Sidra Hameed@Sidra812

An application that leverages neural networks to predict the likelihood of heart disease in users based on their health data. Features User Input: Users can input personal health metrics such as age, gender, blood pressure, cholesterol levels, and other relevant medical history. Neural Network Model: Implement a pre-trained neural network model that processes the input data to predict heart disease risk. The model can be based on existing datasets and algorithms from sources like Kaggle. Real-Time Feedback: Provide users with immediate feedback on their health status, including risk assessment and suggestions for lifestyle changes. Data Visualization: Include graphs and charts to help users understand their health metrics and the prediction results. Educational Resources: Offer information on heart disease, prevention strategies, and when to seek medical advice.

We are excited to present our project, which focuses on addressing emergencies and environmental issues through an advanced AI-driven solution. In this hackathon, our team has developed an application that can generate accurate responses to a variety of emergency scenarios and environmental challenges. Project Overview: Model and Dataset: We utilized the LLaMA 3.1 model with 405B parameters to generate a synthetic dataset of approximately 2,000 question-answer pairs. This dataset was initially created in Excel and later converted into JSON format for model training. The TinyLLaMA 1.1 billion parameter chat version was fine-tuned using this dataset, allowing our model to provide highly contextual and relevant responses. Training and Fine-Tuning: We leveraged the resources available on Google Colab, specifically using T4 GPUs to generate the dataset. We leveraged the resources available on Kaggle, specifically using T4 x2 GPUs to train our model. After completing the fine-tuning process, we pushed the model to Hugging Face, making it accessible for deployment and further testing. Deployment: The model was deployed on Hugging Face Spaces, where we integrated a user-friendly Gradio UI interface. This interface enables users to input queries and receive real-time responses directly from the model. All project files and necessary documentation have been committed to our repository, ensuring full transparency and accessibility. Team: Our project was made possible by the collaborative efforts of a dedicated team of six members: Team Lead: Umar Majeed LinkedIn Profile Team Members: Moazzan Hassan LinkedIn Shahroz Butt LinkedIn Sidra Hammed LinkedIn Muskan Liaqat LinkedIn Sana Qaisar LinkedIn We would like to thank LabLab AI for this opportunity, and we look forward to the impact our application can make in real-world scenarios.