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