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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+pokemon-model.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,60 @@
+# %%
+import gradio as gr
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.applications.resnet50 import preprocess_input
+from PIL import Image
+
+# Load the pre-trained Keras model
+model = load_model('pokemon-model.keras')
+
+# Define the class labels
+class_labels = ['Bulbasaur', 'Glumanda', 'Pikachu']  # Ensure this matches the training order
+
+# Define the image processing and prediction function
+def predict_image(img):
+    # Ensure the image is a PIL image
+    if not isinstance(img, Image.Image):
+        img = Image.fromarray(img)
+    
+    # Resize the image to the size expected by ResNet50
+    img = img.resize((224, 224))
+    
+    # Convert the image to a numpy array
+    img_array = np.array(img)
+    
+    # Convert the image array to a batch of size 1 (1, 224, 224, 3)
+    img_array = np.expand_dims(img_array, axis=0)
+    
+    # Preprocess the image array using ResNet50's preprocessing
+    img_array = preprocess_input(img_array)
+
+    # Make prediction
+    prediction = model.predict(img_array)
+    
+    # Get the label with the highest probability
+    predicted_index = int(np.argmax(prediction))
+    predicted_label = class_labels[predicted_index]
+    
+    return predicted_label
+
+# Create the Gradio interface with multiple examples
+iface = gr.Interface(
+    fn=predict_image, 
+    inputs=gr.Image(image_mode='RGB'),
+    outputs='label',
+    examples=[['00000015.jpg'], ['20.png'], ['glumanda.jpg'], ['j67j7.png'], ['pikachu.jpg']],
+    title="Pokémon Classification",
+    description="Upload an image of a Pokémon to classify it using the pre-trained model."
+)
+
+# Launch the interface inline in the Jupyter Notebook
+iface.launch(inline=True)
+
+
+# %%
+# Print model summary to verify input shape
+print(model.summary())
+
+
+

pokemon-model.keras

@@ -0,0 +1,3 @@
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