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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):
"""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() |