{"id":17760886,"url":"https://github.com/abhimanyu-gaurav/portfolio","last_synced_at":"2026-04-11T09:03:06.637Z","repository":{"id":258992487,"uuid":"868924897","full_name":"Abhimanyu-Gaurav/Portfolio","owner":"Abhimanyu-Gaurav","description":"This repository contains my personal portfolio website, showcasing my skills and projects as a Software Developer. Built using FastAPI, python,Bootstrap, and HTML/CSS,Docker. This portfolio highlights my expertise in Python, FastAPI, and web scraping. 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It features sections like skills, education, work experience, and contact information, along with social media links and a simple dark-themed design.\n\n**Feature:**\n- Responsive design\n- Integration with Google Analytics\n- Auto deployment pipeline with Render\n- Contact form or project showcase\n\n---   \n\n## Technologies Used\n- **python**: \n- **FastAPI**: Backend framework\n- **Docker**: To deploy or run\n- **Git \u0026 GitHub**: To push and deploy\n- **Bootstrap**: CSS framework for responsive design\n- **FontAwesome**: For social media icons\n- **Google Analytics**: For tracking user activity\n\n---   \n\n## Setup\n\n1. **Clone the repository**:\n   ```bash\n   git clone https://github.com/yourusername/your-portfolio.git\n   cd your-portfolio\n\n2. **Install dependencies**: Create a virtual environment and install dependencies from the requirements.txt file:\n    ```bash\n        python -m venv env\n        source env/bin/activate  # On Windows: env\\Scripts\\activate\n        pip install -r requirements.txt\n\n3. **Google Analytics Integration**:\n    If you want to track visitor activity using Google Analytics, follow these steps:\n\n - Go to Google Analytics.\n- Create an account and a property for your portfolio.\n- Get the Tracking ID (starts with \"UA-\").\n- Add the following script to the head section of your index.html file (or equivalent):\n    ```bash\n        \u003c!-- Google Analytics --\u003e\n        \u003cscript async src=\"https://www.googletagmanager.com/gtag/js?id=YOUR_TRACKING_ID\"\u003e\u003c/script\u003e\n        \u003cscript\u003e\n        window.dataLayer = window.dataLayer || [];\n        function gtag(){dataLayer.push(arguments);}\n        gtag('js', new Date());\n\n        gtag('config', 'YOUR_TRACKING_ID');\n        \u003c/script\u003e\n\n- Replace YOUR_TRACKING_ID with your actual tracking ID.\n- Deploy your portfolio, and Google Analytics will start collecting data.    \n\n4. **Configure Docker**:  Build your Docker image\n    ```bash\n    docker build -t fastapi-portfolio:v1.0 .\n\n5. **Run the Docker container**:\n    ```bash\n    docker run -d -p 8000:8000 --name portfolio -v my_volume:/app/data fastapi-portfolio:v1.0\n\n---   \n\n## How to Run\n1. Locally: \n- Run FastAPI using uvicorn:\n     ```bash\n     uvicorn app:app --reload \n\n2. API will be running at:\n    ```bash\n    http://127.0.0.1:8000/\n\n3. Open your browser (Safari, Chrome, Brave) and enter the URL:\n   ```bash\n   http://localhost:8000/\n\n---   \n\n## Docker Setup\n**To containerize and run the portfolio using Docker, follow these steps:**\n\n1. Build Docker Image:\nEvery time you make changes to your code, you need to create a new Docker image. Run the following command to build a new image:\n    ```bash  \n    docker build -t fastapi-portfolio:v1.0 .\n\n2. Run Docker Container:\nAfter building the image, use the following command to run the Docker container:\n    ```bash  \n    docker run -d -p 8000:8000 --name portfolio -v my_volume:/app/data fastapi-portfolio:v1.0\n\n- -d runs the container in detached mode.\n- -p 8000:8000 maps the container’s port 8000 to your local machine’s port 8000.\n- -v my_volume:/app/data mounts a volume for persistent storage (optional based on your needs).\n\n3. Access the Application:\nOnce the container is running, navigate to http://localhost:8000 in your browser to view the portfolio.\n\n---   \n\n## Auto Deployment\n- This portfolio is automatically deployed on Render whenever new changes are pushed to the main branch on GitHub.\n\n**How It Works:**\n1. Auto-Trigger: When you push your code to GitHub, Render detects the changes and triggers a new deployment.\n2. Live Update: The updated portfolio goes live once the deployment is complete.\n\n**To set up a similar auto-deployment system on Render:**\n1. Create an account on Render.\n2. Connect your GitHub repository.\n3. Set up a new web service, and Render will handle the rest.\n\n--- \n\n## License\n- This project is licensed under the MIT License - see the [License](License) file for details.\n\n---    \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhimanyu-gaurav%2Fportfolio","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fabhimanyu-gaurav%2Fportfolio","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabhimanyu-gaurav%2Fportfolio/lists"}