Update app.py

app.py

@@ -1,13 +1,8 @@
-import os
-from huggingface_hub import Repository
-import streamlit.components.v1 as components
-from datasets import load_dataset
-import random
+import streamlit as st
+from dotenv import load_dotenv
 import pickle
-from nltk.tokenize import sent_tokenize
-import nltk
+from huggingface_hub import Repository
 from PyPDF2 import PdfReader
-import streamlit as st
 from streamlit_extras.add_vertical_space import add_vertical_space
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.embeddings.openai import OpenAIEmbeddings
@@ -15,9 +10,7 @@ from langchain.vectorstores import FAISS
 from langchain.llms import OpenAI
 from langchain.chains.question_answering import load_qa_chain
 from langchain.callbacks import get_openai_callback
-
-
-nltk.download('punkt')
+import os
 
 # Step 1: Clone the Dataset Repository
 repo = Repository(
@@ -31,42 +24,59 @@ repo = Repository(
 repo.git_pull()  # Pull the latest changes (if any)
 
 # Step 2: Load the PDF File
-pdf_file_path = "Private_Book/ops2023syst_referenz_20221021.pdf"  # Replace with your PDF file path
-
+pdf_file_path = "Private_Book/Glossar_HELP_DESK_combi.pdf"  # Replace with your PDF file path
 
-# Sidebar contents
 with st.sidebar:
-    st.title(':orange_book: BinDoc GmbH')
+    st.title('BinDoc GmbH')
+    st.markdown("Experience revolutionary interaction with BinDocs Chat App, leveraging state-of-the-art AI technology.")
     
-
-    api_key = os.getenv("OPENAI_API_KEY")
-    # Retrieve the API key from st.secrets
+    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
+
+    glossary_option = st.selectbox(
+        'Want to see more Glossary Topics to ask about?',
+        ('Basisfallwert', 'Cash Flow', 'Arzneimittelgesetz (AMG)')
+    )
 
-    if not api_key:
-        st.warning('API key is required to proceed.')
-        st.stop()  # Stop the app if the API key is not provided
+    add_vertical_space(1)
 
-    st.markdown("Experience the future of document interaction with the revolutionary")
-    st.markdown("**BinDocs Chat App**.")
-    st.markdown("Harnessing the power of a Large Language Model and AI technology,")
-    st.markdown("this innovative platform redefines PDF engagement,")
-    st.markdown("enabling dynamic conversations that bridge the gap between")
-    st.markdown("human and machine intelligence.")
+    whitepaper_option = st.selectbox(
+        'Did you know we\'ve authored some really insightful and helpful whitepapers as well?',
+        ('Die Value Story als strategisches Instrument', 'Patientenmagnet Robotik: Best Practice Beispiel ', 'Das AGAPLesion-Konzept ')
+    )
 
-    add_vertical_space(3)  # Add more vertical space between text blocks
     st.write('Made with ❤️ by BinDoc GmbH')
 
+    api_key = os.getenv("OPENAI_API_KEY")
+    # Retrieve the API key from st.secrets
+
+
 def load_pdf(file_path):
     pdf_reader = PdfReader(file_path)
-    chunks = []
+    text = ""
     for page in pdf_reader.pages:
-        text = page.extract_text()
-        if text:
-            chunks.append(text)
-    
-    store_name = os.path.basename(file_path)[:-4]
-    
+        text += page.extract_text()
+
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=200,
+        length_function=len
+    )
+    chunks = text_splitter.split_text(text=text)
+
+    store_name, _ = os.path.splitext(os.path.basename(file_path))
+
     if os.path.exists(f"{store_name}.pkl"):
         with open(f"{store_name}.pkl", "rb") as f:
             VectorStore = pickle.load(f)
@@ -79,34 +89,25 @@ def load_pdf(file_path):
     return VectorStore
 
 
-def load_chatbot(max_tokens=300):
-    return load_qa_chain(llm=OpenAI(temperature=0.1, max_tokens=max_tokens), chain_type="stuff") 
-    
 
-def display_chat_history(chat_history):
-    for chat in chat_history:
-        background_color = "#FFA07A" if chat[2] == "new" else "#acf" if chat[0] == "User" else "#caf"
-        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 remove_incomplete_sentences(text):
-    sentences = sent_tokenize(text)
-    complete_sentences = [sent for sent in sentences if sent.endswith(('.', '!', '?'))]
-    return ' '.join(complete_sentences)
-
-def remove_redundant_information(text):
-    sentences = sent_tokenize(text)
-    unique_sentences = list(set(sentences))
-    return ' '.join(unique_sentences)
+def load_chatbot():
+    return load_qa_chain(llm=OpenAI(), chain_type="stuff")
 
-# Define a maximum token limit to avoid infinite loops
-MAX_TOKEN_LIMIT = 400
-
-import random
+def main():
 
+    hide_streamlit_style = """
+            <style>
+            #MainMenu {visibility: hidden;}
+            footer {visibility: hidden;}
+            </style>
+            """
+    st.markdown(hide_streamlit_style, unsafe_allow_html=True)
 
-def main():
-    st.title("BinDocs Chat App")
 
+    # Main content
+    st.title("Welcome to BinDocs ChatBot! 🤖")
+    
+    # Directly specifying the path to the PDF file
     pdf_path = pdf_file_path
     if not os.path.exists(pdf_path):
         st.error("File not found. Please check the file path.")
@@ -126,56 +127,57 @@ def main():
     if pdf_path is not None:
         query = st.text_input("Ask questions about your PDF file (in any preferred language):")
 
-    col1, col2 = st.columns(2)
-
-    with col1:
-        st.header("Examples1")
         if st.button("Was genau ist ein Belegarzt?"):
             query = "Was genau ist ein Belegarzt?"
         if st.button("Wofür wird die Alpha-ID verwendet?"):
             query = "Wofür wird die Alpha-ID verwendet?"
-
-    with col2:
-        st.header("Examples2")
         if st.button("Was sind die Vorteile des ambulanten operierens?"):
             query = "Was sind die Vorteile des ambulanten operierens?"
+        if st.button("Was kann ich mit dem Prognose-Analyse Toll machen?"):
+            query = "Was kann ich mit dem Prognose-Analyse Toll machen?"
+        if st.button("Was sagt mir die Farbe der Balken der Bevölkerungsentwicklung?"):
+            query = "Was sagt mir die Farbe der Balken der Bevölkerungsentwicklung?"
+        if st.button("Ich habe mein Meta Password vergessen, wie kann ich es zurücksetzen?"):
+            query = ("Ich habe mein Meta Password vergessen, wie kann ich es zurücksetzen?")
 
-    if st.button("Ask") or (not st.session_state['chat_history'] and query) or (st.session_state['chat_history'] and query != st.session_state['chat_history'][-1][1]):
-        st.session_state['chat_history'].append(("User", query, "new"))
+            
+        if st.button("Ask") or (not st.session_state['chat_history'] and query) or (st.session_state['chat_history'] and query != st.session_state['chat_history'][-1][1]):
+            st.session_state['chat_history'].append(("User", query, "new"))
 
-        loading_message = st.empty()
+            loading_message = st.empty()
+            loading_message.text('Bot is thinking...')
 
-        loading_message.text('Bot is thinking...')
-        
-        VectorStore = load_pdf(pdf_file_path)
-        max_tokens = 120
-        chain = load_chatbot(max_tokens=max_tokens)
-        docs = VectorStore.similarity_search(query=query, k=2)
-        
-        with get_openai_callback() as cb:
-            response = chain.run(input_documents=docs, question=query)
+            VectorStore = load_pdf(pdf_path)
+            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)
 
-            # Post-processing to remove incomplete sentences and redundant information
-            filtered_response = remove_incomplete_sentences(response)
-            filtered_response = remove_redundant_information(filtered_response)
-            
-            st.session_state['chat_history'].append(("Bot", filtered_response, "new"))
+            st.session_state['chat_history'].append(("Bot", response, "new"))
 
+            # Display new messages at the bottom
             new_messages = st.session_state['chat_history'][-2:]
             for chat in new_messages:
                 background_color = "#FFA07A" if chat[2] == "new" else "#acf" if chat[0] == "User" else "#caf"
                 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)
 
+            # Scroll to the latest response using JavaScript
             st.write("<script>document.getElementById('response').scrollIntoView();</script>", unsafe_allow_html=True)
 
             loading_message.empty()
 
+            # Clear the input field by setting the query variable to an empty string
             query = ""
-    else:
-        st.warning("Please enter a query before asking questions.")
 
-    st.session_state['chat_history'] = [(sender, msg, "old") for sender, msg, _ in st.session_state['chat_history']]
-    
+        # Mark all messages as old after displaying
+        st.session_state['chat_history'] = [(sender, msg, "old") for sender, msg, _ in st.session_state['chat_history']]
+
+
+
+def display_chat_history(chat_history):
+    for chat in chat_history:
+        background_color = "#FFA07A" if chat[2] == "new" else "#acf" if chat[0] == "User" else "#caf"
+        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)
 
 if __name__ == "__main__":
-    main()
+    main()