app.py

import gradio as gr
import torch
from model import model, classes
from torchvision import transforms
import torch.nn.functional as F

checkpoint = torch.load('model.pth', map_location=torch.device('cpu'))

# Load the state dictionary into model
model.load_state_dict(checkpoint['model_state_dict'])

# Set model to evaluation mode
model.eval()

# Transforms
transform = transforms.Compose([
    transforms.ToPILImage(),
    transforms.Resize((32, 32)),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])


import torch
import torch.nn.functional as F

def classify(img):

  # Transform
  x = transform(img) 
  
  # Add batch dim
  x = x.unsqueeze(0) 

  # Get predictions
  preds = model(x)

  # Get prediction percentages 
  perc = F.softmax(preds, dim=1)[0] * 100
  
  # Get index of top prediction
  idx = torch.argmax(preds)

  # Get percentage of top prediction
  top_perc = perc[idx].item()

  # Get class name
  class_name = classes[idx]

  # Return prediction with percentage
  return f"{class_name} ({top_perc:.2f}%)"

iface = gr.Interface(classify,
                     "image",
                     "text",
                     theme="huggingface",
                     title="Digit Recognition",
                     description="Upload Image of any Airplane Automobile Bird Cat Deer Dog Frog Horse Ship Truck and the algorithm will detect it in real time! This is CNN trained on CIFAR10 Dataset",
                     article="<p style='text-align: center'>CIFAR10 Classification | Demo Model by Jugal</p>",live=True)
iface.launch(debug=True)