import torch
import sys
sys.path.append("../MLTrain/")
from model_train import MyNeuralNetwork, load_data
from torch.utils.data import DataLoader
import csv
import os
import matplotlib.pyplot as plt
from typing import Tuple, List

def evaluate_model() -> Tuple[List[float], float]:
    model: MyNeuralNetwork = MyNeuralNetwork()
    model.load_state_dict(torch.load('model.pt'))
    model.eval()
    test_dataset = load_data("home_loan_test.csv")
    batch_size: int = 32
    test_dataloader: DataLoader = DataLoader(test_dataset, batch_size=batch_size)
    predictions = []
    labels = []
    get_label = lambda pred: 1 if pred >= 0.5 else 0
    total = 0
    correct = 0
    with torch.no_grad():
        for batch_data, batch_labels in test_dataloader:
            batch_predictions = model(batch_data)
            predicted_batch_labels = [get_label(prediction) for prediction in batch_predictions]
            total += len(predicted_batch_labels)
            batch_labels_list = list(map(int,batch_labels.tolist()))
            correct += sum(x == y for x, y in zip(predicted_batch_labels, batch_labels_list))
            predictions.extend(batch_predictions)
            labels.extend(batch_labels)
    accuracy = correct/total
    return predictions, accuracy

def save_predictions(predictions: list[float]) -> None:
    filename = "results.csv"
    column_name = "predict"
    with open(filename, 'w', newline='') as file:
        writer = csv.writer(file)
        writer.writerow([column_name])
        for result in predictions:
            loan_decision = 1 if result.item() > 0.5 else 0
            writer.writerow([loan_decision])

def save_accuracy(accuracy: float) -> None:
    filename = 'metrics.csv'
    if os.path.exists(filename):
        with open(filename, 'a') as file:
            writer = csv.writer(file)
            writer.writerow([accuracy])
    else:
        with open(filename, 'w') as file:
            writer = csv.writer(file)
            writer.writerow(['accuracy'])
            writer.writerow([accuracy])
        
def plot_accuracy() -> None:
    filename = 'metrics.csv'
    accuracy_results = []
    if os.path.exists(filename):
        with open(filename, 'r') as file:
            reader =  csv.reader(file)
            for idx, row in enumerate(reader):
                if idx == 0:
                    continue
                accuracy_results.append(float(row[0]))
    iterations = list(map(str,range(1, len(accuracy_results)+1)))
    plt.plot(iterations, accuracy_results)
    plt.xlabel('build')
    plt.ylabel('accuracy')
    plt.title("Accuracies over builds.")
    plt.savefig("plot.png")
                
def main() -> None:
    predictions, accuracy = evaluate_model()
    save_predictions(predictions)
    save_accuracy(accuracy)
    plot_accuracy()
    
    
if __name__ == "__main__":
    main()