app.py

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

labels = ['Haunter', 'Gengar', 'Ditto', 'Vulpix']

def predict_pokemon_type(uploaded_file):
    if uploaded_file is None:
        return "No file uploaded.", None, "No prediction"

    model = tf.keras.models.load_model('pokemon-model_2_transferlearning.keras')

    # Load the image from the file path
    with Image.open(uploaded_file) as img:
        img = img.resize((150, 150))
        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))}
        
        # Identify the most confident prediction
        confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}

        return img, 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=["image", "text"],  # Output types for image and text
    title="Pokemon Classifier",  # Title of the interface
    description="Upload a picture of a Pokemon (preferably Cubone, Ditto, Psyduck, Snorlax, or Weedle) to see its type and confidence level. The trained model has an accuracy of 96%!"  # Description of the interface
)

# Launch the interface
iface.launch()