Create app.py

app.py

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+import numpy as np
+import cv2
+import gradio as gr
+from tensorflow.keras.utils import img_to_array
+from tensorflow.keras.models import load_model
+
+# Load your pre-trained model
+model = load_model(r'model.h5')
+
+# Define the prediction function that takes an image as input and returns the predicted label
+def predict_image(img):
+    # Preprocess the image if needed
+    x = img_to_array(img)
+    x = cv2.resize(x, (299, 299), interpolation=cv2.INTER_AREA)
+    x /= 255
+    x = np.expand_dims(x, axis=0)
+    image = np.vstack([x])
+    # Make a prediction using your model
+    prediction = model.predict(image)
+    # Assuming your model returns probabilities, get the label with the highest probability
+    predicted_label = "dog" if prediction > 0.5 else "cat"
+    return predicted_label
+
+# Define the Gradio Interface with the desired title and description
+description_html = """
+<p>This model was trained by Moaz Eldsouky You can find more about me here:</p>
+<p>GitHub: <a href="https://github.com/MoazEldsouky">GitHub Profile</a></p>
+<p>LinkedIn: <a href="https://www.linkedin.com/in/moaz-eldesouky-762288251/">LinkedIn Profile</a></p>
+<p>Kaggle: <a href="https://www.kaggle.com/moazeldsokyx">Kaggle Profile</a></p>
+<p>This model was trained to predict whether an image contains a cat or a dog.</p>
+<p>You can see how this model was trained on the following Kaggle Notebook:</p>
+<p><a href="https://www.kaggle.com/code/moazeldsokyx/dogs-vs-cats-classification-with-xception">Kaggle Notebook</a></p>
+<p>Upload a photo to see how the model predicts!</p>
+"""
+
+# Example images for a dog and a cat
+example_dog_image = "dog_.jpeg"
+example_cat_image = "FELV-cat.jpg"
+
+gr.Interface(
+    fn=predict_image,
+    inputs="image",
+    outputs="text",
+    title="Dogs vs Cats classification with Xception 🐶vs 😺",
+    description=description_html,
+    allow_flagging='never',
+    examples=[
+        [example_dog_image],  # Example image for a dog
+        [example_cat_image],  # Example image for a cat
+    ]
+).launch()