Update app.py

app.py

@@ -8,23 +8,30 @@ model = tf.keras.models.load_model("cnn_model.h5")
 
 def predict(image_array):
     try:
+        # Debug the input type
+        print(f"Type of input: {type(image_array)}")
+        
+        # Handle if input is a dictionary (which appears to be happening)
+        if isinstance(image_array, dict):
+            # If it's a dict, try to get the image data
+            if "image" in image_array:
+                image_array = image_array["image"]
+            else:
+                return "Error: Received dictionary input but couldn't find image data"
+        
         if image_array is None or np.sum(image_array) == 0:
             return "Please draw a digit."
-
+            
         image = Image.fromarray(image_array.astype("uint8"), mode="L")
         image = ImageOps.invert(image).resize((28, 28))
-
         image_array = np.array(image).astype("float32") / 255.0
         image_array = image_array.reshape(1, 28, 28, 1)
-
         logits = model.predict(image_array)
         prediction = int(np.argmax(logits))
         confidence = float(tf.nn.softmax(logits)[0][prediction])
-
         return f"Digit: {prediction} (confidence: {confidence:.2%})"
-
     except Exception as err:
-        return f"Runtime error: {str(err)}"
+        return f"Runtime error: {str(err)}\nInput type: {type(image_array)}"
 
 gr.Interface(
     fn=predict,