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from joblib import dump,load
import pandas as pd
import warnings
import gradio as gr
import cv2
warnings.filterwarnings("ignore")
small_X_train_flatten = pd.read_csv('Homework01_trainX_image_flatten.csv')
small_y_train = pd.read_csv('Homework01_trainy_image_flatten.csv')
best_knn = load("best_knn.joblib")
best_log = load("best_log.joblib")
best_knn.fit(small_X_train_flatten,small_y_train)
best_log.fit(small_X_train_flatten,small_y_train)
def preprocess_image(image):
resized_image = cv2.resize(image, (28, 28))
grayscale_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2GRAY)
flattened_image = grayscale_image.flatten()
normalized_image = flattened_image / 255.0
return normalized_image
class_names = [
"T-shirt/top", "Trouser", "Pullover", "Dress", "Coat",
"Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot"
]
def classify_image(input_image, classifier):
preprocessed_image = preprocess_image(input_image)
reshaped_image = preprocessed_image.reshape(1, -1)
if classifier == "Logistic Regression":
output1 = best_log.predict(reshaped_image)[0]
output2 = dict(zip(class_names, best_log.predict_proba(reshaped_image)[0]))
elif classifier == "K-Nearest Neighbors":
output1 = best_knn.predict(reshaped_image)[0]
output2 = dict(zip(class_names, best_knn.predict_proba(reshaped_image)[0]))
return class_names[output1], output2
gr.Interface(
fn=classify_image,
title="Fashion MNIST Classifier",
inputs=[
gr.Image(type="numpy", label="Input Image"),
gr.Dropdown(
label="Select Classifiers",
choices=["Logistic Regression", "K-Nearest Neighbors"]
)
],
outputs=[
gr.Textbox(label="Predicted Class"),
gr.Label(label="Predicted Label Distribution")
]
).launch(share=True)
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