import torch
import matplotlib.pyplot as plt
import os
import json
from sklearn.metrics import accuracy_score, precision_score, recall_score
import pandas as pd

# Wczytywanie
# Nie potrzebuje wczytywać modelu i danych testowych bo w jobie trenującym już stworzyłem csv z wynikami
# model = torch.load('model.pkl')
# test_set = pd.read_csv('d_test.csv', encoding='latin-1')
test_results = pd.read_csv('neural_network_prediction_results.csv')


# Ewaluacja
y_true = test_results['Testing Y']
y_predicted = test_results['Predicted Y']

accuracy = accuracy_score(y_true, y_predicted)
precision = precision_score(y_true, y_predicted, average='macro')
recall = recall_score(y_true, y_predicted, average='macro')

eval_results = {
    'Accuracy': accuracy,
    'Macro-Avg Precision': precision,
    'Macro-Avg Recall': recall
}


filename = 'eval_results.json'
if not os.path.exists(filename):
    with open(filename, 'w') as file:
        json.dump({'eval_results': []}, file, indent=2)

with open(filename, 'r+') as file:
    file_data = json.load(file)
    file_data['eval_results'].append(eval_results)
    file.seek(0)
    json.dump(file_data, file, indent=2)

with open(filename, 'r') as file:
    results = json.load(file)['eval_results']
    f_acc = []
    f_prc = []
    f_rec = []
    for res in results:
        f_acc.append(res['Accuracy'])
        f_prc.append(res['Macro-Avg Precision'])
        f_rec.append(res['Macro-Avg Recall'])

build_axis = [i+1 for i in range(len(f_acc))]

plt.xlabel('Build')
plt.ylabel('Score')
plt.plot(build_axis, f_acc, label='Accuracy')
plt.plot(build_axis, f_prc, label='Macro-Avg Precision')
plt.plot(build_axis, f_rec, label='Macro-Avg Recall')
plt.legend()
plt.show()
plt.savefig('metrics.png')