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.github/workflows/node.js.yml

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# This workflow will do a clean installation of node dependencies, cache/restore them, build the source code and run tests across different versions of node
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# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-nodejs
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name: Node.js CI
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on:
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push:
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branches: [ "main" ]
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pull_request:
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branches: [ "main" ]
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jobs:
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build:
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runs-on: ubuntu-latest
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strategy:
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matrix:
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node-version: [14.x, 16.x, 18.x]
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# See supported Node.js release schedule at https://nodejs.org/en/about/releases/
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steps:
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- uses: actions/checkout@v4
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- name: Use Node.js ${{ matrix.node-version }}
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uses: actions/setup-node@v3
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with:
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node-version: ${{ matrix.node-version }}
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cache: 'npm'
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- run: npm ci
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- run: npm run build --if-present
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- run: npm test

.github/workflows/webpack.yml

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name: NodeJS with Webpack
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on:
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push:
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branches: [ "main" ]
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pull_request:
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branches: [ "main" ]
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jobs:
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build:
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runs-on: ubuntu-latest
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strategy:
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matrix:
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node-version: [14.x, 16.x, 18.x]
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steps:
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- uses: actions/checkout@v4
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- name: Use Node.js ${{ matrix.node-version }}
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uses: actions/setup-node@v3
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with:
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node-version: ${{ matrix.node-version }}
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- name: Build
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run: |
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npm install
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npx webpack

README.md

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# EpiDetect
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# EpiDetect
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EpiDetect is a web application developed using the MERN stack, designed to predict skin diseases from images captured using your web camera. It uses a fine-tuned ResNet50 model for accurate skin disease detection. The application also supports document upload and summarization with a Q&A chat feature, providing comprehensive functionality for users.
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## Features
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- **User Profile Management**: Users can view and update their profile information.
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- **Skin Disease Prediction**: Upload or capture images using the web camera to predict skin diseases.
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- **Dashboard**: Access different functionalities from a central dashboard.
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- **Blog Creation**: Users can create and manage their blog posts.
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- **Contact Form**: Users can send messages through the contact form.
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- **Prediction Records**: View and download prediction records in PDF format.
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## Screenshots
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### User Profile
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![User Profile](./path_to_your_image/Screenshot 2024-06-07 175359.png)
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### Predict Skin Disease
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![Predict Skin Disease](./path_to_your_image/Screenshot 2024-06-07 175417.png)
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### Contact Us
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![Contact Us](./path_to_your_image/Screenshot 2024-06-07 175530.png)
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### Dashboard
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![Dashboard](./path_to_your_image/Screenshot 2024-06-07 175542.png)
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### Prediction Records
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![Prediction Records](./path_to_your_image/Screenshot 2024-06-07 175600.png)
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### Create Blog Post
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![Create Blog Post](./path_to_your_image/Screenshot 2024-06-07 175612.png)
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### Login
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![Login](./path_to_your_image/Screenshot 2024-06-07 175638.png)
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## Technology Stack
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- **Frontend**: React.js, HTML, CSS
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- **Backend**: Node.js, Express.js
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- **Database**: MongoDB
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- **Machine Learning**: Python, TensorFlow, Keras (ResNet50 model)
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- **Other Tools**: JWT for authentication, multer for file uploads
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## Setup Instructions
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### Prerequisites
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- Node.js
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- MongoDB
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- Python
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- TensorFlow and Keras
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### Installation
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1. Clone the repository:
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```sh
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git clone https://github.com/fatimaazfar/EpiDetect.git
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```
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2. Navigate to the project directory:
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```sh
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cd EpiDetect
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```
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3. Install backend dependencies:
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```sh
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cd backend
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npm install
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```
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4. Install frontend dependencies:
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```sh
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cd ../frontend
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npm install
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```
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5. Set up environment variables for backend:
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- Create a `.env` file in the `backend` directory.
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- Add the following environment variables:
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```env
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MONGO_URI=your_mongodb_connection_string
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JWT_SECRET=your_jwt_secret
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```
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6. Run the backend server:
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```sh
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cd ../backend
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npm start
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```
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7. Run the frontend server:
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```sh
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cd ../frontend
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npm start
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```
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8. Access the application at `http://localhost:3000`.
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## Usage
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1. **User Registration and Login**: Register a new account or login with existing credentials.
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2. **Profile Management**: View and update your profile information.
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3. **Skin Disease Prediction**:
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- Navigate to the `Predict` page.
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- Upload an image or capture one using your web camera.
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- Click on `Predict` to get the prediction results.
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4. **Document Upload and Summarization**:
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- Navigate to the `Dashboard`.
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- Use the document upload feature to upload PDF documents.
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- Get summaries and ask questions based on the document content.
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5. **Blog Creation**: Navigate to the `Create Blog` page to create and manage your blog posts.
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6. **Contact Us**: Use the contact form to send messages.
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7. **Prediction Records**: View and download your prediction records in PDF format.
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## Contributing
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Contributions are welcome! Please fork the repository and submit a pull request for any improvements or bug fixes.
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## License
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This project is licensed under the MIT License. See the [LICENSE](./LICENSE) file for details.
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## Contact
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For any inquiries, please contact Fatima Azfar at [email protected].

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