Update app.py

app.py

@@ -1,43 +1,31 @@
-import gradio as gr  # Ensure you import gradio correctly
+import gradio as gr
 import tensorflow as tf
 import numpy as np
+from PIL import Image
 
-# Load the Keras model
+# Load the trained model
 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
+# Preprocessing function for images
+def preprocess_image(image):
+    # Resize the image to 28x28 as expected by the model
     image = np.array(image)
-    
-    # Resize the image to the expected shape (28, 28, 3) for RGB images
-    image = tf.image.resize(image, (28, 28))  # Resize to 28x28 pixels
-    
-    # Check if the image is grayscale (single channel), and convert to RGB if necessary
-    if image.shape[-1] == 1:  # If it's grayscale (single channel)
-        image = np.repeat(image, 3, axis=-1)  # Convert grayscale to RGB by repeating the channel
-    
-    # Normalize the image
-    image = image / 255.0
-    
-    # Add batch dimension
-    image = np.expand_dims(image, axis=0)  # Add batch dimension to match the model's expected input shape (1, 28, 28, 3)
-    
-    # Perform prediction
-    prediction = model.predict(image)
-    
-    # Get the predicted class (index of the highest probability)
-    predicted_class = np.argmax(prediction)
-    
-    # Return prediction as JSON (with the predicted class label)
+    image = tf.image.resize(image, (28, 28))  # Resize to 28x28
+    image = tf.image.grayscale_to_rgb(image)  # Convert grayscale to RGB (3 channels)
+    image = image / 255.0  # Normalize pixel values to [0, 1]
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    return image
+
+# Function to make predictions
+def predict(image):
+    image = preprocess_image(image)
+    prediction = model.predict(image)  # Predict
+    predicted_class = np.argmax(prediction, axis=-1)[0]  # Get the predicted class
     return {"prediction": int(predicted_class)}
 
 # Create a Gradio interface
-interface = gr.Interface(
-    fn=predict, 
-    inputs="image",  # Image input for testing
-    outputs="json"  # JSON output for prediction results
-)
+interface = gr.Interface(fn=predict, inputs="image", outputs="json")
 
-# Launch the interface
-interface.launch(share=True)
+# Launch the Gradio interface
+if __name__ == "__main__":
+    interface.launch()