Shinjan Saha, Satyabrata Das Adhikari and Abesh Chakraborty
This web-based health assistant predicts the likelihood of multiple diseases—Breast Cancer, Brain Tumor, Pneumonia, Diabetes, and Heart Disease—based on user input. Built using Flask, Machine Learning, and enhanced with a Gemini-powered AI chatbot, this system offers quick and interactive diagnostics support.
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Shinjan Saha
- Built and trained model for heart disease detection
- Integrated all trained ML models into the Flask backend
- Created interactive and user-friendly forms for tabular predictions
- Integrated Gemini API to add a smart chatbot assistant
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Fellow team members
- Built and trained models for diabetes, breast cancer, pneumonia, and brain tumor detection
- Preprocessed datasets and saved model pipelines using
joblib
- Supported UI testing and feedback integration
- 🔍 Predicts 5 diseases from medical data or uploaded images
- 📊 Tabular form inputs with sliders, dropdowns, and radio buttons
- 🧠 Gemini chatbot for symptom-based health conversations
- 💾 Model files saved using
joblib
for seamless deployment - 💡 Clean, responsive design with modern UI/UX
- Backend: Flask
- Frontend: HTML5, CSS3 (Jinja2 Templating)
- ML Tools: scikit-learn, joblib, pandas, TensorFlow
- Chatbot: Gemini API (Google Generative AI)
- Deployment Ready: GitHub integrated
🚀 How to Run
Clone the repo:
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git clone https://github.com/Code-r4Life/Disease-prediction-app.git
Navigate to the project folder:
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cd disease-prediction-app
(Optional) Create a virtual environment
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python -m venv venv
source venv/bin/activate # For Linux/Mac
.\venv\Scripts\activate # For Windows
Install dependencies:
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pip install -r requirements.txt
Add your .env file containing the Gemini API key:
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GEMINI_API_KEY=your_key_here
Run the app:
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python app.py
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