app.py

import gradio as gr
import torch
from model import model, classes
from torchvision import transforms

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

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

# Set your model to evaluation mode
model.eval()


def preprocess_image(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    threshold = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
    resized = cv2.resize(threshold, (28, 28), interpolation=cv2.INTER_AREA)
    tensor = transforms.ToTensor()(resized).unsqueeze(0)
    tensor = transforms.Normalize((0.5,), (0.5,))(tensor)
    return tensor


def classify(image):
    tensor = preprocess_image(image)
    with torch.no_grad():
        output = model(tensor)
        prediction = output.argmax(dim=1, keepdim=True).item()
    return str(prediction)  # Convert prediction to string


iface = gr.Interface(
    fn=classify,
    inputs="sketchpad",
    outputs='label',
    theme="huggingface",
    title="Digit Recognition",
    description="Draw a Digit 0-9 and the algorithm will detect it in real time!",
    article="<p style='text-align: center'>Digit Recognition | Demo Model by Jugal</p>",
    live=True)
iface.launch(debug=True)