🤖 CallMate – AI-Powered Ticket Management System

AI-driven customer support solution that automates ticket categorization, sentiment analysis, and priority handling for faster response times and improved customer satisfaction.

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📌 Table of Contents

1. Overview
2. Core Features
3. Tech Stack
4. Project Structure
5. Getting Started
6. Future Enhancements

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📖 Overview

CallMate is a Django-based ticket management platform enhanced with Machine Learning and NLP.
It automatically categorizes incoming tickets, analyzes sentiment, and prioritizes issues to ensure the most urgent cases are resolved first.

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🚀 Core Features

• Automated Categorization – Classifies tickets into Billing, Technical, General, Complaint.
• Sentiment Analysis – Uses NLTK’s VADER to detect tone (Positive, Negative, Neutral).
• Dynamic Priority Scoring – Combines urgency level & sentiment score for ranking.
• Prioritized Dashboard – Sorted ticket list for instant access to critical issues.
• One-Click Agent Assignment – Bulk-assign tickets to agents by category.
• Responsive UI – Built with Bootstrap 5 for a seamless experience.

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🛠 Tech Stack

Backend: Django
Frontend: HTML, Bootstrap 5, Django Templates
Database: SQLite 3
ML & NLP: Scikit-learn, NLTK (VADER), Joblib
Language: Python

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🛠 5. How to Run the Project

Since the project is not deployed, you can run it locally by following these steps:

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Step 1 – Clone the Repository

git clone <your-repository-url>
cd callmate-project

Step 2 – Set Up a Virtual Environment

For macOS/Linux

python3 -m venv venv
source venv/bin/activate

For Windows

python -m venv venv
.\venv\Scripts\activate

Step 3 – Install Required Packages

pip install -r requirements.txt

Step 4 – Train the Machine Learning Model

python tickets/train_model.py

Step 5 – Run Database Migrations

python manage.py migrate

Step 6 – Start the Development Server

python manage.py runserver

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🚀 Future Enhancements

• User Authentication – Add secure login for customers and agents to enable personalized access and role-based features.
• Real-time Updates – Implement Django Channels or integrate React/Vue for live dashboard refresh when new tickets arrive.
• Advanced Analytics – Build a dedicated analytics dashboard to track KPIs like category distribution, resolution time, and agent performance.
• Email Notifications – Send automated emails to customers when a ticket is submitted and to agents when assigned.
• Enhanced ML Model – Retrain using a larger, more diverse dataset for higher accuracy and broader category prediction.
• Database Upgrade – Switch from SQLite to PostgreSQL for better scalability and production readiness.