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import gradio as gr
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
import joblib
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
import gradio as gr
# define image data type
input_image = gr.inputs.Image(label = "Input Image")
select_algorithm = gr.inputs.Dropdown(choices=["Decision Tree", "Random Forest", "AdaBoost", "Gradient Tree Boosting", "KNN"], label = "Select Algorithm")
out_classify = gr.outputs.Textbox(label = "Predict Class")
out_prob = gr.outputs.Textbox(label = "Predict Probability")
"""
gradio interface
"""
def predict_interface(input_image, select_algorithm):
"""
evaluate model
"""
# Convert image to NumPy array
print(input_image.shape)
input_image2 = input_image.mean(axis=2)
print(input_image2.shape)
img_array = input_image2.reshape(1, 28*28)
model_dict = {"Decision Tree":"best_dt_model.joblib",
"Random Forest":"best_rf_model.joblib",
"AdaBoost":"best_ada_model.joblib",
"Gradient Tree Boosting":"best_gbc_model.joblib",
"KNN":"best_knn_model.joblib"}
# Reload the best trained model from disk using joblib
loaded_model = joblib.load(model_dict[select_algorithm])
# Use the reloaded model to make predictions on the validation data
out_classify = loaded_model.predict(img_array)
out_prob = loaded_model.predict_proba(img_array)
class_names = ["T-shirt/top", "Trouser", "Pullover", "Dress", "Coat",
"Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"]
out_prob2 = '\n'.join([f"{name}\t\t\t{np.round(pro,2)}" for name, pro in zip(class_names, out_prob[0])])
return class_names[out_classify[0]], out_prob2
gr.Interface(fn=predict_interface, inputs=[input_image, select_algorithm],
outputs=[out_classify, out_prob],
examples=[["fashion_1.png", "Random Forest"],
["fashion_2.png", "Random Forest"],
["fashion_3.png", "Random Forest"]]
).launch(debug=True)
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