Update app.py

app.py

@@ -1,12 +1,3 @@
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-
-# Load the model (ensure you have the correct model path)
-model = tf.keras.models.load_model("denis_mnist_cnn_model.h5")
-
-# Define a function to preprocess input and make predictions
 def predict(image):
     # Convert image to a numpy array
     image = np.array(image)
@@ -27,8 +18,11 @@ def predict(image):
     # Perform prediction
     prediction = model.predict(image)
     
-    # Return prediction as JSON
-    return {"prediction": prediction.tolist()}
+    # Get the predicted class (index of the highest probability)
+    predicted_class = np.argmax(prediction)
+    
+    # Return prediction as JSON (with the predicted class label)
+    return {"prediction": int(predicted_class)}
 
 # Create a Gradio interface
 interface = gr.Interface(