app.py

import gradio as gr
import torch
import torch.nn as nn
import gradio as gr
import cv2
from net import Net
    
soft_layer = nn.Softmax()
model_dir = "./model/mnist.pth"
checkpoint = torch.load(model_dir, map_location="cpu")
state_dict = checkpoint.get("state_dict", checkpoint)
model = Net()
model.load_state_dict(state_dict )

 
def classify(input):
    gray = cv2.cvtColor(input, cv2.COLOR_BGR2GRAY)
    resized = cv2.resize(gray, (28,28), interpolation = cv2.INTER_AREA)
    resized_t = torch.tensor(resized)
    resized_t = torch.unsqueeze(resized_t, 0)
    resized_t = torch.unsqueeze(resized_t, 0)
    resized_t = (resized_t -128 )/128.
    return 'label: {}'.format(torch.argmax( soft_layer( model(resized_t))).numpy())
    # return "Hello " + name + "!"

demo = gr.Interface(fn=classify, inputs="image", outputs="text")
# demo.launch(debug=True)
demo.launch()