app.py

import gradio as gr
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms
from PIL import Image

class BestMNISTCNN(nn.Module):
    def __init__(self):
        super().__init__()

        self.convblock1 = nn.Sequential(
            nn.Conv2d(1, 32, 3, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.Conv2d(32, 32, 3, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Dropout(0.1)
        )

        self.convblock2 = nn.Sequential(
            nn.Conv2d(32, 64, 3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.Conv2d(64, 64, 3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Dropout(0.1)
        )

        self.convblock3 = nn.Sequential(
            nn.Conv2d(64, 128, 3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.Conv2d(128, 128, 3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.AdaptiveAvgPool2d((1,1)),
            nn.Dropout(0.2)
        )

        self.fc = nn.Linear(128, 10)

    def forward(self, x):
        x = self.convblock1(x)
        x = self.convblock2(x)
        x = self.convblock3(x)
        x = x.view(x.size(0), -1)
        return self.fc(x)

model = BestMNISTCNN()
model.load_state_dict(torch.load("mnist_cnn_.pth", map_location="cpu"))
model.eval()

transform = transforms.Compose([
    transforms.Grayscale(num_output_channels=1),
    transforms.Resize((28, 28)),
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])

classes = [str(i) for i in range(10)]

def predict(image):
    image = transform(image).unsqueeze(0)

    with torch.no_grad():
        logits = model(image)
        probs = torch.softmax(logits, dim=1)[0]

    return {classes[i]: float(probs[i]) for i in range(10)}

demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil", label="Upload a Digit"),
    outputs=gr.Label(num_top_classes=3),
    title="MNIST CNN Classifier (20 Epochs)",
    description="Upload a digit image to classify using the best CNN model trained for 20 epochs.",
)

demo.launch()