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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):
if uploaded_file is None:
return "No file uploaded.", None, "No prediction"
model = tf.keras.models.load_model('pokemon-model.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))}
return img, confidences
# Define the Gradio interface
iface = gr.Interface(
fn=predict_pokemon_type,
inputs=gr.File(label="Upload File"),
outputs=["image", "text"],
title="Pokemon Classifier",
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 a test accuracy of 99.17%!"
)
# Launch the interface
iface.launch()
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