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import gradio as gr
import tensorflow as tf
from PIL import Image
import numpy as np
model_path = "pokemon_classifier_model.keras"
model = tf.keras.models.load_model(model_path)
labels = ['Pikachu', 'Sandshrew', 'Squirtle']
def predict_image(image):
# Preprocess image
image = Image.fromarray(image.astype('uint8')) # Convert numpy array to PIL image
image = image.resize((224, 224)) # Resize the image to 224x224 pixels
image = np.array(image) / 255.0 # Convert to float and normalize
# Ensure the image has 3 color channels
if image.ndim == 2: # If grayscale, convert to RGB
image = np.stack((image,)*3, axis=-1)
prediction = model.predict(image[None, ...]) # Adding batch dimension
confidences = {labels[i]: float(prediction[0][i]) for i in range(len(labels))}
return confidences
input_image = gr.Image()
output_text = gr.Textbox(label="Predicted Value")
iface = gr.Interface(
fn=predict_image,
inputs=input_image,
outputs=gr.Label(),
title="Pokémon Classifier",
examples=["images/pikachu.png", "images/squirtle.png", "images/sandshrew.png"],
description="Upload an image of Pikachu, Sandshrew, or Squirtle and the classifier will predict which one it is."
)
iface.launch()
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