346 lines
12 KiB
Plaintext
346 lines
12 KiB
Plaintext
{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": [],
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"authorship_tag": "ABX9TyMlDthhM8w5NIUNYffwmHfr",
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"include_colab_link": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/sngo/llms-practice/blob/main/taskmanagement/TaskManagement.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"id": "GM-U1_ArmVFO"
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},
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"outputs": [],
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"source": [
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"!pip install -q gradio>=4.0.0"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"import gradio as gr\n",
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"import openai\n",
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"import json\n",
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"import html\n",
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"from openai import OpenAI\n",
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"import random\n",
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"import datetime\n",
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"from google.colab import userdata"
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],
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"metadata": {
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"id": "FciiJrKSmfOV"
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},
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"execution_count": 8,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"api_key = userdata.get('OPENAI_API_KEY')\n",
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"client = OpenAI(api_key=api_key)\n",
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"model = \"gpt-4.1-mini\""
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],
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"metadata": {
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"id": "7fPLICmznVur"
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},
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"execution_count": 24,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"def abcd_taskTool():\n",
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" tasks = [\n",
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" {\"taskId\": \"T001\", \"accNo\": \"1234567890\", \"status\": \"Pending\", \"description\": \"Update account details\"},\n",
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" {\"taskId\": \"T002\", \"accNo\": \"9876543210\", \"status\": \"In Progress\", \"description\": \"Verify transaction\"}\n",
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" ]\n",
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"\n",
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" probability_of_tasks = 0.8\n",
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" if random.random() < probability_of_tasks:\n",
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" return tasks\n",
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" else:\n",
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" return []"
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],
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"metadata": {
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"id": "zyXLchykmoW_"
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},
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"execution_count": 35,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"def abcd_NotifyTool():\n",
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" # Mock notification function\n",
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" return True"
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],
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"metadata": {
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"id": "h4J8HOOJm78X"
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},
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"execution_count": 11,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# Tool definitions for OpenAI\n",
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"tools = [\n",
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" {\n",
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" \"type\": \"function\",\n",
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" \"function\": {\n",
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" \"name\": \"abcd_taskTool\",\n",
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" \"description\": \"Retrieve a list of ABCD tasks.\",\n",
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" \"parameters\": {}\n",
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" }\n",
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" },\n",
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" {\n",
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" \"type\": \"function\",\n",
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" \"function\": {\n",
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" \"name\": \"abcd_NotifyTool\",\n",
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" \"description\": \"Notify the support team about tasks.\",\n",
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" \"parameters\": {}\n",
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" }\n",
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" }\n",
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"]"
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],
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"metadata": {
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"id": "KOtTI6PunNJ7"
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},
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"execution_count": 12,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# Function to mask first four digits of accNo\n",
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"def mask_accNo(accNo):\n",
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" return \"****\" + accNo[4:] if len(accNo) >= 4 else accNo"
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],
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"metadata": {
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"id": "p7wU9CRBnTAS"
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},
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"execution_count": 13,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# Function to format tasks as an HTML table\n",
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"def format_tasks_as_table(tasks):\n",
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" if not tasks:\n",
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" return \"No tasks found.\"\n",
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"\n",
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" table = \"<table border='1' style='border-collapse: collapse; width: 100%;'>\"\n",
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" table += \"<tr><th>Task ID</th><th>Account Number</th><th>Status</th><th>Description</th></tr>\"\n",
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" for task in tasks:\n",
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" table += \"<tr>\"\n",
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" table += f\"<td>{html.escape(task.get('taskId', ''))}</td>\"\n",
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" table += f\"<td>{html.escape(mask_accNo(task.get('accNo', '')))}</td>\"\n",
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" table += f\"<td>{html.escape(task.get('status', ''))}</td>\"\n",
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" table += f\"<td>{html.escape(task.get('description', ''))}</td>\"\n",
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" table += \"</tr>\"\n",
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" table += \"</table>\"\n",
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" return table"
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],
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"metadata": {
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"id": "b4GLrDLyntA7"
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},
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"execution_count": 14,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# Chat function to handle user input and bot responses with tool calling\n",
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"def chat_function(message, history):\n",
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" # Append user message to history\n",
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" print(f\"User message: {message}\")\n",
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"\n",
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" history.append([message, None])\n",
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" print(f\"History: {history}\")\n",
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"\n",
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" # Prepare messages for OpenAI API\n",
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" messages = [{\"role\": \"system\", \"content\": (\n",
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" \"You are a helpful assistant. When the user asks about checking ABCD tasks, call the abcd_taskTool function. \"\n",
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" \"If tasks are returned, ask the user if they want to notify the support team. \"\n",
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" \"If the user agrees to notify, call the abcd_NotifyTool function. \"\n",
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" \"If no tasks are found, respond with a cheerful message. \"\n",
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" \"If the user declines notification, respond creatively.\"\n",
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" )}]\n",
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" for user_msg, bot_msg in history[:-1]:\n",
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" if user_msg:\n",
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" messages.append({\"role\": \"user\", \"content\": user_msg})\n",
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" if bot_msg:\n",
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" messages.append({\"role\": \"assistant\", \"content\": bot_msg})\n",
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" messages.append({\"role\": \"user\", \"content\": message})\n",
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"\n",
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" # Handle state for notification response\n",
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" state = history[-2][1] if len(history) > 1 else None\n",
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" if state and \"Would you like to notify the support team?\" in state:\n",
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" if message.lower() in [\"yes\", \"y\", \"yeah\", \"sure\", \"ok\", \"notify\", \"yep\"]:\n",
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" # Simulate tool call for notification\n",
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" notify_result = abcd_NotifyTool()\n",
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" response = \"The support team has been notified successfully! They'll take it from here. 😊\"\n",
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" history[-1][1] = response\n",
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" yield history, \"normal\", \"\"\n",
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" return\n",
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" else:\n",
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" response = (\n",
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" \"Alright, we'll hold off on notifying the team. 🌟 \"\n",
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" \"How about we explore something fun, like planning your next big adventure?\"\n",
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" )\n",
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" history[-1][1] = response\n",
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" yield history, \"normal\", \"\"\n",
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" return\n",
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"\n",
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" try:\n",
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" # Call OpenAI API with tool calling\n",
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" response = client.chat.completions.create(\n",
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" model= model,\n",
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" messages=messages,\n",
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" tools=tools,\n",
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" tool_choice=\"auto\",\n",
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" max_tokens=300\n",
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" )\n",
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"\n",
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" # Process response\n",
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" response_message = response.choices[0].message\n",
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" tool_calls = response_message.tool_calls\n",
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"\n",
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" if tool_calls:\n",
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" for tool_call in tool_calls:\n",
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" function_name = tool_call.function.name\n",
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" if function_name == \"abcd_taskTool\":\n",
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" tasks = abcd_taskTool()\n",
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" task_count = len(tasks)\n",
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"\n",
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" if task_count > 0:\n",
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" table = format_tasks_as_table(tasks)\n",
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" response = (\n",
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" f\"There are {task_count} tasks found:<br>{table}<br>\"\n",
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" \"Would you like to notify the support team? (Yes/No)\"\n",
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" )\n",
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" history[-1][1] = response\n",
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" print(f\"History after tool call: {history}\")\n",
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" print(f\"History at -1, 1: {history[-1][1]}\")\n",
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" yield history, \"waiting_for_notify_response\", \"\"\n",
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" else:\n",
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" response = (\n",
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" \"Hooray! No ABCD tasks to worry about! 🎈 \"\n",
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" \"You're free as a bird—any fun plans for the day?\"\n",
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" )\n",
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" history[-1][1] = response\n",
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" yield history, \"normal\", \"\"\n",
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" elif function_name == \"abcd_NotifyTool\":\n",
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" abcd_NotifyTool()\n",
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" response = \"The support team has been notified successfully! They'll take it from here. 😊\"\n",
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" history[-1][1] = response\n",
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" yield history, \"normal\", \"\"\n",
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" else:\n",
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" # No tool calls, use the LLM's response\n",
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" bot_response = response_message.content\n",
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" history[-1][1] = bot_response\n",
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" yield history, \"normal\", \"\"\n",
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"\n",
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" except Exception as e:\n",
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" history[-1][1] = f\"Error: {str(e)}\"\n",
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" yield history, \"normal\", \"\""
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],
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"metadata": {
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"id": "m9MN8vFDnwsA"
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},
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"execution_count": 48,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# Custom CSS for table and chat styling\n",
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"custom_css = \"\"\"\n",
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"table {\n",
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" font-family: Arial, sans-serif;\n",
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" margin: 10px 0;\n",
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"}\n",
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"th, td {\n",
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" padding: 8px;\n",
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" text-align: left;\n",
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"}\n",
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"th {\n",
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" background-color: #f2f2f2;\n",
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"}\n",
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"tr:nth-child(even) {\n",
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" background-color: #f9f9f9;\n",
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"}\n",
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"\"\"\""
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],
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"metadata": {
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"id": "X3Egf6jAn3uP"
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},
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"execution_count": 31,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# Create Gradio interface\n",
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"with gr.Blocks(css=custom_css) as demo:\n",
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" chatbot = gr.Chatbot()\n",
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" msg = gr.Textbox(placeholder=\"Type your message here...\")\n",
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" state = gr.State(value=\"normal\")\n",
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"\n",
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" msg.submit(\n",
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" chat_function,\n",
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" inputs=[msg, chatbot],\n",
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" outputs=[chatbot, state, msg]\n",
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" )"
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],
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"metadata": {
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"id": "g9ctQlBOoDvi"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"demo.launch(debug=True, share=True)"
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],
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"metadata": {
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"id": "DG5gjlXloHhj"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [],
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"metadata": {
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"id": "GiWUiQNJxPSq"
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},
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"execution_count": null,
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"outputs": []
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}
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]
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} |