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import gradio as gr | |
import tensorflow as tf | |
import numpy as np | |
from PIL import Image | |
# Load your custom regression model | |
model_path = "transferlearning_pokemon.keras" | |
#model.load_weights(model_path) | |
model = tf.keras.models.load_model(model_path) | |
labels = ['Abra', 'Ditto', 'Gengar'] | |
def predict_pokemon_type(uploaded_file): | |
if uploaded_file is None: | |
return "No file uploaded.", None, "No prediction" | |
# 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 (Ditto, Abra or Grengar) to see its type and confidence level." | |
) | |
# Launch the interface | |
iface.launch() | |