This project aims to develop an IoT and AI-based scalp disease detection system using an ESP32 microcontroller. The system will collect scalp image data using a camera activated by a LIDAR sensor and a buzzer, then analyze the data using machine learning to provide diagnosis and treatment recommendations.
as note, it only can run locally!
you have to installed some libraries needed:
- cv2
- firebase
- firebase-admin
- streamlit
- google.generativeai
- tensorflow
etc...
- firebasedata2.ipynb = running firebase to get and upload data to firebase from sensor
- scalp_learn.ipynb = model that had been created for scalp classification
- deons.py = streamlit web for running the app locally (cause it needs to access camera hardware
- Dataset-Image = dataset that we use for training the model
- datatrain = data train for predictions