Upload app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+ 
+# Load the Pokémon classifier model
+model_path = "pokemon_classifier_finetuned.keras"
+model = tf.keras.models.load_model(model_path)
+ 
+labels = ['Dodrio', 'Arbok', 'Gengar']
+ 
+# Define function for Pokémon classification
+def preprocess_image(image):
+    # Preprocess image
+    image = Image.fromarray(image.astype('uint8'))
+    image = image.resize((224, 224))
+    image = np.array(image)
+    image = image / 255.0  # Normalize pixel values
+    return image
+ 
+# Prediction
+def predict_pokemon(image):
+    image = preprocess_image(image)
+    prediction = model.predict(image[None, ...])
+    predicted_class = labels[np.argmax(prediction)]
+    confidence = np.round(np.max(prediction) * 100, 2)
+    result = f"Label: {predicted_class}, Confidence: {confidence}%"
+    return result
+ 
+# Create Gradio interface
+input_image = gr.Image()
+output_text = gr.Textbox(label="Pokemon")
+interface = gr.Interface(fn=predict_pokemon, 
+                         inputs=input_image, 
+                         outputs=output_text,   
+                         description="A Pokémon classifier using transfer learning and fine-tuning with EfficientNetB0.")
+ 
+if __name__ == "__main__":
+    interface.launch()