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import gradio as gr
import keras
from keras.src.applications.densenet import preprocess_input
import numpy as np
from load_safetensors import model_load
pokedex = model_load()
with open('Pokemons.txt', 'r') as f:
class_labels = f.read().splitlines()
def classify_pokemon(image):
img = image.resize((224, 224))
x = keras.utils.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = pokedex.predict(x)
top_indices = preds[0].argsort()[-3:][::-1]
results = {class_labels[i]: float(preds[0][i]) for i in top_indices}
return results
title = "Pokedex"
description = "Pokémon first gen classifier"
examples = [
'examples/Pikachu.png',
'examples/Charmander.png',
'examples/Squirtle.png',
'examples/Bulbasaur.png',
'examples/Caterpie.png',
'examples/Cloyster.png',
'examples/Gengar.png',
'examples/Porygon.png',
'examples/Rapidash.png',
'examples/Slowpoke.png',
]
intf = gr.Interface(
fn=classify_pokemon,
inputs=gr.Image(type='pil', label="Upload a Pokémon image"),
outputs=gr.Label(num_top_classes=3, label="Prediction"),
examples=examples,
title=title,
description=description,
)
intf.launch(inline=False)
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