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app.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision import transforms
+from PIL import Image
+import gradio as gr
+
+# --- Define the MLP_one CNN architecture ---
+class MLP_one(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 5 * 5, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = torch.flatten(x, 1)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+
+# --- Load trained model weights ---
+model = MLP_one()
+model.load_state_dict(torch.load("model.pth", map_location="cpu"))
+model.eval()
+
+# --- CIFAR-10 class names ---
+classes = [
+    "airplane", "automobile", "bird", "cat", "deer",
+    "dog", "frog", "horse", "ship", "truck"
+]
+
+# --- Transform pipeline ---
+transform = transforms.Compose([
+    transforms.Resize((32, 32)),  # resize any image to 32x32
+    transforms.ToTensor(),
+    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+])
+
+# --- Prediction function ---
+def predict(image):
+    """
+    Takes any image (JPG, PNG, etc.), converts to RGB, resizes to 32x32,
+    runs through the CNN, and returns class probabilities.
+    """
+    # Convert to RGB (in case of grayscale or RGBA input)
+    image = image.convert("RGB")
+    image = transform(image).unsqueeze(0)  # shape: [1, 3, 32, 32]
+    
+    with torch.no_grad():
+        outputs=gr.Label(num_top_classes=3)
+        probs = torch.softmax(outputs, dim=1)[0]
+    
+    # Convert to dictionary: {class: probability}
+    return {classes[i]: float(probs[i]) for i in range(10)}
+
+# --- Gradio Interface ---
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload any image"),
+    outputs=gr.Label(num_top_classes=3),
+    title="CIFAR-10 Image Classifier (MLP_one)",
+    description=(
+        "Upload any image (JPG, PNG, etc.) and this model will resize it to 32×32 "
+        "and predict the closest CIFAR-10 class."
+    ),
+    examples=[
+        ["https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cifar10-dog.png"],
+        ["https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cifar10-truck.png"],
+    ]
+)
+
+demo.launch()