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Update app.py
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app.py
CHANGED
@@ -87,95 +87,95 @@ def load_chatbot():
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return load_qa_chain(llm=OpenAI(), chain_type="stuff")
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def main():
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st.session_state
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if st.button("Was genau ist ein Belegarzt?"):
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query = "Was genau ist ein Belegarzt?"
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if st.button("Wofür wird die Alpha-ID verwendet?"):
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query = "Wofür wird die Alpha-ID verwendet?"
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if st.button("Was sind die Vorteile des ambulanten Operierens?"):
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query = "Was sind die Vorteile des ambulanten Operierens?"
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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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if query:
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st.session_state['chat_history'].append(("User", query, "new"))
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loading_message = st.empty()
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loading_message.text('Bot is thinking...')
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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'].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'][-2:]
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for chat in new_messages:
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background_color = "#FFA07A" if chat[2] == "new" else "#acf" if chat[0] == "User" else "#caf"
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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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# Scroll to the latest response using JavaScript
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st.write("<script>document.getElementById('response').scrollIntoView();</script>", unsafe_allow_html=True)
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st.
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def display_chat_history(chat_history):
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background_color = "#FFA07A" if chat[2] == "new" else "#acf" if chat[0] == "User" else "#caf"
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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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if __name__ == "__main__":
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main()
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return load_qa_chain(llm=OpenAI(), chain_type="stuff")
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def main():
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try:
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hide_streamlit_style = """
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<style>
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#MainMenu {visibility: hidden;}
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footer {visibility: hidden;}
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</style>
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"""
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st.markdown(hide_streamlit_style, unsafe_allow_html=True)
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# Main content
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st.title("Welcome to BinDocs ChatBot! 🤖")
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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, "my_vector_store", force_reload=False)
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if "chat_history" not in st.session_state:
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st.session_state['chat_history'] = []
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display_chat_history(st.session_state['chat_history'])
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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("Ask questions about your PDF file (in any preferred language):")
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if st.button("Was genau ist ein Belegarzt?"):
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query = "Was genau ist ein Belegarzt?"
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if st.button("Wofür wird die Alpha-ID verwendet?"):
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query = "Wofür wird die Alpha-ID verwendet?"
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if st.button("Was sind die Vorteile des ambulanten Operierens?"):
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query = "Was sind die Vorteile des ambulanten Operierens?"
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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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if query:
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st.session_state['chat_history'].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'].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'][-2:]
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for chat in new_messages:
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background_color = "#FFA07A" if chat[2] == "new" else "#acf" if chat[0] == "User" else "#caf"
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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'] = [(sender, msg, "old") for sender, msg, _ in st.session_state['chat_history']]
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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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def display_chat_history(chat_history):
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background_color = "#FFA07A" if chat[2] == "new" else "#acf" if chat[0] == "User" else "#caf"
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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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if __name__ == "__main__":
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main()
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