deploy mnist gradio model

app.py

@@ -1,7 +1,33 @@
 import gradio as gr
+import numpy as np
+from PIL import Image
+import tensorflow as tf
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Load model (Space-friendly)
+model = tf.keras.models.load_model(
+    "mobilenetv2_mnist.keras",
+    compile=False,
+    safe_mode=False
+)
+
+def predict(image):
+    image = image.resize((32, 32))
+    image = image.convert("RGB")
+
+    img = np.array(image) / 255.0
+    img = np.expand_dims(img, axis=0)
+
+    preds = model.predict(img)
+    digit = int(np.argmax(preds))
+
+    return f"Predicted Digit: {digit}"
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="MNIST MobileNetV2 Classifier",
+    description="Upload a digit image (0–9)"
+)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
 demo.launch()

requirements.txt

@@ -0,0 +1,5 @@
+tensorflow==2.15
+keras==3.0.5
+gradio
+numpy
+pillow