app.py

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

model_path = "pokemon-model_transferlearning.keras"
model = tf.keras.models.load_model(model_path)

# Define the core prediction function
def predict_pokemon(image):
    # Preprocess image
    print(type(image))
    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
    image = image.resize((150, 150))  # Resize the image to 150x150
    image = np.array(image)
    image = np.expand_dims(image, axis=0)  # Expand dimensions to create batch size of 1

    # Predict
    prediction = model.predict(image)
    
    # Assuming the model's output layer uses softmax activation and there are three outputs
    prediction = prediction.flatten()
    predictions = np.round(prediction, 2)  # Flatten the predictions and round them

    # Separate the probabilities for each class
    p_clefairy = predictions[0]  # Probability for Clefairy
    p_snorlax = predictions[1]   # Probability for Snorlax
    p_squirtle = predictions[2]  # Probability for Squirtle

    return {
        'clefairy': p_clefairy,
        'snorlax': p_snorlax,
        'squirtle': p_squirtle
    }

# Create the Gradio interface
input_image = gr.Image()
iface = gr.Interface(
    fn=predict_pokemon,
    inputs=input_image,
    outputs=gr.Label(),
    examples=["test/Squirtle1.png", "test/Squirtle2.jpg", "test/Squirtle3.jpg",
              "test/Snorlax1.jpg", "test/Snorlax2.jpg", "test/Snorlax3.png",
              "test/Clefairy1.png", "test/Clefairy2.png", "test/Clefairy3.png"],
    description="A simple mlp classification model for image classification using the mnist dataset.")
iface.launch()