app.py

import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
import keras as keras

# Load your custom classification model
model = tf.keras.models.load_model('pokemon_classifier_model.keras')

# Class names, should match your dataset
class_names = ['Gengar', 'Pikachu', 'Scyther']

def classify_image(image):
    # Convert the Gradio input image to a PIL image
    if isinstance(image, np.ndarray):
        image = Image.fromarray(image.astype('uint8'), 'RGB')
    
    # Resize the image using np.resize
    image = np.resize(image, (224, 224, 3))  # Add the channel dimension
    
    image = image / 255.0  # Normalize the image
    image = np.expand_dims(image, axis=0)  # Add batch dimension
    
    # Predict the class of the image
    predictions = model.predict(image)
    predicted_class = class_names[np.argmax(predictions)]
    confidence = np.max(predictions)
    
    return {predicted_class: float(confidence)}

image_input = gr.Image()
label = gr.Label(num_top_classes=3)

interface = gr.Interface(
    fn=classify_image, 
    inputs=image_input, 
    outputs=label,
    title='Pokémon Classifier',
    description='Upload an image of Pikachu, Gengar, or Scyther, and the classifier will tell you which Pokémon it is, along with the confidence level of the prediction.'
)

interface.launch()