app.py

import torch
import torch.nn as nn
import torchvision.transforms as transforms
import gradio as gr
import numpy as np
from PIL import Image
# Define the CNN
class SimpleCNN(nn.Module):
    def __init__(self):
        super(SimpleCNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(64 * 7 * 7, 128)
        self.fc2 = nn.Linear(128, 10)
        self.relu = nn.ReLU()
        
    def forward(self, x):
        x = self.pool(self.relu(self.conv1(x)))  # Output: 32x14x14
        x = self.pool(self.relu(self.conv2(x)))  # Output: 64x7x7
        x = x.view(-1, 64 * 7 * 7)  # Flattened to: 3136
        x = self.relu(self.fc1(x))  # Output: 128
        x = self.fc2(x)  # Output: 10 logits
        return x

# Load the trained model
model = SimpleCNN()
model.load_state_dict(torch.load('mnist_cnn.pth', map_location=torch.device('cpu')))
model.eval()

# Define the transformation for the input image
transform = transforms.Compose([
    transforms.Grayscale(num_output_channels=1),
    transforms.Resize((28, 28)),
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

# Prediction function


# Prediction function
def predict(image):
    image = transform(image).unsqueeze(0)  # Add batch dimension
    with torch.no_grad():
        output = model(image)
        probabilities = nn.Softmax(dim=1)(output)
        predicted_class = torch.argmax(probabilities, dim=1)
    return {str(i): probabilities[0][i].item() for i in range(10)}

# Create the Gradio interface
interface = gr.Interface(
    fn=predict,
    inputs=gr.Sketchpad(type='pil'),
    outputs=gr.Label(num_top_classes=10)
)

# Launch the interface
interface.launch()