app.py

import gradio as gr
import torch
from torchvision import datasets, models, transforms
from PIL import Image

LABELS = ['Fiat 500', 'VW Up!']

model = models.resnet18(pretrained=True)
num_ftrs = model.fc.in_features

model.fc = torch.nn.Linear(num_ftrs, 2)

state_dict = torch.load('up500Model.pt', map_location='cpu')
model.load_state_dict(state_dict)
model.eval()

title = "VW Up! or Fiat 500"
description = "Demo for classification of automobiles. To use it, simply upload your image, or click one of the examples to load them."

imgTransforms = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])

def predict(inp):
    inp = Image.fromarray(inp.astype('uint8'), 'RGB')
    inp = imgTransforms(inp).unsqueeze(0)
    with torch.no_grad():
        prediction = torch.nn.functional.softmax(model(inp)[0]) 
    return {LABELS[i]: float(prediction[i]) for i in range(2)}
    
examples = [['fiat500.jpg'],['VWUP.jpg']]

interface = gr.Interface(predict, inputs='image', outputs="label", title=title, description=description, examples=examples, cache_examples=False)
interface.launch()