Update app.py

app.py

@@ -3,26 +3,20 @@ import numpy as np
 from PIL import Image, ImageOps
 import tensorflow as tf
 
-#Load the model 
+# Load the model 
 model = tf.keras.models.load_model("cnn_model.h5")
 
 def predict(image_array):
     try:
-        print("Function called!")
-
-        # Check for blank or None input
         if image_array is None or np.sum(image_array) == 0:
             return "Please draw a digit."
 
-        # Convert to PIL image and process
         image = Image.fromarray(image_array.astype("uint8"), mode="L")
         image = ImageOps.invert(image).resize((28, 28))
 
-        # Normalize and reshape for model input
         image_array = np.array(image).astype("float32") / 255.0
         image_array = image_array.reshape(1, 28, 28, 1)
 
-        # Predict
         logits = model.predict(image_array)
         prediction = int(np.argmax(logits))
         confidence = float(tf.nn.softmax(logits)[0][prediction])
@@ -30,13 +24,11 @@ def predict(image_array):
         return f"Digit: {prediction} (confidence: {confidence:.2%})"
 
     except Exception as err:
-        return f"💥 Runtime error: {str(err)}"
-
+        return f"Runtime error: {str(err)}"
 
-gr.Sketchpad(image_mode="L", canvas_size=(200, 200), type="numpy")
 gr.Interface(
     fn=predict,
-    inputs=gr.Sketchpad(image_mode="L", canvas_size=(200, 200)),
+    inputs=gr.Sketchpad(image_mode="L", canvas_size=(200, 200), type="numpy"),
     outputs="text",
-    title="Digit Classifier",
+    title="Digit Classifier"
 ).launch()