import gradio as gr


import torch

model = torch.load('v4-epoch=19-val_loss=0.6964-val_accuracy=0.8964.ckpt')

import requests
from PIL import Image
from torchvision import transforms

# Download human-readable labels for ImageNet.
labels = ['good', 'ill']

def predict(inp):
  img = transforms.ToTensor()(inp)
  img = torchvision.transforms.Resize((800, 800))(img)
  img = torchvision.transforms.CenterCrop(CROP)(img)
  img = img.unsqueeze(0)
  with torch.no_grad():
    prediction = torch.nn.functional.softmax(model(img)[0], dim=0)
    confidences = {labels[i]: float(prediction[i]) for i in range(2)}    
  return confidences

import gradio as gr

gr.Interface(fn=predict, 
             inputs=gr.Image(type="pil"),
             outputs=gr.Label(num_top_classes=3),
             ).launch()