M7-Equalizer/Numbers_Classification

1import numpy as np2from tensorflow.keras.models import load_model3import cv24import gradio as gr5 6model =  load_model('accuracy_ 0.9956 - 0.9908.h5')7def probs(x):8  probs=np.copy(x)9  dict_={}10  for i in range(3):11    index = np.argmax(probs)12    dict_[str(index)] = probs[index]13    probs[index]= np.min(probs)14  return dict_15def classify(input):16  input = input['composite'].astype(np.float32)17  input = input / 255.018  prediction = model.predict(np.expand_dims(input, axis=0))[0]19  result = probs(prediction)20  return result,input21 22label = gr.Label(num_top_classes=3)23sk = gr.Sketchpad(image_mode='L',crop_size = (28,28), brush= gr.Brush(colors=["#FFFFFF"],default_size=10),type='numpy',elem_classes="sket")24 25interface = gr.Interface(fn=classify, inputs=sk, outputs=[label,'image'],live=True,theme="Monochrome",26                         css=".sket {background: rgba(0, 0, 0, 0.25)};")27interface.launch(inline=False)