This project is an innovative, AI-powered medical chatbot designed to help patients understand their medical reports and receive intelligent, personalized health recommendations. Leveraging Google Gemini and a modern full-stack architecture, the chatbot delivers natural, context-aware conversations and actionable medical insights in real time. The chatbot allows users to upload or input details from their medical reports. It then analyzes the content using advanced AI models and provides relevant suggestions, including possible diagnoses, follow-up actions, or general health advice. The system combines the power of large language models with semantic search and a seamless user experience. Key Features: Medical Report Analysis: Users can submit their reports, which the chatbot interprets using NLP and medical data context. Personalized AI Recommendations: The chatbot responds with tailored health insights, based on report content and user input. Conversational AI: Natural dialogue powered by Google Gemini and enhanced with Hugging Face's Inference API for additional ML capabilities. Semantic Search: Pinecone enables fast, accurate retrieval of relevant medical information via vector embeddings. Responsive Frontend: Built using Next.js, styled with Shadcn/ui, and deployed via Vercel, the frontend is clean, responsive, and user-friendly. Modular and Scalable Architecture: The codebase is well-structured, supporting fast iteration and future feature expansion. AI Integration: Powered by Google Gemini, Vercel AI SDK, and Hugging Face Inference API to ensure robust performance and flexibility. Tech Stack: Frontend: Next.js, Shadcn/ui, HTML/CSS Backend/AI: Google Gemini, Hugging Face Inference API, Vercel AI SDK Search/Database: Pinecone (vector database) Deployment: Vercel