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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Digit.keras filter=lfs diff=lfs merge=lfs -text

Digit.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0deb8876ae99141d3dd2e22aa9f1a18f8036695a8e90367a20ca415859839300
+size 2739953

app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+from tensorflow import keras
+
+model = keras.models.load_model("Digit.keras")
+
+def preprocess_image(img):
+    if img is None:
+        raise ValueError("No image provided.")
+
+    # If Sketchpad returns a dict, try common keys
+    if isinstance(img, dict):
+        if "image" in img and img["image"] is not None:
+            img = img["image"]
+        elif "composite" in img and img["composite"] is not None:
+            img = img["composite"]
+        else:
+            raise ValueError("No image data found in dictionary.")
+
+    # If still a NumPy array, convert to PIL
+    if isinstance(img, np.ndarray):
+        img = Image.fromarray(img.astype("uint8"))
+
+    # --- MNIST preprocessing ---
+    img = img.convert("L").resize((28, 28))
+    arr = np.array(img).astype("float32")
+    if arr.mean() > 127:  # invert if white background
+        arr = 255 - arr
+    arr = arr / 255.0
+    return arr[np.newaxis, ..., np.newaxis]  # (1,28,28,1)
+
+def predict(img):
+    try:
+        x = preprocess_image(img)
+        probs = model.predict(x, verbose=0)[0]
+        pred = int(np.argmax(probs))
+        return str(pred), {str(i): float(probs[i]) for i in range(10)}
+    except Exception as e:
+        return f"Error: {e}", {}
+
+
+with gr.Blocks(title="MNIST Digit Classifier") as demo:
+    gr.Markdown("## ✍️ Draw a digit (0–9)")
+    with gr.Row():
+        with gr.Column():
+            canvas = gr.Sketchpad(
+                canvas_size=(280, 280),
+                type="pil",                 # <-- forces PIL, avoids dicts
+                label="Draw a digit here"
+            )
+            btn = gr.Button("Predict")
+        with gr.Column():
+            pred_txt = gr.Textbox(label="Predicted Digit", interactive=False)
+            probs = gr.Label(label="Class Probabilities", num_top_classes=10)
+
+    btn.click(predict, inputs=canvas, outputs=[pred_txt, probs])
+
+demo.launch()