Muhammad Talha@MuhammadTalha

"Virtual AI Patient" is a new way dedicated to medical students in order to help them practice on their virtual patient so that they can act more professional and much faster when they face a similar cases in the reality.

An virtual AI Psychotherapist which Helps and consoles people who were depressed and became mentally unstable.Speaks with them revive them from their Suicidal Thoughts and gives tasks to keep them engaged and Joyful.Its an Voice to voice AI.

Revolutionizing Financial Analysis with Next Gen Hackathon: In the fast-paced world of finance, staying competitive demands cutting-edge tools and strategies. Our solution, developed during the Next Gen Hackathon, represents a groundbreaking leap in financial analysis. Built on a sophisticated multi-agent framework with specialized roles such as Data Analyst, Trading Strategist, Risk Manager, and Coordinator, it offers a comprehensive approach to navigating financial markets. The central intelligence drives seamless collaboration among agents, enabling real-time market analysis, trend detection, strategy optimization, trade execution, risk management, and task automation—key elements for effective financial decision-making. Our technical approach includes Python for scripting, Jupyter Notebooks for exploration, real-time financial data APIs, and machine learning libraries for predictive analytics. Despite challenges like ensuring data quality and managing real-time processing, our solution excels in delivering precise insights, empowering users to make informed decisions. Looking ahead, we plan to expand our solution to cover more financial markets and asset classes, integrating advanced machine learning models for enhanced predictive accuracy. We are also committed to refining the user interface for broader accessibility, catering to experts and beginners alike. In conclusion, our Next Gen Hackathon solution marks a pivotal shift in financial analysis. As we continue to innovate, we aim to equip businesses and investors with actionable insights and strategic foresight to thrive in the ever-evolving financial landscape.

Multi AI Agents Chatbot Enhancing Research is a revolutionary application designed to transform the research process by leveraging the power of collaborative AI. This innovative chatbot utilizes multiple AI agents to assist researchers in streamlining information gathering, enhancing productivity, and fostering collaboration among team members. By integrating advanced technologies from Replit and Cursor, the app achieves an impressive 10x speed in processing information and responding to inquiries, allowing researchers to focus on critical analysis rather than mundane tasks. Each AI agent specializes in different areas, enabling the chatbot to provide tailored assistance and insights across various domains. Whether it's literature reviews, data analysis, or brainstorming ideas, our chatbot acts as a dynamic research partner, simplifying complex tasks and promoting efficient workflows. The user-friendly interface encourages seamless interaction, making it easy for researchers to ask questions, share documents, and receive real-time feedback. Join us in redefining the future of research with Multi AI Agents Chatbot Enhancing Research, where speed, efficiency, and collaboration meet to unlock new possibilities in your research endeavors.

This innovative app is designed to revolutionize the way users search for and access information by utilizing multiple AI agents to interact seamlessly with external tools such as Wikipedia and various research repositories. The app’s intelligent architecture enables comprehensive search capabilities and efficient retrieval of information, allowing users to quickly pinpoint relevant research papers, articles, and data through a dynamic chatbot interface. Built on the powerful LangGraph framework, this project pushes the boundaries of AI-driven interactions by integrating chatbots directly with knowledge sources, facilitating a rich, research-oriented experience. By bridging AI technology with external databases, it empowers students, researchers, and professionals to access the information they need with unprecedented ease, streamlining their workflows and enhancing productivity. This initiative is a prime example of how advanced AI solutions can transform the traditional methods of information retrieval into an interactive, conversational experience.

In the Edge Runners 3.2 Hackathon, we're creating a Personalized Diabetes Plan app using Meta's advanced Llama 3.2 models. Our approach focuses on three key objectives: Generate Synthetic Data: Leveraging Llama 3.2's capabilities, we create high-quality, diverse datasets to enhance data variety and improve model training. Optimize Models: We train smaller models such as TinyLlama and Phi-3 to maximize their performance on edge devices like smartphones, tablets, and IoT devices. Deploy on Edge Devices: Our cloud prototypes are designed for seamless integration on edge hardware, ensuring efficient local data processing without relying heavily on centralized cloud resources. This project showcases our innovative solution's potential to run sophisticated AI models directly on edge devices, transforming diabetes management into a highly personalized experience.