Hafiz Muhammad Usama@muhammad_usama

Code Genie is an AI-powered Streamlit application designed to assist developers, students, and teams in analyzing, reviewing, refactoring, and documenting source code across multiple programming languages. The platform integrates advanced AI capabilities to offer a smarter and more personalized coding assistant experience. At its core, Code Genie leverages the Groq API with the LLaMA model to power a range of intelligent features, including: Code Explanation: Understand complex logic with natural language breakdowns tailored to user skill level. Semantic Search: Find relevant functions, files, or logic based on the meaning of user queries—not just keywords. Workflow Reporting: Automatically generate summaries and insights about code structure and quality. Error Detection: Identify potential bugs, anti-patterns, or inefficient implementations. Unit Test Generation: Create boilerplate or advanced test cases automatically based on existing functions. To complement these features, Blackbox AI API is used for: Code Refactoring: Clean up, optimize, and restructure code based on AI-powered analysis. Additionally, the Blackbox AI agent was used via its official Visual Studio Code extension—not as a built-in integration—to automatically generate inline code comments. This feature supported the documentation module by providing accurate and context-aware comments based on code structure. Every page in Code Genie requires users to select: Programming Language Skill Level (Beginner, Intermediate, Expert) Developer Role (e.g., Backend, Frontend, QA, Student) Preferred Explanation Language (e.g., English, Urdu) These inputs allow Code Genie to personalize all outputs—ensuring that code insights, suggestions, and explanations are context-aware and user-friendly. By combining Groq’s high-speed LLaMA models with Blackbox AI tools, Code Genie provides a robust, AI-powered environment for improving code quality, understanding, and productivity.

In today’s interconnected world, internet access is crucial for education, healthcare, and economic development. Unfortunately, many regions still suffer from inadequate connectivity, restricting their access to these essential services. This project aims to tackle this issue by identifying and ranking underserved regions based on internet connectivity and socio-economic factors using AI-powered solutions.The primary goal of this project is to develop a robust AI model capable of identifying underserved areas and ranking them based on factors such as: Internet connectivity Population density Socio-economic status By pinpointing these underserved regions, we aim to assist policymakers in prioritizing network expansion and resource allocation in a way that maximizes impact.

E-commerce platforms often face overwhelming customer support demands, from tracking orders to processing returns and answering product queries. These tasks are resource-intensive, particularly for small and medium-sized businesses. The AI-Driven Customer Support Assistant addresses this issue by automating customer interactions, improving efficiency, and enhancing the shopping experience. The assistant uses conversational AI to provide real-time order tracking, handle returns and refunds, offer personalized product recommendations, and notify customers about promotions. With multilingual 24/7 support, it caters to a global audience. The chatbot connects seamlessly with e-commerce APIs to retrieve accurate data, ensuring customers receive quick, reliable assistance. Built for scalability, the assistant can handle peak traffic periods and integrates effortlessly with existing systems. A sleek, interactive UI ensures a user-friendly experience, while its modular design supports future expansion into more complex functionalities. By reducing support costs and boosting customer satisfaction, this solution empowers businesses to focus on growth while delivering exceptional service.