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.env

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+OPENAI_API_KEY=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
+LLAMA_CLOUD_API_KEY=llx-HNSQopbOXxJJ1sP49b5CRvtn4SmGwxCQEPop3jDEJi5IYolz

app.py

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+import os
+import io
+import requests
+import streamlit as st
+from openai import OpenAI
+from PyPDF2 import PdfReader
+import urllib.parse
+from dotenv import load_dotenv
+from openai import OpenAI
+from io import BytesIO
+from streamlit_extras.colored_header import colored_header
+from streamlit_extras.add_vertical_space import add_vertical_space
+from streamlit_extras.switch_page_button import switch_page
+import json
+import pandas as pd
+from st_aggrid import AgGrid, GridOptionsBuilder, GridUpdateMode, DataReturnMode
+import time
+import random
+import aiohttp
+import asyncio
+from PyPDF2 import PdfWriter
+
+load_dotenv()
+
+# ---------------------- Configuration ----------------------
+st.set_page_config(page_title="Building Regulations Chatbot", layout="wide", initial_sidebar_state="expanded")
+# Load environment variables from .env file
+load_dotenv()
+
+# Set OpenAI API key
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+# ---------------------- Session State Initialization ----------------------
+
+if 'pdf_contents' not in st.session_state:
+    st.session_state.pdf_contents = []
+if 'chat_history' not in st.session_state:
+    st.session_state.chat_history = []
+if 'processed_pdfs' not in st.session_state:
+    st.session_state.processed_pdfs = False
+if 'id_counter' not in st.session_state:
+    st.session_state.id_counter = 0
+if 'assistant_id' not in st.session_state:
+    st.session_state.assistant_id = None
+if 'thread_id' not in st.session_state:
+    st.session_state.thread_id = None
+if 'file_ids' not in st.session_state:
+    st.session_state.file_ids = []
+
+
+# ---------------------- Helper Functions ----------------------
+
+def get_vector_stores():
+    try:
+        vector_stores = client.beta.vector_stores.list()
+        return vector_stores
+    except Exception as e:
+        return f"Error retrieving vector stores: {str(e)}"
+
+
+def fetch_pdfs(city_code):
+    url = f"http://91.203.213.50:5000/oereblex/{city_code}"
+    response = requests.get(url)
+    if response.status_code == 200:
+        data = response.json()
+        print("First data:", data.get('data', [])[0] if data.get('data') else None)
+        return data.get('data', [])
+    else:
+        st.error(f"Failed to fetch PDFs for city code {city_code}")
+        return None
+
+
+def download_pdf(url, doc_title):
+    # Add 'https://' scheme if it's missing
+    if not url.startswith(('http://', 'https://')):
+        url = 'https://' + url
+
+    try:
+        response = requests.get(url)
+        response.raise_for_status()  # Raise an exception for bad status codes
+
+        # Sanitize doc_title to create a valid filename
+        sanitized_title = ''.join(c for c in doc_title if c.isalnum() or c in (' ', '_', '-')).rstrip()
+        sanitized_title = sanitized_title.replace(' ', '_')
+        filename = f"{sanitized_title}.pdf"
+
+        # Ensure filename is unique by appending the id_counter if necessary
+        if os.path.exists(filename):
+            filename = f"{sanitized_title}_{st.session_state.id_counter}.pdf"
+            st.session_state.id_counter += 1
+
+        # Save the PDF content to a file
+        with open(filename, 'wb') as f:
+            f.write(response.content)
+
+        return filename
+    except requests.RequestException as e:
+        st.error(f"Failed to download PDF from {url}. Error: {str(e)}")
+        return None
+
+
+# Helper function to upload file to OpenAI
+def upload_file_to_openai(file_path):
+    try:
+        file = client.files.create(
+            file=open(file_path, 'rb'),
+            purpose='assistants'
+        )
+        return file.id
+    except Exception as e:
+        st.error(f"Failed to upload file {file_path}. Error: {str(e)}")
+        return None
+
+
+def create_assistant():
+    assistant = client.beta.assistants.create(
+        name="Building Regulations Assistant",
+        instructions="You are an expert on building regulations. Use the provided documents to answer questions accurately.",
+        model="gpt-4o-mini",
+        tools=[{"type": "file_search"}]
+    )
+    st.session_state.assistant_id = assistant.id
+    return assistant.id
+
+
+def format_response(response, citations):
+    """Format the response with proper markdown structure."""
+    formatted_text = f"""
+{response}
+
+{"### Citations" if citations else ""}
+{"".join([f"- {citation}\n" for citation in citations]) if citations else ""}
+"""
+    return formatted_text.strip()
+
+def response_generator(response, citations):
+    """Generator for streaming response with structured output."""
+    # Yield the main response word by word
+    words = response.split()
+    for i, word in enumerate(words):
+        yield word + " "
+        # Add natural pauses at punctuation
+        if word.endswith(('.', '!', '?', ':')):
+            time.sleep(0.1)
+        else:
+            time.sleep(0.05)
+    
+    # If there are citations, yield them with proper formatting
+    if citations:
+        # Add some spacing before citations
+        yield "\n\n### Citations\n\n"
+        time.sleep(0.1)
+        
+        for citation in citations:
+            yield f"- {citation}\n"
+            time.sleep(0.05)
+
+def chat_with_assistant(file_ids, user_message):
+    print("----- Starting chat_with_assistant -----")
+    print("Received file_ids:", file_ids)
+    print("Received user_message:", user_message)
+
+    # Create attachments for each file_id
+    attachments = [{"file_id": file_id, "tools": [{"type": "file_search"}]} for file_id in file_ids]
+    print("Attachments created:", attachments)
+
+    if st.session_state.thread_id is None:
+        print("No existing thread_id found. Creating a new thread.")
+        thread = client.beta.threads.create(
+            messages=[
+                {
+                    "role": "user",
+                    "content": user_message,
+                    "attachments": attachments,
+                }
+            ]
+        )
+        st.session_state.thread_id = thread.id
+        print("New thread created with id:", st.session_state.thread_id)
+    else:
+        print(f"Existing thread_id found: {st.session_state.thread_id}. Adding message to the thread.")
+        message = client.beta.threads.messages.create(
+            thread_id=st.session_state.thread_id,
+            role="user",
+            content=user_message,
+            attachments=attachments
+        )
+        print("Message added to thread with id:", message.id)
+
+    try:
+        thread = client.beta.threads.retrieve(thread_id=st.session_state.thread_id)
+        print("Retrieved thread:", thread)
+    except Exception as e:
+        print(f"Error retrieving thread with id {st.session_state.thread_id}: {e}")
+        return "An error occurred while processing your request.", []
+
+    try:
+        run = client.beta.threads.runs.create_and_poll(
+            thread_id=thread.id, assistant_id=st.session_state.assistant_id
+        )
+        print("Run created and polled:", run)
+    except Exception as e:
+        print("Error during run creation and polling:", e)
+        return "An error occurred while processing your request.", []
+
+    try:
+        messages = list(client.beta.threads.messages.list(thread_id=thread.id, run_id=run.id))
+        print("Retrieved messages:", messages)
+    except Exception as e:
+        print("Error retrieving messages:", e)
+        return "An error occurred while retrieving messages.", []
+
+    # Process the first message content
+    if messages and messages[0].content:
+        message_content = messages[0].content[0].text
+        print("Raw message content:", message_content)
+
+        annotations = message_content.annotations
+        citations = []
+        seen_citations = set()
+        
+        # Process annotations and citations
+        for index, annotation in enumerate(annotations):
+            message_content.value = message_content.value.replace(annotation.text, f"[{index}]")
+            if file_citation := getattr(annotation, "file_citation", None):
+                try:
+                    cited_file = client.files.retrieve(file_citation.file_id)
+                    citation_entry = f"[{index}] {cited_file.filename}"
+                    if citation_entry not in seen_citations:
+                        citations.append(citation_entry)
+                        seen_citations.add(citation_entry)
+                except Exception as e:
+                    print(f"Error retrieving cited file for annotation {index}: {e}")
+
+        # Create a container for the response with proper styling
+        response_container = st.container()
+        with response_container:
+            message_placeholder = st.empty()
+            streaming_content = ""
+            
+            # Stream the response with structure
+            for chunk in response_generator(message_content.value, citations):
+                streaming_content += chunk
+                # Use markdown for proper formatting during streaming
+                message_placeholder.markdown(streaming_content + "▌")
+            
+            # Final formatted response
+            final_formatted_response = format_response(message_content.value, citations)
+            message_placeholder.markdown(final_formatted_response)
+            
+            return final_formatted_response, citations
+    else:
+        return "No response received from the assistant.", []
+
+
+# ---------------------- Streamlit App ----------------------
+
+# ---------------------- Custom CSS Injection ----------------------
+
+# Inject custom CSS to style chat messages
+st.markdown("""
+    <style>
+    /* Style for the chat container */
+    .chat-container {
+        display: flex;
+        flex-direction: column;
+        gap: 1.5rem;
+    }
+
+    /* Style for individual chat messages */
+    .chat-message {
+        margin-bottom: 1.5rem;
+    }
+
+    /* Style for user messages */
+    .chat-message.user > div:first-child {
+        color: #1E90FF;  /* Dodger Blue for "You" */
+        font-weight: bold;
+        margin-bottom: 0.5rem;
+    }
+
+    /* Style for assistant messages */
+    .chat-message.assistant > div:first-child {
+        color: #32CD32;  /* Lime Green for "Assistant" */
+        font-weight: bold;
+        margin-bottom: 0.5rem;
+    }
+
+    /* Style for the message content */
+    .message-content {
+        padding: 1rem;
+        border-radius: 0.5rem;
+        line-height: 1.5;
+    }
+
+    .message-content h3 {
+        color: #444;
+        margin-top: 1rem;
+        margin-bottom: 0.5rem;
+        font-size: 1.1rem;
+    }
+
+    .message-content ul {
+        margin-top: 0.5rem;
+        margin-bottom: 0.5rem;
+        padding-left: 1.5rem;
+    }
+
+    .message-content li {
+        margin-bottom: 0.25rem;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+page = st.sidebar.selectbox("Choose a page", ["Documents", "Home", "Admin"])
+
+if page == "Home":
+    st.title("Building Regulations Chatbot", anchor=False)
+
+    # Sidebar improvements
+    with st.sidebar:
+        colored_header("Selected Documents", description="Documents for chat")
+        if 'selected_pdfs' in st.session_state and not st.session_state.selected_pdfs.empty:
+            for _, pdf in st.session_state.selected_pdfs.iterrows():
+                st.write(f"- {pdf['Doc Title']}")
+        else:
+            st.write("No documents selected. Please go to the Documents page.")
+
+    # Main chat area improvements
+    colored_header("Chat", description="Ask questions about building regulations")
+
+    # Chat container with custom CSS class
+    st.markdown('<div class="chat-container" id="chat-container">', unsafe_allow_html=True)
+    for chat in st.session_state.chat_history:
+        with st.container():
+            if chat['role'] == 'user':
+                st.markdown(f"""
+                <div class="chat-message user">
+                    <div><strong>You</strong></div>
+                    <div class="message-content">{chat['content']}</div>
+                </div>
+                """, unsafe_allow_html=True)
+            else:
+                st.markdown(f"""
+                <div class="chat-message assistant">
+                    <div><strong>Assistant</strong></div>
+                </div>
+                """, unsafe_allow_html=True)
+                # Use st.markdown to render the assistant's message content
+                st.markdown(chat['content'])
+    st.markdown('</div>', unsafe_allow_html=True)
+
+    # Inject JavaScript to auto-scroll the chat container
+    st.markdown("""
+        <script>
+            const chatContainer = document.getElementById('chat-container');
+            if (chatContainer) {
+                chatContainer.scrollTop = chatContainer.scrollHeight;
+            }
+        </script>
+        """, unsafe_allow_html=True)
+
+    # Chat input improvements
+    with st.form("chat_form", clear_on_submit=True):
+        user_input = st.text_area("Ask a question about building regulations...", height=100)
+        col1, col2 = st.columns([3, 1])
+        with col2:
+            submit = st.form_submit_button("Send", use_container_width=True)
+
+    if submit and user_input.strip() != "":
+        # Add user message to chat history
+        st.session_state.chat_history.append({"role": "user", "content": user_input})
+        
+        if not st.session_state.file_ids:
+            st.error("Please process PDFs first.")
+        else:
+            with st.spinner("Generating response..."):
+                try:
+                    response, citations = chat_with_assistant(st.session_state.file_ids, user_input)
+                    # The response is already formatted, so we can add it directly to chat history
+                    st.session_state.chat_history.append({
+                        "role": "assistant", 
+                        "content": response
+                    })
+                except Exception as e:
+                    st.error(f"Error generating response: {str(e)}")
+
+        # Rerun the app to update the chat display
+        st.rerun()
+
+    # Footer improvements
+    add_vertical_space(2)
+    st.markdown("---")
+    col1, col2 = st.columns(2)
+    with col1:
+        st.caption("Powered by OpenAI GPT-4 and Pinecone")
+    with col2:
+        st.caption("© 2023 Your Company Name")
+
+elif page == "Documents":
+    st.title("Document Selection")
+
+    city_code_input = st.text_input("Enter city code:", key="city_code_input")
+    load_documents_button = st.button("Load Documents", key="load_documents_button")
+
+    if load_documents_button and city_code_input:
+        with st.spinner("Fetching PDFs..."):
+            pdfs = fetch_pdfs(city_code_input)
+            if pdfs:
+                st.session_state.available_pdfs = pdfs
+                st.success(f"Found {len(pdfs)} PDFs")
+            else:
+                st.error("No PDFs found")
+
+    if 'available_pdfs' in st.session_state:
+        st.write(f"Total PDFs: {len(st.session_state.available_pdfs)}")
+
+        # Create a DataFrame from the available PDFs
+        df = pd.DataFrame(st.session_state.available_pdfs)
+
+        # Select and rename only the specified columns
+        df = df[['municipality', 'abbreviation', 'doc_title', 'file_title', 'file_href', 'enactment_date', 'prio']]
+        df = df.rename(columns={
+            "municipality": "Municipality",
+            "abbreviation": "Abbreviation",
+            "doc_title": "Doc Title",
+            "file_title": "File Title",
+            "file_href": "File Href",
+            "enactment_date": "Enactment Date",
+            "prio": "Prio"
+        })
+
+        # Add a checkbox column to the DataFrame at the beginning
+        df.insert(0, "Select", False)
+
+        # Configure grid options
+        gb = GridOptionsBuilder.from_dataframe(df)
+        gb.configure_default_column(enablePivot=True, enableValue=True, enableRowGroup=True)
+        gb.configure_column("Select", header_name="Select", cellRenderer='checkboxRenderer')
+        gb.configure_column("File Href", cellRenderer='linkRenderer')
+        gb.configure_selection(selection_mode="multiple", use_checkbox=True)
+        gb.configure_side_bar()
+        gridOptions = gb.build()
+
+        # Display the AgGrid
+        grid_response = AgGrid(
+            df,
+            gridOptions=gridOptions,
+            enable_enterprise_modules=True,
+            update_mode=GridUpdateMode.MODEL_CHANGED,
+            data_return_mode=DataReturnMode.FILTERED_AND_SORTED,
+            fit_columns_on_grid_load=False,
+        )
+
+        # Get the selected rows
+        selected_rows = grid_response['selected_rows']
+
+        # Debug: Print the structure of selected_rows
+        st.write("Debug - Selected Rows Structure:", selected_rows)
+
+        if st.button("Process Selected PDFs"):
+            if len(selected_rows) > 0:  # Check if there are any selected rows
+                # Convert selected_rows to a DataFrame
+                st.session_state.selected_pdfs = pd.DataFrame(selected_rows)
+                st.session_state.assistant_id = create_assistant()
+                with st.spinner("Processing PDFs and creating/updating assistant..."):
+                    file_ids = []
+
+                    for _, pdf in st.session_state.selected_pdfs.iterrows():
+                        # Debug: Print each pdf item
+                        st.write("Debug - PDF item:", pdf)
+
+                        file_href = pdf['File Href']
+                        doc_title = pdf['Doc Title']
+
+                        # Pass doc_title to download_pdf
+                        file_name = download_pdf(file_href, doc_title)
+                        if file_name:
+                            file_path = f"./{file_name}"
+                            file_id = upload_file_to_openai(file_path)
+                            if file_id:
+                                file_ids.append(file_id)
+                            else:
+                                st.warning(f"Failed to upload {doc_title}. Skipping this file.")
+                        else:
+                            st.warning(f"Failed to download {doc_title}. Skipping this file.")
+
+                    st.session_state.file_ids = file_ids
+                st.success("PDFs processed successfully. You can now chat on the Home page.")
+            else:
+                st.warning("Select at least one PDF.")
+        
+
+elif page == "Admin":
+    st.title("Admin Panel")
+    st.header("Vector Stores Information")
+
+    vector_stores = get_vector_stores()
+    json_vector_stores = json.dumps([vs.model_dump() for vs in vector_stores])
+    st.write(json_vector_stores)
+
+    # Add a button to go back to the main page
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

requirements.txt

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+streamlit==1.25.0
+openai==0.28.0
+PyPDF2==3.0.1
+python-dotenv==1.0.0
+streamlit-extras==0.1.8
+requests==2.31.0
+pandas==2.1.1
+st-aggrid==0.3.3
+aiohttp==3.8.5
+asyncio==4.0.0