Projects

GitResume is a production-ready web application designed to automatically generate professional, ATS-optimized resume content directly from a developer's GitHub repositories. By simply providing a repository URL, users can leverage AI to analyze their codebase, identify key technical achievements, and produce impactful, data-driven narrative sections for their resumes. CORE FEATURES & FUNCTIONALITY 1. AI-Powered Content Generation: At its core, GitResume uses AI models to transform repository data into compelling resume sections. This includes a project title, a list of technologies, and achievement-focused bullet points. 2. Intelligent Code Analysis: The application clones a user's repository and uses the tree-sitter library to parse the code, identify the tech stack, analyze the structure, and extract key metrics like function and class counts. 3. Job Description Tailoring: Users can input a job description to generate resume content that is specifically tailored to the requirements of the role, integrating relevant keywords naturally. 4. In-depth Project Insights: Beyond bullet points, the tool generates additional notes on technical setups, future plans for the project, and potential advancements. 5. Interview Preparation: To help users prepare for interviews, the application generates a list of potential technical and behavioral questions based on the repository's content, complete with comprehensive answers. 6. Real-time Streaming UI: The generation process is streamed to the user in real-time via WebSockets, showing the current status from cloning and analyzing to generating the final output. 7. Secure GitHub Authentication: Users can log in via GitHub OAuth to securely analyze their private repositories. TECHNICAL ARCHITECTURE & STACK 1. Backend: The application is built with FastAPI and Python 3.11. It handles user authentication, repository analysis, and communication with AI services. The backend also features robust error handling and logging. 2. Frontend: The user interface is rendered using Jinja2 templates and styled with Tailwind CSS (via CDN) for a clean, retro-modern aesthetic. The frontend is designed to be responsive and accessible. 3. AI & Code Analysis: * AI Providers: Modular support for multiple AI providers including Gemini, OpenAI, Groq, and Claude, which can be configured via environment variables. * Code Parsing: Leverages Tree-sitter for efficient and accurate parsing of various programming languages to identify the tech stack and code structure. * Grammar Correction: An integrated AI-powered grammar check ensures the generated text is polished and professional. 4. Caching & Performance: Redis is used for session management and rate limiting to ensure the application is scalable and performant. 5. Containerization: The entire application is containerized using Docker with a multi-stage build process for a minimal and secure production image, served by Uvicorn.

Finance manager is a comprehensive and secure personal finance management application from the ground up. The goal was to create a tool that is not only functional but also intuitive and user-friendly, allowing users to take full control of their financial lives. This application is a testament to my skills in full-stack development, database management, and building secure, scalable web applications. CORE FEATURES & FUNCTIONALITY The Finance Manager is packed with features designed to provide a complete financial overview: 1. Interactive Dashboard: The heart of the application, the dashboard offers an immediate snapshot of the user's financial health. It displays key metrics like total income, expenses, and current balance, along with a chart visualizing income vs. expenses over time and a list of recent transactions. 2. Seamless Transaction Management: Users can effortlessly add, edit, and delete income and expense records. The transaction forms are designed for a smooth user experience, with options for advanced details like payment methods, tags, and notes. 3. Intelligent Budgeting System: The application features a robust budgeting system where users can set monthly budgets for different expense categories. Progress bars and visual cues provide instant feedback on spending, helping users stay on track. 4. In-Depth Financial Reporting: The reports section offers a deep dive into the user's financial data. With dynamic charts, users can analyze monthly trends, break down spending by category and payment method, and even get a cash flow projection. 5. Personalized User Experience: Users can customize the application to their liking, with support for light and dark themes, multiple currencies, and custom date formats. TECHNICAL ARCHITECTURE & STACK I chose a modern and robust tech stack to build this application, ensuring it is both scalable and maintainable: 1. Backend: The application is powered by Python and the Flask web framework. I designed a clean and modular application structure, with a focus on security and performance. The backend handles user authentication, data processing, and serves a RESTful API for the frontend. 2. Database: I used MongoDB as the database, which is a perfect fit for the flexible data structures of financial transactions. I leveraged PyMongo for seamless integration with the Flask application. 3. Frontend: The user interface is built with HTML, CSS, and JavaScript, and styled with the Bootstrap 5 framework for a responsive and modern design. I used Chart.js to create the dynamic and interactive charts that are a core part of the reporting features. 4. Deployment: The entire application is containerized using Docker and Docker Compose, which allows for easy and consistent deployment across different environments. The application is served by Gunicorn, a production-ready WSGI server, ensuring it can handle a high volume of requests. SECURITY CONSIDERATIONS Security was a top priority throughout the development process. I implemented several security measures to protect user data: 1. Secure Authentication: User passwords are not stored in plaintext. Instead, they are hashed using bcrypt, a strong and widely-trusted password hashing algorithm. 2. Rate Limiting: To prevent brute-force attacks on the login and registration pages, I implemented rate limiting using the Flask-Limiter library. 3. Secure Sessions: The application uses secure, HttpOnly session cookies to protect against cross-site scripting (XSS) attacks. 4. Admin Privileges: The application includes a secure admin system for user management. Sensitive admin actions, like granting or revoking admin rights, require password confirmation for an extra layer of security.

For the Cansat India competition, I developed a comprehensive, desktop-based ground control station to provide real-time data visualization, mission control, and post-flight analysis. My goal was to create a robust and high-performance system to ensure we had complete oversight and control of our CanSat from launch to recovery. Here’s a breakdown of how I built it and what I achieved. CORE MISSION: REAL-TIME INSIGHT AND CONTROL The primary challenge was to process a continuous stream of telemetry data from our CanSat's hardware and present it in an intuitive, actionable desktop application. I engineered the system to feature a live dashboard with dynamic charts and gauges, a command interface for mission-critical events, and a robust data logging mechanism for post-flight analysis. ARCHITECTING THE APPLICATION WITH PYTHON & PYQT5 I chose Python for its powerful data handling libraries and built the entire ground control station as a desktop application using the PyQt5 framework. This approach allowed for a highly responsive user interface and direct access to the system's serial ports for hardware communication. 1. Bridging Hardware and Software: I wrote a dedicated serial_handler module using the pyserial library to interface directly with our ground station's hardware. This module runs in a background thread, continuously listening for incoming data packets. To safely update the GUI from this background thread, I used PyQt5's signal and slot mechanism, ensuring the application remained responsive and thread-safe. 2. Command Handling: Sending commands back to the CanSat was a critical feature. I implemented functions in the GUI that, when triggered by button clicks in the mission control panel, would write commands directly to the serial port via the serial_handler, enabling us to trigger events like payload deployment remotely. 3. Robust Data Logging: For post-mission analysis, I created a data_handler module. Every piece of telemetry received is timestamped and appended to a mission-specific CSV file. For post-flight analysis, I integrated pandas for data manipulation and matplotlib to generate and display summary plots directly within the PyQt5 application, which was invaluable for our flight reports. CRAFTING AN INTUITIVE DESKTOP GUI WITH PYQT5 The entire user interface, from windows and buttons to live charts, was built using PyQt5's rich set of widgets. This allowed me to create a native desktop application that felt fast and reliable. 1. Live Data Visualization: I developed a custom dashboard with real-time data visualizations. I used graphing libraries compatible with PyQt5 (like Matplotlib's Qt backend or a dedicated Qt charting library) to create dynamic line graphs for telemetry like altitude and pressure, and custom widgets to act as gauges for instantaneous readings. 2. GPS Tracking Map: To visualize the CanSat's position, I integrated a mapping widget capable of displaying GPS coordinates on a map, which updated in real-time as new data came in. 3. Mission Control Interface: I designed a simple but effective mission control panel within the application where team members could send predefined commands with a single click. The interface provided immediate feedback on command transmission status. ACHIEVEMENTS & OUTCOMES This project was a significant achievement in building a complete, real-time IoT application with a sophisticated desktop GUI. I successfully bridged the gap between raw hardware data and a high-performance user interface. The system provided our team with the critical tools needed for a successful Cansat mission: live monitoring, remote control, and detailed data analysis, all within a self-contained and reliable desktop application.

This project is a comprehensive ground station and telemetry system designed for the Teknofest 2024 Model Satellite Competition. It features a ground station GUI developed with PySide6 for real-time data visualization and a Raspberry Pi-based satellite system for collecting and transmitting telemetry data. The system monitors crucial flight data, including altitude, pressure, temperature, and GPS coordinates, while also providing a 3D visualization of the satellite's orientation. Key Features: 1. Real-time Data Visualization: The ground station displays live telemetry data through various graphs and line edits, allowing for immediate analysis of the satellite's performance. 2. Interactive Map: A Folium-powered map tracks the satellite's GPS location in real-time, plotting its trajectory with a polyline. 3. 3D Orientation Display: A VTK-based widget provides a 3D visualization of the satellite's orientation (roll, pitch, and yaw), offering a more intuitive understanding of its behavior. 4. CSV Data Simulation: A separate GUI tool allows for simulating telemetry data from a CSV file, enabling thorough testing of the ground station's functionality without a live satellite connection. 5. Data Logging: All incoming telemetry data is logged to a CSV file for post-flight analysis and blackbox purposes. 6. Websocket Communication: The ground station and Raspberry Pi communicate via websockets, ensuring a reliable and real-time data link. Technologies Used 1. Backend: * Python: The core language for both the ground station and the Raspberry Pi. * Raspberry Pi: The main computer of the science payload, handling sensor data acquisition and telemetry transmission. * Websockets: Used for real-time, bidirectional communication between the Raspberry Pi and the ground station. * Asyncio: Utilized for concurrent operations in the backend, particularly for handling websocket connections and sensor data streams. * Flask: Used to create a simple web server on the Raspberry Pi for streaming logs and media files. * Adafruit CircuitPython Libraries: Used for interfacing with various sensors like BMP390, TMP117, and BNO055. 2. Frontend (Ground Station): * PySide6: The core GUI framework used to build the ground station application. * Folium: Used for creating the interactive map visualization. * VTK (Visualization Toolkit): Employed for the 3D rendering of the satellite's orientation. * PyQtGraph: Used for plotting the real-time telemetry data in various graphs.