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+# shapeAI
+
+_Forked from [@quentinbkk's shape ai](https://github.com/quentinbkk/shapeAI)_
+
+Shape AI is a web app utilizing a classifier model to identify user drawn geometric shapes. Currently, the model can identify drawn squares, rectangles, circles, and triangles.
+
+**[Try it live ✏️](https://shapeai.craisin.tech)**
+
+## Running the App 🏃
+### Docker 🐋
+```bash
+git clone https://github.com/craisined/shapeAI
+cd shapeAI
+docker build -t shapeAI .
+docker run -p 3000:3000 shapeAI
+```
+### Manually 🖥️
+```bash
+git clone https://github.com/craisined/shapeAI
+cd shapeAI
+python3 -m venv env
+source env/bin/activate
+cd app
+pip install -r requirements.txt
+gunicorn -b 0.0.0.0:3000 app:app
+```
+
+## Model Training 💪
+OpenCV is used to synthetically generate training data in ```training/generate_shapes.py```.
+
+Data is stored in ```training/data``` - add and modify the folder to add training cases.
+
+Run ```training/train.py``` to train the model - exports to ```shape_model.keras```.
+
+## Technical Overview 👨‍💻
+### Abilities
+1. High training accuracy - model consistently trains with accuracy > 99%
+2. Fast speed - model has sub 50ms response times
+3. Synthetic data and preproccessing - generates training data and sends user drawing from website to language model
+### Frameworks
+1. Model built with Tensorflow and Keras
+2. Image manipulation built using OpenCV and Pillow
+3. Backend built using Flask
+4. Frontend built using vanilla HTML, CSS, JS
+### Changes from original fork
+1. Web UI and Flask backend added
+2. Synthetic training data altered to produce a more human friendly model
+### WIP
+1. Low accuracy on certain cases - further improve synthetic shape generation
+2. Add confidence for classification - do not display a result if confidence is low
+3. Imporve mobile UI to further prevent scroll while drawing
+4. Expand dataset to various alphanumerical characters