Installation Guide
SymptIQ — AI Disease Prediction Using Symptoms | Django Final Year Project
Back to ProjectInstallation Guide
Follow the steps below to set up SymptIQ on your local machine. The application requires Python 3.10 or newer and pip installed on your system.
Prerequisites
- Python 3.10 or newer
- pip (Python package manager)
- Jupyter Notebook (for model training via notebook)
- A terminal or command prompt
Step 1 — Navigate to the Project Folder
After downloading or cloning the project, open your terminal and navigate to the project root directory.
cd "AI diease Prediction using Sympotm"
Step 2 — Create and Activate a Virtual Environment
It is recommended to use a virtual environment to keep dependencies isolated.
Create the environment:
python -m venv venv
Activate on macOS / Linux:
source venv/bin/activate
Activate on Windows:
venv\Scripts\activate
Step 3 — Install Dependencies
Install all required Python packages listed in the requirements file.
pip install -r requirements.txt
Step 4 — Apply Database Migrations
Run Django migrations to initialise the SQLite database and create the required tables.
python manage.py migrate
Step 5 — Create a Superuser (Optional)
To access the Django admin panel and manage prediction logs and contact messages, create an admin account.
python manage.py createsuperuser
Step 6 — Train the Machine Learning Models
The ML models must be trained before the prediction feature becomes available. Model files are saved to the ml_models/ directory.
Option A — Jupyter Notebook (Recommended)
cd training
jupyter notebook model_training.ipynb
Open the notebook and run all cells from top to bottom. The notebook will train Naive Bayes, Logistic Regression, and Random Forest classifiers, evaluate them, and export the following files to ml_models/:
- best_model.pkl
- symptoms_list.pkl
- label_encoder.pkl
- disease_info.pkl
- training_summary.json
- Visualisation PNG charts
Option B — Standalone Training Script
python train.py
Step 7 — Run the Development Server
Once training is complete, start the Django development server.
python manage.py runserver
Visit the application in your browser at:
http://127.0.0.1:8000
Admin Panel
Access the Django admin interface to manage prediction logs and contact messages.
http://127.0.0.1:8000/admin/
Log in with the superuser credentials you created in Step 5.
REST API Endpoints
SymptIQ exposes three JSON API endpoints for programmatic access. No authentication is required.
- POST /api/predict/ — Submit selected symptoms and receive top disease predictions with confidence scores
- GET /api/symptoms/?q= — Search and retrieve matching symptoms from the 377-symptom list
- GET /api/chart-data/?type= — Fetch chart data for disease distribution, symptom frequency, model performance, and prediction trends
Production Deployment
To deploy SymptIQ on a production server, follow these additional steps.
python manage.py collectstatic
export SECRET_KEY="your-secure-secret-key"
export DEBUG=False
export ALLOWED_HOSTS="yourdomain.com"
pip install gunicorn
gunicorn symptiq.wsgi:application --bind 0.0.0.0:8000
Note: If the ML model files are missing from the ml_models/ directory, the Predict page will display a warning and the Analyse button will be disabled. Always complete Step 6 before running the server.
Need Help?
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