import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import pickle

# Define the neural network model
class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.fc1 = nn.Linear(1, 64)
        self.fc2 = nn.Linear(64, 1)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        return x

# Define a custom dataset
class CustomDataset(Dataset):
    def __init__(self, X, y):
        self.X = torch.FloatTensor(X.values.reshape(-1, 1))
        self.y = torch.FloatTensor(y.values.reshape(-1, 1))

    def __len__(self):
        return len(self.X)

    def __getitem__(self, idx):
        return self.X[idx], self.y[idx]

# Load the dataset
df = pd.read_csv('data.csv')

# Select the relevant columns (e.g., 'Rating' and 'Writer')
data = df[['Rating', 'Writer']]

# Drop rows with missing values
data = data.dropna()

# Convert the 'Writer' column to numeric using label encoding
encoder = LabelEncoder()
data['Writer'] = encoder.fit_transform(data['Writer'])

# Split the data into training and testing sets
X = data['Writer']
y = data['Rating']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create the model instance
model = Model()

# Define the loss function and optimizer
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters())

# Create dataloaders for training and testing
train_dataset = CustomDataset(X_train, y_train)
test_dataset = CustomDataset(X_test, y_test)
train_dataloader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_dataloader = DataLoader(test_dataset, batch_size=64)

# Train the model
for epoch in range(10):
    model.train()
    for inputs, targets in train_dataloader:
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()

# Save the model to a file
torch.save(model.state_dict(), 'model.pth')

# Save the encoder to a file
with open('encoder.pkl', 'wb') as f:
    pickle.dump(encoder, f)

# Make predictions on new data
new_writer = 'Jim Cash'
new_writer_encoded = torch.tensor(encoder.transform([new_writer])).float()

model.eval()
rating_prediction = model(new_writer_encoded)
print("Predicted rating for the writer 'Jim Cash':", rating_prediction.item())