import sys

import torch
import torch.nn as nn
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score


def drop_relevant_columns(imbd_data):
    imbd_data.drop(columns=["Poster_Link"], inplace=True)
    imbd_data.drop(columns=["Overview"], inplace=True)
    imbd_data.drop(columns=["Certificate"], inplace=True)
    return imbd_data


def lowercase_columns_names(imbd_data):
    imbd_data["Series_Title"] = imbd_data["Series_Title"].str.lower()
    imbd_data["Genre"] = imbd_data["Genre"].str.lower()
    imbd_data["Director"] = imbd_data["Director"].str.lower()
    imbd_data["Star1"] = imbd_data["Star1"].str.lower()
    imbd_data["Star2"] = imbd_data["Star2"].str.lower()
    imbd_data["Star3"] = imbd_data["Star3"].str.lower()
    imbd_data["Star4"] = imbd_data["Star4"].str.lower()
    return imbd_data


def data_to_numeric(imbd_data):
    imbd_data = imbd_data.replace(np.nan, '', regex=True)
    imbd_data["Gross"] = imbd_data["Gross"].str.replace(',', '')
    imbd_data["Gross"] = pd.to_numeric(imbd_data["Gross"], errors='coerce')
    imbd_data["Runtime"] = imbd_data["Runtime"].str.replace(' min', '')
    imbd_data["Runtime"] = pd.to_numeric(imbd_data["Runtime"], errors='coerce')
    imbd_data["IMDB_Rating"] = pd.to_numeric(imbd_data["IMDB_Rating"], errors='coerce')
    imbd_data["Meta_score"] = pd.to_numeric(imbd_data["Meta_score"], errors='coerce')
    imbd_data["Released_Year"] = pd.to_numeric(imbd_data["Released_Year"], errors='coerce')
    imbd_data = imbd_data.dropna()
    imbd_data = imbd_data.reset_index()
    imbd_data.drop(columns=["index"], inplace=True)
    return imbd_data


def create_train_dev_test(imbd_data):
    data_train, data_test = train_test_split(imbd_data, test_size=230, random_state=1, shuffle=True)
    data_test, data_dev = train_test_split(data_test, test_size=115, random_state=1, shuffle=True)
    data_test.to_csv("data_test.csv", encoding="utf-8", index=False)
    data_dev.to_csv("data_dev.csv", encoding="utf-8", index=False)
    data_train.to_csv("data_train.csv", encoding="utf-8", index=False)


def normalize_gross(imbd_data):
    imbd_data[["Gross"]] = imbd_data[["Gross"]] / 10000000
    return imbd_data


def prepare_dataset():
    df = pd.read_csv('imdb_top_1000.csv')
    df = drop_relevant_columns(df)
    df_lowercase = lowercase_columns_names(df)
    df = data_to_numeric(df_lowercase)
    df = normalize_gross(df)
    return df


class LinearRegressionModel(torch.nn.Module):

    def __init__(self):
        super(LinearRegressionModel, self).__init__()
        self.linear = torch.nn.Linear(1, 1)  # One in and one out

    def forward(self, x):
        y_pred = self.linear(x)
        return y_pred


df = prepare_dataset()
data_train, data_test = train_test_split(df, random_state=1, shuffle=True)

X_train = pd.DataFrame(data_train["Meta_score"], dtype=np.float64)
X_train = X_train.to_numpy()

y_train = pd.DataFrame(data_train["Gross"], dtype=np.float64)
y_train = y_train.to_numpy()

X_train = X_train.reshape(-1, 1)
y_train = y_train.reshape(-1, 1)

X_train = torch.from_numpy(X_train.astype(np.float32)).view(-1, 1)
y_train = torch.from_numpy(y_train.astype(np.float32)).view(-1, 1)

input_size = 1
output_size = 1

model = nn.Linear(input_size, output_size)

learning_rate = 0.0001
l = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)


# num_epochs = 1000
num_epochs = int(sys.argv[1])

for epoch in range(num_epochs):
    # forward feed
    y_pred = model(X_train.requires_grad_())

    # calculate the loss
    loss = l(y_pred, y_train)

    # backward propagation: calculate gradients
    loss.backward()

    # update the weights
    optimizer.step()

    # clear out the gradients from the last step loss.backward()
    optimizer.zero_grad()

    if epoch % 100 == 0:
        print('epoch {}, loss {}'.format(epoch, loss.item()))

predicted = model(X_train).detach().numpy()

pred = pd.DataFrame(predicted)
pred.to_csv('result.csv')

# save model
torch.save(model, "model.pkl")

# plt.scatter(X_train.detach().numpy() , y_train.detach().numpy())
# plt.plot(X_train.detach().numpy() , predicted , "red")
# plt.xlabel("Meta_score")
# plt.ylabel("Gross")
# plt.show()