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6808f7f bd8f6d7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | import gradio as gr
import tensorflow as tf
print(tf.__version__)
import numpy as np
from PIL import Image
model_path = "vehicles_transferlearning_nasnetlarge.keras"
model = tf.keras.models.load_model(model_path)
# Define the core prediction function
def predict_vehicles(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, 2)
# Separate the probabilities for each class
p_car = prediction[0][0] # Probability for class 'car'
p_motorcycle = prediction[0][1] # Probability for class 'motorcycle'
p_truck = prediction[0][2] # Probability for class 'truck'
return {'car': p_car, 'motorcycle': p_motorcycle, 'truck': p_truck}
# Create the Gradio interface
input_image = gr.Image()
iface = gr.Interface(
fn=predict_vehicles,
inputs=input_image,
outputs=gr.Label(),
examples=["Sample_images/sample1.png", "Sample_images/sample2.jpg", "Sample_images/sample3.jpg", "Sample_images/sample4.jpg", "Sample_images/sample5.jpg", "Sample_images/sample6.jpg"],
description="TEST.")
iface.launch() |