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
@@ -15,6 +14,9 @@ from langchain.llms import OpenAI
 from langchain.chains.question_answering import load_qa_chain
 from langchain.callbacks import get_openai_callback
 import os
+import uuid
+import json
+
 
 import pandas as pd
 import pydeck as pdk
@@ -30,6 +32,10 @@ if 'chat_history_page2' not in st.session_state:
 if 'chat_history_page3' not in st.session_state:
     st.session_state['chat_history_page3'] = []
 
+# This session ID will be unique per user session and consistent across all pages.
+if 'session_id' not in st.session_state:
+    st.session_state['session_id'] = str(uuid.uuid4())
+
 
 
 # Step 1: Clone the Dataset Repository
@@ -41,6 +47,17 @@ repo = Repository(
 )
 repo.git_pull()  # Pull the latest changes (if any)
 
+
+# Step 1: Clone the ChatSet Repository - save all the chats anonymously
+repo2 = Repository(
+    local_dir="Chat_Store",  # Local directory to clone the repository
+    repo_type="dataset",  # Specify that this is a dataset repository
+    clone_from="Anne31415/Chat_Store",  # Replace with your repository URL
+    token=os.environ["HUB_TOKEN"]  # Use the secret token to authenticate
+)
+repo.git_pull()  # Pull the latest changes (if any)
+
+
 # Step 2: Load the PDF File
 pdf_path = "Private_Book/KH_Reform230124.pdf"  # Replace with your PDF file path
 
@@ -90,7 +107,6 @@ 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:
@@ -171,11 +187,55 @@ def handle_no_answer(response):
 
 def ask_bot(query):
     # Definiere den standardmäßigen Prompt
-    standard_prompt = "Schreibe immer höflich und auf antworte immer in der Sprache in der der User auch schreibt. Formuliere immer ganze freundliche ganze Sätze und biete wenn möglich auch mehr Informationen (aber nicht mehr als 1 Satz mehr). Wenn der User sehr vage schreibt frage nach."
+    standard_prompt = "Schreibe immer höflich und auf antworte immer in der Sprache in der der User auch schreibt. Formuliere immer ganze freundliche ganze Sätze und biete wenn möglich auch mehr Informationen (aber nicht mehr als 1 Satz mehr). Wenn der User sehr vage schreibt frage nach. Wenn du zu einer bestimmten Frage Daten aus mehreren Jahren hast, frage den User für welche Jahre er sich interessiert und nenne ihm natürlich Möglichkeiten über die Jahre die du hast. "
     # Kombiniere den standardmäßigen Prompt mit der Benutzeranfrage
     full_query = standard_prompt + query
     return full_query
+
+def save_conversation(chat_histories, session_id):
+    base_path = "Chat_Store/conversation_logs"
+    if not os.path.exists(base_path):
+        os.makedirs(base_path)
+
+    filename = f"{base_path}/{session_id}.json"
+
+    # Check if the log file already exists
+    existing_data = {"page1": [], "page2": [], "page3": []}
+    if os.path.exists(filename):
+        with open(filename, 'r', encoding='utf-8') as file:
+            existing_data = json.load(file)
+
+    # Append the new chat history to the existing data for each page
+    for page_number, chat_history in enumerate(chat_histories, start=1):
+        existing_data[f"page{page_number}"] += chat_history
+
+    with open(filename, 'w', encoding='utf-8') as file:
+        json.dump(existing_data, file, indent=4, ensure_ascii=False)
+
+    # Git operations
+    try:
+        # Change directory to Chat_Store for Git operations
+        original_dir = os.getcwd()
+        os.chdir('Chat_Store')
+    
+        # Correct file path relative to the Git repository's root
+        git_file_path = f"conversation_logs/{session_id}.json"
     
+        repo2.git_add(git_file_path)
+        repo2.git_commit(f"Add/update conversation log for session {session_id}")
+        repo2.git_push()
+    
+        # Change back to the original directory
+        os.chdir(original_dir)
+    except Exception as e:
+        st.error(f"Error during Git operations: {e}")
+
+        
+def display_session_id():
+    session_id = st.session_state['session_id']
+    st.sidebar.markdown(f"**Your Session ID:** `{session_id}`")
+    st.sidebar.markdown("Use this ID for reference in communications or feedback.")
+
 
 def page1():
     try:
@@ -199,88 +259,97 @@ def page1():
             st.image(image, use_column_width='always')
 
  
-        # 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, "KH_Reform_2301", force_reload=False)
+        if not os.path.exists(pdf_path):
+            st.error("File not found. Please check the file path.")
+            return
+
+        VectorStore = load_vector_store(pdf_path, "KH_Reform_2301", force_reload=False)
+
+        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()
+
+        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("Wie viele Ärzte benötigt eine Klinik in der Leistungsgruppe Stammzell-transplantation?"):
+                query = "Wie viele Ärzte benötigt eine Klinik in der Leistungsgruppe Stammzell-transplantation?"
+            if st.button("Wie viele Leistungsgruppen gibt es?"):
+                query = ("Wie viele Leistungsgruppen gibt es?")
+            if st.button("Was sind die hauptsächlichen Änderungsvorhaben der Krankenhausreform?"):
+                query = "Was sind die hauptsächlichen Änderungsvorhaben der Krankenhausreform?"
+
+        
+        with col2:
+            if st.button("Welche und wieviele Fachärzte benötige ich für die Leistungsgruppe Pädiatrie? "):
+                query = "Welche und wieviele Fachärzte benötige ich für die Leistungsgruppe Pädiatrie"
+            if st.button("Was soll die Reform der Notfallversorgung beinhalten?"):
+                query = "Was soll die Reform der Notfallversorgung beinhalten?"
+            if st.button("Was bedeutet die Vorhaltefinanzierung?"):
+                query = "Was bedeutet die Vorhaltefinanzierung?"
+
 
-            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()
     
-            query = st.text_input("Geben Sie hier Ihre Frage ein / Enter your question here:")
+        if query:
+            full_query = ask_bot(query)
+            st.session_state['chat_history_page1'].append(("User", query, "new"))
 
-            add_vertical_space(2)  # Adjust as per the desired spacing
-            
-            # Create two columns for the buttons
-            col1, col2 = st.columns(2)
+            # Start timing
+            start_time = time.time()
             
-            with col1:
-                if st.button("Wie viele Ärzte benötigt eine Klinik in der Leistungsgruppe Stammzell-transplantation?"):
-                    query = "Wie viele Ärzte benötigt eine Klinik in der Leistungsgruppe Stammzell-transplantation?"
-                if st.button("Wie viele Leistungsgruppen soll es durch die neue Krankenhaus-Reform geben?"):
-                    query = ("Wie viele Leistungsgruppen soll es durch die neue Krankenhaus-Reform geben?")
-                if st.button("Was sind die hauptsächlichen Änderungsvorhaben der Krankenhausreform?"):
-                    query = "Was sind die hauptsächlichen Änderungsvorhaben der Krankenhausreform?"
+            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 col2:
-                if st.button("Welche und wieviele Fachärzte benötige ich für die Leistungsgruppe Pädiatrie? "):
-                    query = "Welche und wieviele Fachärzte benötige ich für die Leistungsgruppe Pädiatrie"
-                if st.button("Was soll die Reform der Notfallversorgung beinhalten?"):
-                    query = "Was soll die Reform der Notfallversorgung beinhalten?"
-                if st.button("Was bedeutet die Vorhaltefinanzierung?"):
-                    query = "Was bedeutet die Vorhaltefinanzierung?"
+            # Calculate duration
+            duration = end_time - start_time
+            st.text(f"Response time: {duration:.2f} seconds")
+
+            st.session_state['chat_history_page1'].append(("Bot", response, "new"))
+
+            # Combine chat histories from all pages
+            all_chat_histories = [
+                st.session_state['chat_history_page1'],
+                st.session_state['chat_history_page2'],
+                st.session_state['chat_history_page3']
+            ]
+
+            # Save the combined chat histories
+            save_conversation(all_chat_histories, st.session_state['session_id'])
+
 
 
-        
-            if query:
-                full_query = ask_bot(query)
-                st.session_state['chat_history_page1'].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()
-                
-                # 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']]
+
+            # 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}")
@@ -309,90 +378,96 @@ 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
-            
-            # Create two columns for the buttons
-            col1, col2 = st.columns(2)
+        if query:
+            full_query = ask_bot(query)
+            st.session_state['chat_history_page2'].append(("User", query, "new"))
+
+            # Start timing
+            start_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? "
+            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 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?"
+            # Calculate duration
+            duration = end_time - start_time
+            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"))
-
-                # 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
-
-                # 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']]
+            # Combine chat histories from all pages
+            all_chat_histories = [
+                st.session_state['chat_history_page1'],
+                st.session_state['chat_history_page2'],
+                st.session_state['chat_history_page3']
+            ]
+
+            # Save the combined chat histories
+            save_conversation(all_chat_histories, st.session_state['session_id'])
+
+            # 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']]
 
     except Exception as e:
         st.error(f"Upsi, an unexpected error occurred: {e}")
@@ -421,107 +496,153 @@ 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
+            st.text(f"Response time: {duration:.2f} seconds")
+
+            st.session_state['chat_history_page3'].append(("Bot", response, "new"))
+
+            # Combine chat histories from all pages
+            all_chat_histories = [
+                st.session_state['chat_history_page1'],
+                st.session_state['chat_history_page2'],
+                st.session_state['chat_history_page3']
+            ]
+
+            # Save the combined chat histories
+            save_conversation(all_chat_histories, st.session_state['session_id'])
+
+
+            # 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)
 
             
-            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?"
+            save_conversation(st.session_state['chat_history_page3'], st.session_state['session_id'], 3)
 
-        
+            # Clear the input field after the query is made
+            query = ""
 
-            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()
-                
-                # 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_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']]
+        # 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}")
         # Optionally log the exception details to a file or error tracking service
 
+def page4():
+    try: 
+        st.header(":mailbox: Get In Touch With Me!")
+        st.markdown("Ihre Session-ID finden Sie auf der linken Seite!")
+
+        contact_form = """
+        <form action="https://formsubmit.co/anne.demond@googlemail.com" method="POST">
+             <input type="hidden" name="_captcha" value="false">
+             <input type="text" name="Session-ID" placeholder="Your Session-ID goes here" required>
+             <input type="email" name="email" placeholder="Your email" required>
+             <textarea name="message" placeholder="Your message here"></textarea>
+             <form action="https://formsubmit.co/your-random-string" method="POST" />
+             <button type="submit">Send</button>
+        </form>
+        """
+        
+        st.markdown(contact_form, unsafe_allow_html=True)
+        
+        # Use Local CSS File
+        def local_css(file_name):
+            with open(file_name) as f:
+                st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
+        
+        
+        local_css("style.css")
+        
+    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
+
 
+def display_session_id():
+    session_id = st.session_state['session_id']
+    st.sidebar.markdown(f"**Your Session ID:** `{session_id}`")
+    st.sidebar.markdown("Use this ID for reference in communications or feedback.")
 
+# Main function
 def main():
     # Sidebar content
     with st.sidebar:
         st.title('BinDoc GmbH')
         st.markdown("Experience revolutionary interaction with BinDocs Chat App, leveraging state-of-the-art AI technology.")
         add_vertical_space(1)
-        page = st.sidebar.selectbox("Choose a page", ["KH_Reform", "Kennzahlenbuch 100 Kennzahlen", "Kosten- und Strukturdaten der Krankenhäuser"])
-        add_vertical_space(1)
+        page = st.sidebar.selectbox("Choose a page", ["KH_Reform", "Kennzahlenbuch 100 Kennzahlen", "Kosten- und Strukturdaten der Krankenhäuser", "Form"])
+        add_vertical_space(4)
+        display_session_id()  # Display the session ID in the sidebar
         st.write('Made with ❤️ by BinDoc GmbH')
 
+        
     # Main area content based on page selection
     if page == "KH_Reform":
         page1()
@@ -529,6 +650,8 @@ def main():
         page2()
     elif page == "Kosten- und Strukturdaten der Krankenhäuser":
         page3()
+    elif page == "Form":
+        page4()
 
 
 if __name__ == "__main__":