app.py

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

class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']

device = torch.device("cpu")
inference_transform = T.Compose([
    T.ToTensor(),
    T.Normalize(mean=(0.4914, 0.4822, 0.4465),
                std=(0.2023, 0.1994, 0.2010)),
])

class SmallCifarCNN(nn.Module):
    def __init__(self, num_classes: int = 10):
        super().__init__()
        self.features = nn.Sequential(
            # Block 1
            nn.Conv2d(3, 32, kernel_size=3, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2),  # 32 -> 16
            # Block 2
            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2),  # 16 -> 8
            # Block 3
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2),  # 8 -> 4
        )
        self.classifier = nn.Sequential(
            nn.Flatten(),
            nn.Linear(128 * 4 * 4, 256),
            nn.ReLU(inplace=True),
            nn.Dropout(p=0.5),
            nn.Linear(256, num_classes),
        )

    def forward(self, x):
        x = self.features(x)
        x = self.classifier(x)
        return x

deployed_model = SmallCifarCNN(num_classes=len(class_names)).to(device)

model_path = 'cifar_cnn_best.pt'
deployed_model.load_state_dict(
    torch.load(model_path, map_location=device)
)
deployed_model.to(device)
deployed_model.eval()

def predict_cifar_image(img: Image.Image):
    """
    Gradio callback:
    - Takes a PIL Image
    - Resizes to 32x32 (CIFAR size)
    - Normalizes and runs through the CNN
    - Returns top-3 class probabilities
    """
    img = img.convert("RGB")
    img = img.resize((32, 32), Image.BILINEAR)

    x = inference_transform(img).unsqueeze(0).to(device)
    with torch.no_grad():
        logits = deployed_model(x)
        probs = F.softmax(logits, dim=1).cpu().numpy().ravel()

    topk = 3
    idxs = np.argsort(-probs)[:topk]
    return {class_names[i]: float(probs[i]) for i in idxs}

demo = gr.Interface(
    fn=predict_cifar_image,
    inputs=gr.Image(type="pil", label="Upload an RGB image (will be resized to 32×32)"),
    outputs=gr.Label(num_top_classes=3, label="Top-3 CIFAR-10 predictions"),
    title="CIFAR-10 CNN Classifier",
    description="Small CNN trained on CIFAR-10. Upload an image and see top-3 class probabilities.",
)


if __name__ == "__main__":
    demo.launch()