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import cv2
import numpy as np
from sklearn.neighbors import KNeighborsClassifier

# Sample dataset of body measurements and corresponding clothing types
# Format: [height, weight, body type] -> clothing type (0: Casual, 1: Formal)
data = np.array([[160, 60, 0],
                 [170, 65, 1],
                 [155, 55, 0],
                 [180, 70, 1],
                 [165, 58, 0]])

X_train = data[:, :-1]  # Features: height and weight
y_train = data[:, -1]   # Target: clothing type

# Initialize and train the K-Nearest Neighbors classifier
knn_classifier = KNeighborsClassifier(n_neighbors=3)
knn_classifier.fit(X_train, y_train)

def predict_clothing_type(height, weight):
    # Predict the clothing type based on height and weight
    prediction = knn_classifier.predict([[height, weight]])
    return "Casual" if prediction == 0 else "Formal"

# Example body scan
height = 170
weight = 68

# Predict clothing type based on body scan
predicted_clothing_type = predict_clothing_type(height, weight)
print("Recommended clothing type:", predicted_clothing_type)
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