Upload 4 files

INSTRUCTIONS.txt

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+1. Create a Hugging Face Space: SDK = Gradio, hardware = CPU Basic.
+2. Extract this ZIP on your computer.
+3. Upload app.py, requirements.txt and README.md to the root of the Space.
+4. Upload your saved model file cifar10_cnn.pt to the same root.
+5. Commit the changes and wait for the build to complete.
+
+Do not upload this ZIP itself as a single file: first extract it.

README.md

@@ -1,13 +1,34 @@
 ---
-title: Cifar
-emoji: 🏢
-colorFrom: purple
-colorTo: red
+title: CIFAR-10 Image Classifier
+emoji: 🖼️
+colorFrom: blue
+colorTo: purple
 sdk: gradio
-sdk_version: 6.19.0
-python_version: '3.13'
 app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# CIFAR-10 Image Classifier
+
+This is an educational image classifier trained on CIFAR-10.
+
+It predicts one of 10 labels:
+
+- airplane
+- automobile
+- bird
+- cat
+- deer
+- dog
+- frog
+- horse
+- ship
+- truck
+
+## Required model file
+
+Place `cifar10_cnn.pt` in this repository root, next to `app.py`.
+
+## Note
+
+The model was trained on small 32×32 CIFAR-10 images. It may make mistakes on normal phone photos.

app.py

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+from pathlib import Path
+
+import gradio as gr
+import torch
+import torch.nn as nn
+from torchvision import transforms
+
+
+class SimpleCNN(nn.Module):
+    # This must match the network trained in the original notebook.
+    def __init__(self):
+        super().__init__()
+        self.features = nn.Sequential(
+            nn.Conv2d(3, 32, kernel_size=3, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+            nn.Conv2d(32, 64, kernel_size=3, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+            nn.Conv2d(64, 128, kernel_size=3, padding=1),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+        )
+        self.classifier = nn.Sequential(
+            nn.Flatten(),
+            nn.Linear(128 * 4 * 4, 256),
+            nn.ReLU(),
+            nn.Dropout(0.30),
+            nn.Linear(256, 10),
+        )
+
+    def forward(self, x):
+        return self.classifier(self.features(x))
+
+
+MODEL_PATH = Path(__file__).with_name("cifar10_cnn.pt")
+DEVICE = torch.device("cpu")
+
+TRANSFORM = transforms.Compose([
+    transforms.Resize((32, 32)),
+    transforms.ToTensor(),
+    transforms.Normalize(
+        (0.4914, 0.4822, 0.4465),
+        (0.2470, 0.2435, 0.2616),
+    ),
+])
+
+
+def load_model():
+    if not MODEL_PATH.exists():
+        raise FileNotFoundError(
+            "Файл cifar10_cnn.pt не найден. Загрузите его в корень Space рядом с app.py."
+        )
+    try:
+        checkpoint = torch.load(MODEL_PATH, map_location=DEVICE, weights_only=True)
+    except TypeError:
+        checkpoint = torch.load(MODEL_PATH, map_location=DEVICE)
+
+    model = SimpleCNN().to(DEVICE)
+    model.load_state_dict(checkpoint["model_state_dict"])
+    model.eval()
+
+    classes = checkpoint.get(
+        "classes",
+        ["airplane", "automobile", "bird", "cat", "deer",
+         "dog", "frog", "horse", "ship", "truck"],
+    )
+    return model, classes
+
+
+MODEL, CLASSES = load_model()
+
+
+def predict(image):
+    if image is None:
+        return {}, "Загрузите изображение."
+
+    x = TRANSFORM(image.convert("RGB")).unsqueeze(0).to(DEVICE)
+    with torch.no_grad():
+        probabilities = torch.softmax(MODEL(x), dim=1)[0].cpu()
+
+    values, indices = torch.topk(probabilities, k=3)
+    results = {
+        CLASSES[index.item()]: float(value)
+        for value, index in zip(values, indices)
+    }
+    label = CLASSES[indices[0].item()]
+    confidence = float(values[0]) * 100
+    return results, f"Модель считает, что это: **{label}** ({confidence:.1f}%)."
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """# 🖼️ Распознавание изображений — CIFAR-10
+
+Загрузите фото. Модель выберет наиболее похожий класс: **самолёт, автомобиль, птица, кот, олень, собака, лягушка, лошадь, корабль или грузовик**.
+
+> Это учебная модель, обученная на маленьких изображениях CIFAR-10. На обычных фото она может ошибаться."""
+    )
+
+    with gr.Row():
+        image_input = gr.Image(
+            label="Загрузите изображение",
+            type="pil",
+            sources=["upload", "webcam"],
+        )
+        result = gr.Label(label="Три наиболее вероятных класса", num_top_classes=3)
+
+    explanation = gr.Markdown()
+    button = gr.Button("Распознать", variant="primary")
+    button.click(predict, inputs=image_input, outputs=[result, explanation])
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+torch
+torchvision
+pillow