app.py

import torch
from model import create_resnet
import numpy as np
import gradio as gr
import os 
from timeit import default_timer as timer
from typing import Tuple, Dict

model = create_resnet()
model.load_state_dict(torch.load(f="ResNet18_epoch-14.pth",
                                    map_location=torch.device("cpu")))

from torchvision import datasets, transforms
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

def predict(img):
  start_time = timer()
  transformed_image = transform(img)
  transformed_image = transformed_image.unsqueeze(0)
  model.eval()

  with torch.no_grad():
    output = model(transformed_image)
    predicted_label = int(torch.sigmoid(output).item())

  end_time = timer()
  pred_time = round(end_time - start_time, 4)
  output = "Good" if predicted_label == 1 else "Bad"

  return output, pred_time

# Gradio Interface
title = "🍋 Lemon Quality Classifier 🍋"
description = "A [ResNet18](https://pytorch.org/vision/main/models/generated/torchvision.models.resnet18.html) computer vision model to classify lemons as good or bad in quality."
article = "Created for practice and learning."

example_list = [["examples/" + example] for example in os.listdir("examples")]

demo = gr.Interface(fn=predict,
                    inputs=gr.Image(type="pil"),
                    outputs=[gr.Label(num_top_classes=1, label="Prediction"),
                             gr.Number(label="Prediction time (s)")],
                    examples=example_list,
                    title=title,
                    description=description,
                    article=article)

demo.launch()