Transport Management System Using Python and AI Chatbot

The Transport Management System Using Python and AI Chatbot is a web-based application developed using Django, Python, SQLite, HTML, CSS, and JavaScript. It is designed to organize transport operations, including vehicle management, driver records, compliance monitoring, maintenance, fuel logs, expenses, procurement, and dispatches.

The project provides a custom administration panel, a read-only user portal, dashboards, reports, CSV import and export, and a machine-learning chatbot. These features help organize transport records, monitor safety requirements, and access operational information from a centralized application.

The interface uses the Inter font, a sidebar, a top navigation bar, global search, light and dark modes, and responsive layouts. The project uses SQLite as its configured database.

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Project Overview

Project Detail	Description
Project Name	Transport Management System Using Python and AI Chatbot
Programming Language	Python
Framework	Django
Database	SQLite
Frontend	HTML, CSS, JavaScript
Charts	Chart.js
Machine Learning	Scikit-learn, TF-IDF, Logistic Regression, NLTK
Recommended Python Version	Python 3.10–3.12
Project Type	Web Application

Features of Transport Management System

1. Custom Admin Panel

The project includes a custom administration panel available at /panel/. It manages transport-related records through a structured interface rather than Django’s built-in admin site.

Company Management: Manage oil marketing companies and transport companies.
Vehicle Makers and Owners: Maintain manufacturer, vehicle owner, and lessor records.
Vehicle Registry: Manage vehicle registration numbers, TL numbers, capacity, chambers, make and model, engine and chassis numbers, leasing information, configuration, and vehicle status.
Compliance Records: Track insurance, fitness certificates, road tax, permits, registration, dip charts, driving licences, and CNIC records.
Maintenance Management: Maintain vehicle maintenance records and due dates.
Fuel Logs and Expenses: Record fuel usage, fuel costs, and operating expenses.
Procurement and Dispatches: Manage fuel procurement and transport dispatch records.
Driver Management: Maintain driver details, company assignments, licence information, contact details, and training records.
Safety Management: Manage annual training, emergency drills, toolbox meetings, violations, safety policies, and emergency procedures.

2. Record Management and Data Utilities

The application’s management modules provide common record-handling tools.

Add, view, edit, and delete records, with confirmation dialogs for deletion.
Search records and apply filters and sorting.
Navigate records using pagination.
Export supported records to CSV files.
Import records using CSV files and provided templates.
Print supported record views.
Validate submitted data on the server.
Use helper utilities for matching driver IDs, updating driver ages, and importing driver records with images.

3. Dashboard and Charts

The dashboard displays operational information calculated from the SQLite database.

Clickable KPI cards.
Eight Chart.js charts.
Alerts related to transport and compliance records.
Latest dispatch information.

4. Alerts and Notifications

The application identifies important expiry and overdue conditions and provides a notification centre with read and unread status.

Expired and expiring vehicle insurance, fitness, tax, permit, registration, and dip chart records.
Driver licence and CNIC expiry alerts.
Overdue maintenance alerts.
Inactive vehicle alerts.
Alerts for drivers without valid annual training.

5. Driver Safety and Compliance

The driver management functionality includes driver profiles and safety-related information.

Driver profiles with photographs.
Compliance scores from 0 to 100, with a breakdown.
Driver training history.
Violation records.
Motorway training information.
Driver licence and CNIC validity tracking.

6. Reports

The system includes seven reports for reviewing transport operations and compliance information.

Fleet compliance report.
Driver compliance report.
Expiring documents report.
Dispatch summary report.
Fuel and operating cost report.
Violations report.
Maintenance report.

Supported reports include CSV export and print functionality.

7. Role-Based Access

Role	Access
Admin	Full access, including deletion, user management, and chatbot analytics.
Manager	Add, edit, import, and export records; cannot delete records.
User	Read-only portal at `/portal/`, without financial, violation, or CNIC data.

How Does the AI Chatbot Work?

The project includes a machine-learning chatbot available at /chatbot/. It uses natural language processing and an intent classifier to understand supported questions and retrieve answers from live database queries.

Training Dataset: chatbot/data/intents.json contains 32 intents and approximately 400 training phrases.
Text Processing: NLTK RegexpTokenizer and PorterStemmer process text, remove stop words, and normalize identifiers.
Feature Extraction: Scikit-learn’s TF-IDF vectorizer uses one-word and two-word features.
Intent Classification: Logistic Regression predicts the user’s intent.
Confidence Fallback: Questions below the configured confidence threshold receive a fallback response and suggestions.
Conversation History: Chat interactions are stored through the ChatLog model.
Chatbot Analytics: View analytics, review unanswered questions, and retrain the model.
Permission Checks: Access to private information is checked according to the user’s role.

Example questions include “Which documents are expiring soon?”, “Show overdue maintenance”, “Recent dispatches”, and “What to do in case of fire”. Managers and administrators can also ask supported questions about expenses, fuel procurement, violations, and expired CNIC records.

How to Download

Get This Project

The complete package is available so you can run, study, and submit it with confidence. It includes:

Full Source Code
Project Report
Synopsis
PPT Presentation

Screenshot

How to Run the Project in VS Code

Follow these steps to set up and run the project on Windows using PowerShell.

Step 1: Install Python and VS Code

Install Python 3.10–3.12 and Visual Studio Code. Make sure Python is added to PATH.

Step 2: Extract and Open the Project

Extract the ZIP file and open the project folder containing manage.py in VS Code. Select Terminal → New Terminal.

Step 3: Create and Activate a Virtual Environment

python -m venv venv
.\venv\Scripts\Activate.ps1

Step 4: Upgrade pip and Install Dependencies

python -m pip install --upgrade pip
pip install -r requirements.txt

Step 5: Create Database Migrations

python manage.py makemigrations core chatbot

Step 6: Apply Database Migrations

python manage.py migrate

Step 7: Load Demo Data

python manage.py seed_demo

Step 8: Train the Chatbot

python manage.py train_chatbot

Step 9: Run the Tests (Optional)

python manage.py test

Step 10: Start the Server

python manage.py runserver

Open the following address in your browser:

http://127.0.0.1:8000/

Role	Username	Password
Admin	`admin`	`Admin@12345`
Manager	`manager`	`Manager@12345`
User	`user`	`User@12345`

These are the demo credentials listed in the project README. The seeding command loads demo data and can be run again without creating duplicate demo records.

Frequently Asked Questions (FAQs)

1. What is the Transport Management System Using Python and AI Chatbot?

It is a Django web application for managing transport companies, vehicles, drivers, compliance, maintenance, fuel, expenses, procurement, dispatches, and safety records. It also includes a machine-learning chatbot.

2. Which technologies are used in this project?

The project uses Python, Django, SQLite, HTML, CSS, JavaScript, Chart.js, scikit-learn, NLTK, and Joblib.

3. Which database does the project use?

The supplied project is configured to use SQLite, with the database file created in the project directory when migrations are applied.

4. What are the available user roles?

The system includes Admin, Manager, and User roles. Admins have full access, managers can add, edit, import, and export records without deleting them, and regular users have read-only access to permitted portal information.

5. How can I run the project locally?

Create a virtual environment, install the requirements, generate and apply migrations, run the demo-data and chatbot-training commands, and start the server with python manage.py runserver.

Conclusion

The Transport Management System Using Python and AI Chatbot combines transport record management with compliance monitoring, driver safety, maintenance, fuel and expense tracking, procurement, dispatches, reports, and notifications. Its role-based access and machine-learning chatbot provide additional ways to manage and retrieve information within the application.