VectorStore = load_vector_store(pdf_path3, "Kosten_Str_2301", force_reload=False)
+
+
+
+        display_chat_history(st.session_state['chat_history_page3'])
+
+        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()
+
+        query = st.text_input("Ask questions about your PDF file (in any preferred language):")
+
+        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("Wie hat sich die Bettenanzahl in den letzten 10 Jahren entwickelt?"):
+                query = "Wie hat sich die Bettenanzahl in den letzten 10 Jahren entwickelt?"
+            if st.button("Wie viele Patienten werden pro Jahr vollstationär behandelt?"):
+                query = ("Wie viele Patienten werden pro Jahr vollstationär behandelt?")
+            if st.button("Wie viele Vollkräfte arbeiten in Summe in deutschen Krankenhäusern?"):
+                query = "Wie viele Vollkräfte arbeiten in Summe in deutschen Krankenhäusern? "
+
+        
+        with col2:
+            if st.button("Welche unterschiedlichen Personalkosten gibt es im Krankenhaus?"):
+                query = "Welche unterschiedlichen Personalkosten gibt es im Krankenhaus?"
+            if st.button("Welche Sachkosten werden in Krankenhäusern unterschieden?"):
+                query = "Welche Sachkosten werden in Krankenhäusern unterschieden? "
+            if st.button("Wie hoch sind die Gesamtkosten der Krankenhäuser pro Jahr?"):
+                query = "Wie hoch sind die Gesamtkosten der Krankenhäuser pro Jahr?"
 
     
-            display_chat_history(st.session_state['chat_history_page3'])
-    
-            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()
-    
-            query = st.text_input("Ask questions about your PDF file (in any preferred language):")
 
-            add_vertical_space(2)  # Adjust as per the desired spacing
+        if query:
+            full_query = ask_bot(query)
+            st.session_state['chat_history_page3'].append(("User", query, "new"))
+
+            # Start timing
+            start_time = time.time()
             
-            # Create two columns for the buttons
-            col1, col2 = st.columns(2)
+            with st.spinner('Bot is thinking...'):
+                chain = load_chatbot()
+                docs = VectorStore.similarity_search(query=query, k=5)
+                with get_openai_callback() as cb:
+                    response = chain.run(input_documents=docs, question=full_query)
+                    response = handle_no_answer(response)  # Process the response through the new function
+                    
+            # Stop timing
+            end_time = time.time()
             
-            with col1:
-                if st.button("Wie hat sich die Bettenanzahl in den letzten 10 Jahren entwickelt?"):
-                    query = "Wie hat sich die Bettenanzahl in den letzten 10 Jahren entwickelt?"
-                if st.button("Wie viele Patienten werden pro Jahr vollstationär behandelt?"):
-                    query = ("Wie viele Patienten werden pro Jahr vollstationär behandelt?")
-                if st.button("Wie viele Vollkräfte arbeiten in Summe in deutschen Krankenhäusern?"):
-                    query = "Wie viele Vollkräfte arbeiten in Summe in deutschen Krankenhäusern? "
+            # Calculate duration
+            duration = end_time - start_time
 
-            
-            with col2:
-                if st.button("Welche unterschiedlichen Personalkosten gibt es im Krankenhaus?"):
-                    query = "Welche unterschiedlichen Personalkosten gibt es im Krankenhaus?"
-                if st.button("Welche Sachkosten werden in Krankenhäusern unterschieden?"):
-                    query = "Welche Sachkosten werden in Krankenhäusern unterschieden? "
-                if st.button("Wie hoch sind die Gesamtkosten der Krankenhäuser pro Jahr?"):
-                    query = "Wie hoch sind die Gesamtkosten der Krankenhäuser pro Jahr?"
+            # You can use Streamlit's text function to display the timing
+            st.text(f"Response time: {duration:.2f} seconds")
 
-        
+            st.session_state['chat_history_page3'].append(("Bot", response, "new"))
 
-            if query:
-                full_query = ask_bot(query)
-                st.session_state['chat_history_page3'].append(("User", query, "new"))
 
-                # Start timing
-                start_time = time.time()
-                
-                with st.spinner('Bot is thinking...'):
-                    chain = load_chatbot()
-                    docs = VectorStore.similarity_search(query=query, k=5)
-                    with get_openai_callback() as cb:
-                        response = chain.run(input_documents=docs, question=full_query)
-                        response = handle_no_answer(response)  # Process the response through the new function
-                        
-                # Stop timing
-                end_time = time.time()
+            # Display new messages at the bottom
+            new_messages = st.session_state['chat_history_page3'][-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)
                 
-                # Calculate duration
-                duration = end_time - start_time
+            save_conversation(st.session_state['chat_history_page3'], st.session_state['session_id'], 3)
 
-                # You can use Streamlit's text function to display the timing
-                st.text(f"Response time: {duration:.2f} seconds")
-    
-                st.session_state['chat_history_page3'].append(("Bot", response, "new"))
-    
-    
-                # Display new messages at the bottom
-                new_messages = st.session_state['chat_history_page3'][-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_page3'] = [(sender, msg, "old") for sender, msg, _ in st.session_state['chat_history_page3']]
+            # Clear the input field after the query is made
+            query = ""
+
+        # Mark all messages as old after displaying
+        st.session_state['chat_history_page3'] = [(sender, msg, "old") for sender, msg, _ in st.session_state['chat_history_page3']]
 
     except Exception as e:
         st.error(f"Upsi, an unexpected error occurred: {e}")