app.py

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

labels = ['Cubone', 'Ditto', 'Psyduck', 'Snorlax', 'Weedle']

def predict_pokemon_type(uploaded_file):
    """Process the uploaded file."""
    if uploaded_file is None:
        return "No file uploaded."
    
    model = tf.keras.models.load_model('pokemon-model_transferlearning.keras')
    # Load the image from the file path
    with Image.open(uploaded_file) as img:
        img = img.resize((200, 200))
        img_array = np.array(img)

        prediction = model.predict(np.expand_dims(img_array, axis=0))
        confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}

        return confidences


# Define the Gradio interface
iface = gr.Interface(
    fn=predict_pokemon_type,  # Function to process the input
    inputs=gr.File(label="Upload File"),  # File upload widget
    outputs="text",  # Output type
    title="Pokemon Classifier",  # Title of the interface
    description="Upload a picture of a pokemon (preferably Cubone, Ditto, Psyduck, Snorlax or Weedle), because the model was trained on 'em. It has an astonishing accuracy of 16% :)"  # Description of the interface
)

# Launch the interface
iface.launch()