Update app.py

app.py

@@ -19,9 +19,6 @@ import pandas as pd
 import pydeck as pdk
 from urllib.error import URLError
 
-
-st.set_page_config(layout="initial_sidebar_state="collapsed"")
-
 # Step 1: Clone the Dataset Repository
 repo = Repository(
     local_dir="Private_Book",  # Local directory to clone the repository
@@ -34,67 +31,17 @@ repo.git_pull()  # Pull the latest changes (if any)
 # Step 2: Load the PDF File
 pdf_path = "Private_Book/KOMBI_all2.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
-
-    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
 
 
-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
 
-# Utility function to load text from a PDF
-def load_pdf_text(file_path):
-    pdf_reader = PdfReader(file_path)
-    text = ""
-    for page in pdf_reader.pages:
-        text += page.extract_text() or ""  # Add fallback for pages where text extraction fails
-    return text
-
-def load_chatbot():
-    return load_qa_chain(llm=OpenAI(), chain_type="stuff")
+st.set_page_config(layout="initial_sidebar_state="collapsed"")
 
-def main():
+def page1():
     try:
         hide_streamlit_style = """
                 <style>
@@ -206,12 +153,69 @@ def main():
         st.error(f"Upsi, an unexpected error occurred: {e}")
         # Optionally log the exception details to a file or error tracking service
 
+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
+
+# Utility function to load text from a PDF
+def load_pdf_text(file_path):
+    pdf_reader = PdfReader(file_path)
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text() or ""  # Add fallback for pages where text extraction fails
+    return text
+
+def load_chatbot():
+    return load_qa_chain(llm=OpenAI(), chain_type="stuff")
+
 
 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():
+    st.title("New Page")
+    st.write("This is the content of the new page.")
+
+def main():
+    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.sidebar.title('Navigation')
+        page = st.sidebar.selectbox("Choose a page", ["Page 1", "Page 2"])
+
+        if page == "Page 1":
+            page1()
+        elif page == "Page 2":
+            page2()
+        
+        add_vertical_space(1)  # Adjust as per the desired spacing
+    
+        st.write('Made with ❤️ by BinDoc GmbH')
+    
 
+    
 if __name__ == "__main__":
     main()