--- a
+++ b/xc.py
@@ -0,0 +1,35 @@
+# c.py
+from tensorflow import keras
+from keras.models import load_model
+from keras.preprocessing.image import load_img, img_to_array
+import numpy as np
+
+def predict_image(file_path):
+    try:
+        # Load the pre-trained model
+        model_path = r'model\CNN_model.h5'
+        print(f"Loading model from: {model_path}")
+        loaded_model = load_model(model_path)  # Load the model
+        print("Model loaded successfully!")
+
+        # Preprocess the image
+        img = load_img(file_path, target_size=(224, 224))  # Resize to target size
+        img_array = img_to_array(img)  # Convert to numpy array
+        img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+        img_array = img_array / 255.0  # Normalize pixel values
+
+        # Debugging: Check the shape and content of the image array
+        print(f"Image shape after preprocessing: {img_array.shape}")
+
+        # Predict the class
+        predictions = loaded_model.predict(img_array)
+        print(f"Raw predictions: {predictions}")
+
+        # Get the predicted class
+        predicted_class = np.argmax(predictions, axis=1)[0]
+        print(f"Predicted class: {predicted_class}")
+
+        return predicted_class  # Return only the predicted class
+    except Exception as e:
+        print(f"Error processing image or predicting: {e}")
+        return None