initial commit

app.py

@@ -0,0 +1,22 @@
+# Load the model
+model = tf.keras.models.load_model('pokemon_classifier_model.keras')
+
+def predict(image):
+    img = tf.keras.preprocessing.image.img_to_array(image)
+    img = tf.keras.preprocessing.image.smart_resize(img, (224, 224))
+    img = tf.expand_dims(img, 0)  # Make batch of one
+    
+    pred = model.predict(img)
+    pred_label = tf.argmax(pred, axis=1).numpy()[0]  # get the index of the max logit
+    pred_class = class_names[pred_label]  # use the index to get the corresponding class name
+    confidence = tf.nn.softmax(pred)[0][pred_label]  # softmax to get the confidence
+    
+    print(f"Predicted: {pred_class}, Confidence: {confidence:.4f}")
+    return pred_class
+
+
+# Setup Gradio interface
+iface = gr.Interface(fn=predict, inputs=gr.Image(), outputs="text", title="Pokémon Classifier")
+
+# Run the interface
+iface.launch()