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Anthracnose  \n  - Bacterial Canker  \n  - Cutting Weevil  \n  - Die Back  \n  - Gall Midge  \n  - Healthy  \n  - Powdery Mildew  \n  - Sooty Mould  \n- 📊 Displays prediction confidence score  \n- 💡 Provides disease-specific treatment suggestions  \n- 🖥️ User-friendly, responsive design  \n- 🧠 Model built on **ResNet50 transfer learning** with TensorFlow/Keras  \n\n---\n\n## 🛠️ Tech Stack\n- **Frontend:** HTML, CSS, JavaScript  \n- **Backend:** Python, Flask, Flask-CORS  \n- **Deep Learning:** TensorFlow, Keras, ResNet50 (Transfer Learning)  \n- **Storage/Reports:** JSON (training history \u0026 evaluation metrics)  \n\n---\n\n## ⚙️ Setup Instructions\n\n1. **Clone the repository**\n   - git clone https://github.com/vignesh-p3007/MangoMedix.git\n   - cd MangoMedix\n\n2. **Create a virtual environment (recommended)**\n     - python -m venv venv\n     - source venv/bin/activate   # for Linux/Mac\n     - .\\venv\\Scripts\\activate    # for Windows\n\n3. **Install dependencies**\n     - pip install -r requirements.txt\n\n4. **Run the web app**\n      - python backend/app.py\n\n5. **Access in browser**\n      - http://127.0.0.1:5000/\n    \n---\n\n## Screenshots\n\nHere are some screenshots of the MangoMedix web application:\n\n### 1. Home Page\n![Home Page](screenshots/home_page.png)\n\n### 2. Upload Leaf Image\n![Upload Page](screenshots/upload_page.png)\n\n### 3. Prediction Result\n![Prediction Result](screenshots/prediction_result.png)\n\n---\n\n### Pre-trained Model\n\nThe trained model `disease_detector.h5` (~271 MB) is hosted externally due to GitHub file size limits.  \n\n**Download the model here:** [Google Drive Link](https://drive.google.com/file/d/1IR_KaRTu36PF7MKqXWnoakgL6z2iGNpX/view?usp=drive_link)  \n\n**Instructions:**\n1. Download the `disease_detector.h5` file from the link above.\n2. Place the file in the `backend/` folder of the MangoMedix project.\n3. Run the web application as usual:\n   \n  python backend/app.py\n\n---\n\n## Demo\n\nWatch the MangoMedix web app in action:\n\n**Demo Video:** [Click Here to View](https://drive.google.com/file/d/1vjyj6GXkkCFYufw_3UGDOo0Vt-6c4dT2/view?usp=drive_link)  \n\n---\n## 🚀 Live Demo\n\nCheck out the live demo of MangoMediX here:  \n👉 [Click to View](https://huggingface.co/spaces/vignesh-p3007/MangoMediX)\n\n---\n\n## 📊 Results \u0026 Accuracy\n\n- Achieved 92% prediction accuracy using ResNet50 with transfer learning\n- Early detection helps prevent disease spread and optimize resource usage\n- Scalable to larger datasets and adaptable for other crops\n\n---\n\n## 🚀 Future Improvements\n\n- Expand dataset to include more diseases\n- Mobile-friendly version for farmers\n- Deploy on cloud (Render, Hugging Face Spaces, or AWS)\n- Add notifications for disease alerts\n- Multilingual support\n\n---\n\n## 📜 License\n\nThis project is licensed under the MIT License.\n\n---\n\n## 👨‍💻 Authors\n\n- [Vignesh](https://www.linkedin.com/in/vignesh-p3007)  \n- [Kshitij R Amin](https://www.linkedin.com/in/kshitij-r-amin-9a2274208/)  \n- [Nitheesh Ishwar Naik](https://www.linkedin.com/in/nitheeshnaik)  \n- [Suhas S](https://www.linkedin.com/in/suhas-s-2773082a6/)  \n\n**Guided by:** Mrs. Usha C S, Assistant Professor, AJIET Mangalore\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvignesh-p3007%2Fmangomedix","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvignesh-p3007%2Fmangomedix","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvignesh-p3007%2Fmangomedix/lists"}