Create model_utils.py

model_utils.py

@@ -0,0 +1,31 @@
+# model_utils.py
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+class ImageClassifier:
+    def __init__(self, model_path):
+        self.model = tf.keras.models.load_model(model_path)
+        # Update these based on your model's requirements
+        self.input_size = (224, 224)  # Example size, change as needed
+        self.class_names = ['class1', 'class2', 'class3']  # Replace with your class names
+    
+    def preprocess_image(self, image):
+        """Preprocess the image for your model"""
+        image = image.resize(self.input_size)
+        image_array = np.array(image)
+        image_array = image_array / 255.0  # Normalize if your model expects this
+        image_array = np.expand_dims(image_array, axis=0)
+        return image_array
+    
+    def predict(self, image):
+        """Make a prediction on the image"""
+        processed_image = self.preprocess_image(image)
+        predictions = self.model.predict(processed_image)
+        predicted_class = np.argmax(predictions[0])
+        confidence = np.max(predictions[0])
+        return {
+            'class': self.class_names[predicted_class],
+            'confidence': float(confidence),
+            'all_predictions': predictions.tolist()
+        }