Upload app.py

app.py

@@ -0,0 +1,32 @@
+import torch
+import torch.nn.functional as F
+import gradio as gr
+import numpy as np
+from PIL import Image
+from model import CNN
+
+# Load model
+model = CNN()
+model.load_state_dict(torch.load("pytorch_model.bin", map_location="cpu"))
+model.eval()
+
+# Prediction function
+def predict_digit(image):
+    if image is None:
+        return "No image"
+    image = Image.fromarray(image).convert("L").resize((28, 28))
+    image = np.array(image) / 255.0
+    image = torch.tensor(image).unsqueeze(0).unsqueeze(0).float()
+    with torch.no_grad():
+        output = model(image)
+        probabilities = F.softmax(output, dim=1).numpy().flatten()
+        return {str(i): float(probabilities[i]) for i in range(10)}
+
+# Interface (no 'tool', 'type', or other unsupported args)
+gr.Interface(
+    fn=predict_digit,
+    inputs=gr.Image(label="Upload a digit image"),
+    outputs=gr.Label(num_top_classes=3),
+    title="Digit Classifier",
+    description="Upload a 28x28 grayscale image of a handwritten digit (0–9)."
+).launch()