import os
from ast import literal_eval

from segment_key import *
from matplotlib import pyplot as plt
import gradio as gr



def show_kps(contour):
    list1 = range(0, 310)
    list2 = list(zip(get_features_right(contour), list1))

    x_coords = [point[0] for point in list2]
    y_coords = [point[1] for point in list2]
    plt.scatter(x_coords, y_coords, c='red', marker='o', label='Keypoints')

    list2 = list(zip(get_features_left(contour), list1))
    x_coords = [point[0] for point in list2]
    y_coords = [point[1] for point in list2]
    plt.scatter(x_coords, y_coords, c='red', marker='o', label='Keypoints')

    list2 = list(zip(list1, get_features_up(contour)))
    x_coords = [point[0] for point in list2]
    y_coords = [point[1] for point in list2]
    plt.scatter(x_coords, y_coords, c='red', marker='o', label='Keypoints')

    list2 = list(zip(list1, get_features_down(contour)))
    x_coords = [point[0] for point in list2]
    y_coords = [point[1] for point in list2]
    plt.scatter(x_coords, y_coords, c='red', marker='o', label='Keypoints')

    plt.show()


def get_all_features():
    contours = []
    with open('prediction/database.txt', 'r') as file:
        lines = file.readlines()
        for line in lines:
            results = (line.split(';')[1])
            results = results.replace(",,", ",'',")
            results = literal_eval(results)
            results = np.array(results)
            contours.append((line.split(';')[0], results))

    return contours


def cos_similarity(feature1, feature2):
    return np.dot(feature1, feature2) / (np.linalg.norm(feature1) * np.linalg.norm(feature2))


def predict_match(image):
    kp_image, main_feature = final_features(image)
    contours = get_all_features()

    l = []

    for image in contours:
        feature = image[1]
        feature_similarity = 1 - cos_similarity(feature, main_feature)

        l.append([image[0], feature_similarity])

    l.sort(key=lambda x: x[1])

    return kp_image, l[0][0]


# Create the Gradio interface
iface = gr.Interface(
    fn=predict_match,
    inputs=gr.Image(type='pil'),
    outputs=["image", "text"],
    title="YOLOv8 Object Detection",
    description="Upload an image to detect objects using the YOLOv8 model.",
)

# Launch the interface
iface.launch()