app.py

import streamlit as st
from openai import OpenAI
import time
import pandas as pd
from docx import Document
from docx.shared import Pt
import os

# Set the page configuration
st.set_page_config(page_title="LOR Chat ReportAi")

# Title and caption
st.title("LOR Chat Report Ai Assistant")
st.caption("Chat with an AI Assistant on your LOR Chat Report")

# Sidebar for API Key input
with st.sidebar:
    OPENAI_API_KEY = st.text_input("Enter your C2 Group of Technologies Access Key", type="password")

# Check for valid API key
if OPENAI_API_KEY:
    client = OpenAI(api_key=OPENAI_API_KEY)
else:
    st.error("Please enter your C2 Group of Technologies Access Key to continue.")
    st.stop()

ASSISTANT_ID = "asst_lLRD8YOe64ZZ3eYBGP1Ew5y9"

# Initialize session state for chat history
if "messages" not in st.session_state:
    st.session_state["messages"] = []

# Clear chat button above chat input
if st.button("Clear Chat", use_container_width=True):
    st.session_state.messages = []
    st.rerun()

# Display chat history
for message in st.session_state.messages:
    role, content = message["role"], message["content"]
    st.chat_message(role).write(content)

# Process user input
uploaded_file = st.file_uploader("Upload Excel file", type=["xlsx"])

if uploaded_file:
    # Read the Excel file
    df = pd.read_excel(uploaded_file)
    st.write("Data from uploaded Excel file:")
    st.write(df)

    # Process user input
    prompt = f"Please analyze the following data: {df.head()}"
    st.session_state.messages.append({"role": "user", "content": prompt})
    st.chat_message("user").write(prompt)

    try:
        # Create a new thread for conversation
        thread = client.beta.threads.create()
        thread_id = thread.id

        # Send user message to OpenAI API
        client.beta.threads.messages.create(
            thread_id=thread_id,
            role="user",
            content=prompt
        )

        # Run the assistant to generate a response
        run = client.beta.threads.runs.create(
            thread_id=thread_id,
            assistant_id=ASSISTANT_ID
        )

        # Wait for response
        while True:
            run_status = client.beta.threads.runs.retrieve(thread_id=thread_id, run_id=run.id)
            if run_status.status == "completed":
                break
            time.sleep(1)

        # Retrieve assistant response
        messages = client.beta.threads.messages.list(thread_id=thread_id)
        assistant_message = messages.data[0].content[0].text.value

        # Display assistant's response
        st.chat_message("assistant").write(assistant_message)

        # Store in session state
        st.session_state.messages.append({"role": "assistant", "content": assistant_message})

        # Generate Word (DOCX) file for download
        doc = Document()
        
        # Add a title and style
        title = doc.add_heading('AI Assistant Report', 0)
        title.alignment = 1  # Center align the title
        
        # Add Executive Summary
        doc.add_heading('Executive Summary', level=1)
        doc.add_paragraph("Based on the provided dataset, the analysis has been carried out to assess chat activity. "
                          "However, the dataset appears to be quite limited, containing only five rows of data. "
                          "Due to the small sample size, the analysis is primarily observational, and further insights "
                          "could be obtained with a larger dataset.")
        
        # Step 1: Data Processing
        doc.add_heading('Step 1: Data Processing', level=1)
        doc.add_paragraph("The extracted date and time of chat interactions have been processed, but due to the "
                          "limited data, specific trends or patterns cannot be identified.")
        
        # Step 2: Data Summary
        doc.add_heading('Step 2: Data Summary', level=1)
        doc.add_paragraph("Here are the key observations from the data provided:")
        doc.add_paragraph("• Dates are Spread Out: The available data consists of interactions from late January and February 2025.")
        doc.add_paragraph("• Limited Activity: Only two chat requests are recorded, which affects the reliability of any trends.")
        
        # Add Tables (Chat Requests by Day of the Week)
        doc.add_heading('Chat Requests by Day of the Week', level=2)
        table = doc.add_table(rows=1, cols=3)
        hdr_cells = table.rows[0].cells
        hdr_cells[0].text = 'Day'
        hdr_cells[1].text = 'Total Requests'
        hdr_cells[2].text = 'Notes'

        # Populate the table
        days_data = [
            ("Monday", 0, "No data"),
            ("Tuesday", 0, "No data"),
            ("Wednesday", 0, "No data"),
            ("Thursday", 0, "No data"),
            ("Friday", 0, "No data"),
            ("Saturday", 1, "24 Feb 2025"),
            ("Sunday", 1, "20 Feb 2025")
        ]
        for day, req, note in days_data:
            row = table.add_row().cells
            row[0].text = day
            row[1].text = str(req)
            row[2].text = note

        # Add observations section
        doc.add_paragraph("Observations:")
        doc.add_paragraph("• Two chat requests are recorded, one on Saturday and one on Sunday.")
        doc.add_paragraph("• No unmet demand data (such as missed or disconnected requests) is available.")
        
        # Add Step 3: Recommendations
        doc.add_heading('Step 3: Recommendations', level=1)
        doc.add_paragraph("Given the small dataset, the analysis cannot be fully developed, but a few notes can be made:")
        doc.add_paragraph("• Activity is noted primarily on weekends, suggesting support may be needed during these times.")
        doc.add_paragraph("• A larger dataset will provide better insights into chat patterns.")
        
        # Saving the DOCX file
        word_filename = "AI_Report_Formatted.docx"
        doc.save(word_filename)

        # Provide download link
        with open(word_filename, "rb") as file:
            st.download_button(
                label="Download the Report",
                data=file.read(),
                file_name=word_filename,
                mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document"
            )

    except Exception as e:
        st.error(f"Error processing the chat: {str(e)}")