|
from json import load |
|
import gradio as gr |
|
import tensorflow as tf |
|
|
|
input_module1 = gr.Image(label = "test_image", image_mode='L') |
|
input_module2 = gr.Dropdown(choices=['KNN', 'Softmax'], label = "Select Algorithm") |
|
output_module1 = gr.Textbox(label = "Predicted Class") |
|
output_module2 = gr.Label(label = "Predict Probability") |
|
|
|
def fashion_images(input1, input2): |
|
from PIL import Image |
|
img_pil = Image.fromarray(input1) |
|
img_28x28 = np.array(img_pil.resize((28, 28), Image.ANTIALIAS)) |
|
|
|
numpy_image = img_28x28.reshape(1, 28*28) |
|
print("numpy_image: ", numpy_image.shape) |
|
|
|
if input2 == "KNN": |
|
model = joblib.load('best_knn_model.joblib') |
|
else: |
|
model = joblib.load('best_logistic_model.joblib') |
|
out = model.predict(numpy_image)[0] |
|
out_prob = model.predict_proba(numpy_image)[0] |
|
|
|
print("out: ",out) |
|
print("out_prob: ",out_prob.shape) |
|
|
|
class_names = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"] |
|
final = class_names[out] |
|
|
|
class_prob = { class_names[i]:prob for i, prob in enumerate(out_prob)} |
|
return final, class_prob |
|
|
|
gr.Interface(fn=fashion_images, inputs=[input_module1, input_module2], outputs=[output_module1,output_module2]).launch(debug=True) |
