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import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
model_path = "Dog_transfer_learning_NASNetLarge.keras"
model = tf.keras.models.load_model(model_path)
# Define the core prediction function
def predict_dog(image):
# Preprocess image
print(type(image))
image = Image.fromarray(image.astype('uint8')) # Convert numpy array to PIL image
image = image.resize((150, 150)) #resize the image to 28x28 and converts it to gray scale
image = np.array(image)
image = np.expand_dims(image, axis=0) # same as image[None, ...]
# Predict
prediction = model.predict(image)
# No need to apply sigmoid, as the output layer already uses softmax
# Convert the probabilities to rounded values
prediction = np.round(prediction, 3)
# Separate the probabilities for each class
p_husky = prediction[0][0] # Probability for class 'articuno'
p_pomeranian = prediction[0][1] # Probability for class 'moltres'
p_rottwiler = prediction[0][2] # Probability for class 'zapdos'
p_shiba = prediction[0][3] # Probability for class 'zapdos'
return {'husky': p_husky, 'pomeranian': p_pomeranian, 'rottwiler': p_rottwiler, 'shiba': p_shiba}
# Create the Gradio interface
input_image = gr.Image()
iface = gr.Interface(
fn=predict_dog,
inputs=input_image,
outputs=gr.Label(),
examples=["images/husky_1.jpg", "images/husky_2.jpg", "images/husky_3.jpg", "images/pomeranian_1.jpg", "images/pomeranian_2.jpg", "images/pomeranian_3.jpg", "images/rottwiler_1.jpg", "images/rottwiler_2.jpg", "images/rottwiler_3.jpg", "images/shiba_1.jpg", "images/shiba_2.jpg", "images/shiba_3.jpg"],
description="TEST.")
iface.launch()
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