Create prd2.py

prd2.py

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+import cv2  # Assuming you have OpenCV installed
+import numpy as np
+from tensorflow.keras.preprocessing import image
+import tensorflow as tf
+from huggingface_hub import from_pretrained_keras
+model = from_pretrained_keras("okeowo1014/catsanddogs")
+
+# Load the saved model
+# model = tf.keras.models.load_model('cat_dog_classifier.keras')  # Replace with your model filename
+img_width, img_height = 224, 224  # VGG16 expects these dimensions
+
+
+# Function to preprocess an image for prediction
+def preprocess_image(img_path):
+    img = cv2.imread(img_path)  # Read the image
+    img = cv2.resize(img, (img_width, img_height))  # Resize according to model input size
+    img = img.astype('float32') / 255.0  # Normalize pixel values
+    img = np.expand_dims(img, axis=0)  # Add a batch dimension (model expects batch of images)
+    return img
+
+
+# Get the path to your new image
+new_image_path = 'test1/11.jpg'  # Replace with your image path
+
+# Preprocess the image
+preprocessed_image = preprocess_image(new_image_path)
+
+# Make prediction
+prediction = model.predict(preprocessed_image)
+
+# Decode the prediction (assuming class 0 is cat, 1 is dog)
+predicted_class = int(prediction[0][0] > 0.5)  # Threshold of 0.5 for binary classification
+class_names = ['cat', 'dog']  # Adjust class names according to your model
+
+print(f"Predicted class: {class_names[predicted_class]}")