Update app.py

app.py

@@ -1,30 +1,31 @@
 import gradio as gr
 import tensorflow as tf
 import numpy as np
+from PIL import Image
 
-# Load the Keras model
+# Load your model
 model = tf.keras.models.load_model("denis_mnist_cnn_model.h5")
 
-# Define a function to preprocess input and make predictions
+# Define the prediction function
 def predict(image):
-    # Preprocess the image (resize, normalize, etc.)
-    image = tf.image.resize(image, (224, 224))  # Example: Resize to 224x224
-    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    image = np.array(image)  # Convert to numpy array
+    image = tf.image.resize(image, (224, 224))  # Resize to the model's expected input size
+    image = np.expand_dims(image, axis=0)  # Add batch dimension (model expects a batch of images)
     image = image / 255.0  # Normalize pixel values
-
-    # Perform prediction
+    
+    # Check if the model needs flattening
+    if len(image.shape) == 4:  # Check if image has a batch dimension
+        image = tf.keras.layers.Flatten()(image)  # Flatten the image if necessary
+    
     prediction = model.predict(image)
     return {"prediction": prediction.tolist()}
-    
 
-# Create a Gradio interface
+# Create the Gradio interface
 interface = gr.Interface(
-    fn=predict, 
-    inputs="image",  # Text input for comma-separated values
-    outputs="json"  # JSON output for prediction results
+    fn=predict,
+    inputs="image",  # Image input
+    outputs="json",  # Output as JSON
 )
 
-# Launch the Gradio app
-if __name__ == "__main__":
-    interface.launch(share=True)
-
+# Launch the interface
+interface.launch(share=True)