Create app.py

app.py

@@ -0,0 +1,46 @@
+import gradio as gr
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+ 
+# Load the pre-trained Pokémon model
+model_path = "pokemon_classifier_model.keras"
+model = tf.keras.models.load_model(model_path)
+ 
+# Define the Pokémon classes
+classes = ['Aerodactyl', 'Alakazam', 'Beedrill']  # Adjust these as per your model's classes
+ 
+# Define the image classification function
+def classify_image(image):
+    try:
+        # Ensure the image is in RGB and normalize it
+        if image.ndim == 2:  # Check if the image is grayscale
+            image = np.stack((image,)*3, axis=-1)  # Convert grayscale to RGB by repeating the gray channel
+        elif image.shape[2] == 4:  # Check if the image has an alpha channel
+            image = image[:, :, :3]  # Drop the alpha channel
+        image = Image.fromarray(image.astype('uint8'), 'RGB')  # Convert to PIL Image to resize
+        image = image.resize((150, 150))  # Resize to match the model's input size
+ 
+        image_array = np.array(image) / 255.0  # Convert to array and normalize
+        image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
+ 
+        # Predict using the model
+        prediction = model.predict(image_array)
+        predicted_class = classes[np.argmax(prediction)]
+        confidence = np.max(prediction)
+ 
+        return f"Predicted Pokémon: {predicted_class}, Confidence: {np.round(confidence * 100, 2)}%"
+    except Exception as e:
+        return str(e)  # Return the error message if something goes wrong
+ 
+# Create Gradio interface
+input_image = gr.Image()  # Using Gradio's Image component correctly
+output_label = gr.Label()
+ 
+interface = gr.Interface(fn=classify_image,
+                         inputs=input_image,
+                         outputs=output_label,
+                         examples=["pokemon/aerodactyl.png", "pokemon/alakazam.png", "pokemon/beedrill.png"],
+                         description="Upload an image of a Pokémon (Aerodactyl, Alakazam or Beedrill) to classify!")
+ 
+interface.launch()