Update app.py

app.py

@@ -15,11 +15,18 @@ from langchain.chains.question_answering import load_qa_chain
 from langchain.callbacks import get_openai_callback
 import os
 
-#st.set_page_config(layout="wide")
+import pandas as pd
+import pydeck as pdk
+from urllib.error import URLError
+
+# Initialize session state variables
+if 'chat_history_page1' not in st.session_state:
+    st.session_state['chat_history_page1'] = []
+
+if 'chat_history_page2' not in st.session_state:
+    st.session_state['chat_history_page2'] = []
 
 
-# Set the page config to make the sidebar start in the collapsed state
-st.set_page_config(initial_sidebar_state="collapsed")
 
 # Step 1: Clone the Dataset Repository
 repo = Repository(
@@ -33,54 +40,39 @@ repo.git_pull()  # Pull the latest changes (if any)
 # Step 2: Load the PDF File
 pdf_path = "Private_Book/141123_Kombi_compressed.pdf"  # Replace with your PDF file path
 
-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)  # Adjust as per the desired spacing
-    
-    st.markdown("""
-    Hello! I’m here to assist you with:<br><br>
-    📘 **Glossary Inquiries:**<br>
-    I can clarify terms like "DiGA", "AOP", or "BfArM", providing clear and concise explanations to help you understand our content better.<br><br>
-    🆘 **Help Page Navigation:**<br>
-    Ask me if you forgot your password or want to know more about topics related to the platform.<br><br>
-    📰 **Latest Whitepapers Insights:**<br>
-    Curious about our recent publications? Feel free to ask about our latest whitepapers!<br><br>
-    """, unsafe_allow_html=True)
-    
-    add_vertical_space(1)  # Adjust as per the desired spacing
+# Step 2: Load the PDF File
+pdf_path2 = "Private_Book/Deutsche_Kodierrichtlinien_23.pdf"  # Replace with your PDF file path
 
-    st.write('Made with ❤️ by BinDoc GmbH')
 
-    api_key = os.getenv("OPENAI_API_KEY")
-    # Retrieve the API key from st.secrets
+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
 
 
+# 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):
-    # Check if we need to force reload the vector store (e.g., when the PDF changes)
-    if force_reload or not os.path.exists(f"{store_name}.pkl"):
-        text_splitter = RecursiveCharacterTextSplitter(
-            chunk_size=1000,
-            chunk_overlap=200,
-            length_function=len
-        )
-        
-        text = load_pdf_text(file_path)
-        chunks = text_splitter.split_text(text=text)
-        
-        embeddings = OpenAIEmbeddings()
-        VectorStore = FAISS.from_texts(chunks, embedding=embeddings)
-        with open(f"{store_name}.pkl", "wb") as f:
-            pickle.dump(VectorStore, f)
-    else:
-        with open(f"{store_name}.pkl", "rb") as f:
-            VectorStore = pickle.load(f)
 
-    return VectorStore
+        # Check if we need to force reload the vector store (e.g., when the PDF changes)
+        if force_reload or not os.path.exists(f"{store_name}.pkl"):
+            text_splitter = RecursiveCharacterTextSplitter(
+                chunk_size=1000,
+                chunk_overlap=200,
+                length_function=len
+            )
+            
+            text = load_pdf_text(file_path)
+            chunks = text_splitter.split_text(text=text)
+            
+            embeddings = OpenAIEmbeddings()
+            VectorStore = FAISS.from_texts(chunks, embedding=embeddings)
+            with open(f"{store_name}.pkl", "wb") as f:
+                pickle.dump(VectorStore, f)
+        else:
+            with open(f"{store_name}.pkl", "rb") as f:
+                VectorStore = pickle.load(f)
+    
+        return VectorStore
 
 # Utility function to load text from a PDF
 def load_pdf_text(file_path):
@@ -93,7 +85,16 @@ def load_pdf_text(file_path):
 def load_chatbot():
     return load_qa_chain(llm=OpenAI(), chain_type="stuff")
 
-def main():
+
+def display_chat_history(chat_history):
+    for chat in chat_history:
+        background_color = "#ffeecf" if chat[2] == "new" else "#ffeecf" if chat[0] == "User" else "#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)
+
+
+
+
+def page1():
     try:
         hide_streamlit_style = """
                 <style>
@@ -114,22 +115,16 @@ def main():
             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_path):
                 st.error("File not found. Please check the file path.")
                 return
     
-            VectorStore = load_vector_store(pdf_path, "my_vector_store", force_reload=False)
-    
-    
-            if "chat_history" not in st.session_state:
-                st.session_state['chat_history'] = []
-    
-            display_chat_history(st.session_state['chat_history'])
+            VectorStore = load_vector_store(pdf_path, "vector_store_page1", 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)
@@ -163,7 +158,7 @@ def main():
 
         
             if query:
-                st.session_state['chat_history'].append(("User", query, "new"))
+                st.session_state['chat_history_page1'].append(("User", query, "new"))
 
                 # Start timing
                 start_time = time.time()
@@ -185,11 +180,11 @@ def main():
                 # You can use Streamlit's text function to display the timing
                 st.text(f"Response time: {duration:.2f} seconds")
     
-                st.session_state['chat_history'].append(("Bot", response, "new"))
+                st.session_state['chat_history_page1'].append(("Bot", response, "new"))
     
     
                 # Display new messages at the bottom
-                new_messages = st.session_state['chat_history'][-2:]
+                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)
@@ -199,18 +194,144 @@ def main():
                 query = ""
     
             # Mark all messages as old after displaying
-            st.session_state['chat_history'] = [(sender, msg, "old") for sender, msg, _ in st.session_state['chat_history']]
+            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
 
 
-def display_chat_history(chat_history):
-    for chat in chat_history:
-        background_color = "#ffeecf" if chat[2] == "new" else "#ffeecf" if chat[0] == "User" else "#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)
+
+
+def page2():
+    try:
+        hide_streamlit_style = """
+                <style>
+                #MainMenu {visibility: hidden;}
+                footer {visibility: hidden;}
+                </style>
+                """
+        st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+    
+         # Create columns for layout
+        col1, col2 = st.columns([3, 1])  # Adjust the ratio to your liking
+
+        with col1:
+            st.title("Kodieren statt Frustrieren!")
+
+        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')
+
+ 
+        # 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, "vector_store_page2", 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("Wann kodiere ich etwas als Hauptdiagnose und wann als Nebendiagnose?"):
+                    query = "Wann kodiere ich etwas als Hauptdiagnose und wann als Nebendiagnose?"
+                if st.button("Ein Patient wird mit Aszites bei bekannter Leberzirrhose stationär aufgenommen. Es wird nur der Aszites durch eine Punktion behandelt.Wie kodiere ich das?"):
+                    query = ("Ein Patient wird mit Aszites bei bekannter Leberzirrhose stationär aufgenommen. Es wird nur der Aszites durch eine Punktion behandelt.Wie kodiere ich das?")
+                if st.button("Hauptdiagnose: Hirntumor wie kodiere ich das?"):
+                    query = "Hauptdiagnose: Hirntumor wie kodiere ich das?"
+
+            
+            with col2:
+                if st.button("Welche Prozeduren werden normalerweise nicht verschlüsselt?"):
+                    query = "Welche Prozeduren werden normalerweise nicht verschlüsselt?"
+                if st.button("Was muss ich bei der Kodierung der Folgezusänden von Krankheiten beachten?"):
+                    query = "Was muss ich bei der Kodierung der Folgezusänden von Krankheiten beachten?"
+                if st.button("Was mache ich bei einer Verdachtsdiagnose, wenn mein Patien nach Hause entlassen wird?"):
+                    query = "Was mache ich bei einer Verdachtsdiagnose, wenn mein Patien nach Hause entlassen wird?"
+
+        
+            if query:
+                st.session_state['chat_history_page2'].append(("User", query, "new"))
+
+                # 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_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']]
+
+    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 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", ["Document Analysis Bot", "Coding Assistance Bot"])
+        add_vertical_space(1)
+        st.write('Made with ❤️ by BinDoc GmbH')
+
+    # Main area content based on page selection
+    if page == "Document Analysis Bot":
+        page1()
+    elif page == "Coding Assistance Bot":
+        page2()
 
 
 if __name__ == "__main__":
-    main()
+    main()