harshinivijay68/smart-ai-healthcare-

Smart-AI-Healthcare is a web-based application that takes symptom information from a user and tries to predict what disease they might have, using machine learning models trained on symptom and disease datasets. It is framed as a healthcare assistant for early awareness, not as a diagnostic tool, and the README describes it as a student or research project rather than a clinical product. The application accepts symptoms in three ways: typed text, voice input, and image uploads. It supports multiple languages for the conversation, though the README does not specify which ones. Once symptoms are entered, the system returns a predicted disease name, a severity level, dietary recommendations, suggested precautions, and directions to nearby hospitals via Google Maps. It can also generate a PDF summary of the health report for the user to save or share. The technical stack is Python on the backend using FastAPI, with scikit-learn handling the machine learning predictions and ReportLab generating the PDF reports. The frontend is plain HTML, CSS, and JavaScript. To run it locally, you clone the repository, install dependencies, start the FastAPI server with uvicorn, and open the index.html file in a browser. The README is concise and does not go into detail about the training data, the specific machine learning models used, or how accurate the predictions are. The project structure shows folders for backend code, datasets, trained models, and a frontend directory, but no further file-level documentation is provided.