import torch
import gradio as gr
from torch import nn
from torch.nn import functional as F
import torchvision
from PIL import Image
from torchvision import transforms


transformer = transforms.Compose([
    transforms.Resize((256, 256)),
   # transforms.RandomHorizontalFlip(),
    #transforms.RandomRotation(degrees=10),
    transforms.ToTensor()
    #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225
    #])
])
model=torch.jit.load('model.pt',map_location=torch.device('cpu'))
#model=torch.jit.load('model1.pt')
classes=['Minivan Car', 'Muscle Car ', 'Sedan Car', 'Sports Car', 'None of the Above class']

def predict(inp):
  inp=transformer(inp).unsqueeze(0)
  #inp = transforms.ToTensor()(inp).unsqueeze(0)
  
  with torch.no_grad():
  
    prediction =F.softmax(model(inp)[0], dim=0)
    confidences = {classes[i]: float(prediction[i]) for i in range(5)}    
  return confidences
  # gr.Interface(fn=predict, inputs=gr.Image(type="pil"),outputs=gr.Label(num_top_classes=4),title='Image classification',interpretation='default').launch(debug='True')
gr.Interface(predict, gr.inputs.Image(type="pil"),outputs='label',title='Image classification').launch(debug='True')