Tayyaba Mustafa@Tayyabamustafa

Kirana+ is a cutting-edge prototype developed to revolutionize the way local stores interact with customers through WhatsApp. Using IBM Watsonx, Kirana+ simplifies the processes of store registration, product listing, and order management. Local businesses can easily register their stores and list their products, making them accessible to customers directly via WhatsApp. Customers can browse product catalogs, place orders, and receive updates—all through a familiar messaging platform. This solution enhances local commerce by providing a user-friendly interface for both businesses and consumers, aiming to improve efficiency and accessibility in local shopping experiences. Developed within a tight two-day timeframe, Kirana+ showcases the potential for AI-driven solutions to transform traditional retail practices.

EchoAI is an innovative AI-driven platform designed to revolutionize social media content creation. By harnessing advanced natural language processing techniques, it generates tailored content that reflects a brand's unique voice and personality. Key Features: Brand Voice Integration: EchoAI effectively mimics the tone and style of a brand, ensuring consistency across all social media platforms. This feature allows businesses to maintain their identity while creating engaging posts. Content Generation: Users can provide brief prompts or ideas, and EchoAI generates relevant and captivating content. Whether it's tweets, Instagram captions, or longer posts, the platform caters to various social media needs. Customizable Parameters: The tool offers adjustable settings for content length, creativity (temperature), and post type, allowing for a personalized content creation experience tailored to specific requirements. User-Friendly Interface: EchoAI features an intuitive design that is accessible to users of all skill levels, enabling quick and effortless content generation. Real-Time Preview: Users receive an instant preview of generated content, allowing for immediate adjustments and refinements to better suit their preferences. Use Cases: EchoAI serves a diverse range of users, including social media managers who streamline content creation for multiple clients, small business owners looking to maintain an active online presence, and content creators or influencers aiming to enhance engagement with their audience. Benefits: The platform automates the content creation process, saving users valuable time while enhancing audience interaction. It ensures consistency in brand voice across various platforms and easily adapts to increasing content needs as businesses grow.

The AI wellness assistant is a personalized platform designed to support mental and physical well-being. By tracking mood through sentiment analysis, it offers real-time recommendations to improve emotional health and resilience. The app generates customized fitness plans based on individual goals and fitness levels, promoting physical wellness through achievable exercises. Additionally, it provides guided meditation and relaxation techniques tailored to users' emotional states, helping manage stress and anxiety. The combination of mood tracking, fitness guidance, and mindfulness practices empowers users to maintain a healthy balance between mind and body, fostering long-term emotional and physical wellness.

EmergencyCore is an advanced disaster management system designed to help emergency responders by providing real-time disaster analysis, weather predictions, and damage assessments. Key Features: Disaster Report Analysis: Users input disaster reports with urgency levels. The system analyzes the report using Grok AI to classify the disaster, assess severity, and provide safety recommendations. Weather Forecasting: Users can get weather predictions for any location, including risk assessments and safety tips based on weather conditions. Damage Assessment: Users upload images showing disaster damage. The system processes the image to assess the extent of the damage and offers initial feedback for further action. Aid Recommendation System: The app predicts the type of aid required based on factors like deaths, disaster duration, and cost, using a trained Random Forest model. User Interface: Built with Gradio, the app features an intuitive interface for easy reporting, weather requests, and damage image uploads. Technologies Used: Python: Main programming language for backend. Grok AI: Analyzes disaster reports and generates insights. Gradio: Creates the interactive interface for user input and displaying results. Scikit-learn: Used for training machine learning models, such as the Random Forest model for aid recommendations. Pandas: Manages and processes disaster data for machine learning. How It Works: Disaster Report Analysis: The user inputs a report, which is processed by Grok to classify the disaster and suggest necessary actions. Weather Forecasting: The app provides location-based weather forecasts with risk assessments. Damage Assessment: Users upload images of the damage, and the system evaluates them, suggesting follow-up actions.

AstroCleanAI is an advanced AI-powered space debris tracking and risk prediction system designed to enhance space safety. With the increasing amount of space junk threatening satellites and future missions, managing debris is more critical than ever. AstroCleanAI leverages artificial intelligence to detect, classify, and predict debris behavior, ensuring proactive collision avoidance and efficient satellite operations. 🛰️ Key Features: Real-Time Space Debris Detection & Classification – Uses deep learning models (YOLOv5, ResNet-50) to analyze satellite imagery and radar data, identifying debris based on size, trajectory, and risk level. AI-Powered Collision Risk Prediction – Implements an XGBoost-based risk assessment model to predict satellite-debris collision probabilities and suggest optimal avoidance maneuvers. Interactive Space Debris Simulation – Visualizes real-world debris movement and potential collision risks using 3D orbital mechanics modeling. Public Engagement Dashboard – Allows researchers, students, and enthusiasts to explore real-time space debris tracking and AI-driven insights. 🛠️ Technology Stack: AI & Machine Learning: TensorFlow, PyTorch, YOLOv5, ResNet-50, XGBoost Space Data & Analysis: NASA’s Open TLE Data, OpenCV for satellite imagery processing Web & Visualization: Streamlit, Matplotlib (3D trajectory simulation), Flask backend 🌍 Impact & Future Scope: AstroCleanAI contributes to sustainable space exploration by mitigating debris-related risks, preventing costly satellite damage, and optimizing space traffic management. Future enhancements may include integrating real-world satellite telemetry, collaborating with space agencies for improved predictions, and developing an autonomous debris mitigation system capable of orchestrating deorbiting maneuvers.