Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+import numpy as np
+
+# Load the trained model
+model = tf.keras.models.load_model('cat_dog_classifier_vgg16.h5')
+
+# Define a function to make predictions
+def predict_image(img):
+    # Preprocess the image
+    img = img.resize((224, 224)) 
+    img_array = image.img_to_array(img)  
+    img_array = np.expand_dims(img_array, axis=0)  
+    img_array = img_array / 255.0  
+
+    # Make a prediction
+    prediction = model.predict(img_array)
+    if prediction[0] < 0.5:
+        return "Cat"
+    else:
+        return "Dog"
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_image, 
+    inputs=gr.inputs.Image(type="pil"), 
+    outputs="text",
+    title="Cat and Dog Classifier",
+    description="Upload an image of a cat or a dog and the model will classify it.",
+    examples=["cat_example.jpg", "dog_example.jpg"]
+)
+
+# Launch the interface
+iface.launch()