app.py

from flask import Flask, request, render_template
import numpy as np

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Conv2D, Flatten, Dense, Dropout
from tensorflow.keras.metrics import Precision, Recall, TopKCategoricalAccuracy
from tensorflow.keras.optimizers import Adamax



#  Replace this with any version of interest: (Available: 45, 49 and 50 )
version = 50
WEIGHTS_PATH = f"Weights/v_{version}.weights.h5"


model = Sequential([
    Conv2D(16, (3,3), activation='relu', input_shape=(28, 28, 1)),
    MaxPooling2D(2,2),
    Conv2D(64, (3,3), activation='relu'),
    MaxPooling2D(2,2),
    Flatten(),
    Dropout(0.2),
    Dense(128, activation='relu'),
    Dropout(0.2),
    Dense(64, activation='relu'),
    Dropout(0.2),
    Dense(10, activation='softmax') 
])
model.compile(
    optimizer=Adamax(0.001),
    loss='categorical_crossentropy',
    metrics=['accuracy',  TopKCategoricalAccuracy(3), Precision(), Recall()]
)

model.load_weights(WEIGHTS_PATH) 

app = Flask(__name__)

classes = [i for i in range(10)]

def label(pred):
    return {classes[i]: float(pred[0][i]) for i in range(len(classes))}

@app.route('/')
def home():
    return render_template('index.html')

@app.route('/classify', methods=['POST'])
def classify():
    drawing = request.get_json()['drawing']
    drawing = np.array(drawing)
    pred = model.predict(np.expand_dims(drawing, axis=0).astype(np.float16), verbose=0)[0].astype(np.float64)
    return {classes[i]: pred[i] for i in range(10)}
    
if __name__ == '__main__':
    app.run()