import os
import pandas as pd
import torch
import torch.nn as nn

class SimpleNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(SimpleNN, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, output_size)
        self.softmax = nn.Softmax(dim=1)

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

input_size = 20 
hidden_size = 50
output_size = 4 

model = SimpleNN(input_size, hidden_size, output_size)
model.load_state_dict(torch.load("model.pth"))
model.eval() 

file_path = os.path.join("C:", os.sep, "Users", "reyva", "OneDrive", "Pulpit", "studia", "InzynieriaUczeniaMaszynowego", 'Test1.csv')
test_data = pd.read_csv(file_path)

inputs = torch.tensor(test_data.drop(['price_range', 'ID'], axis=1).values, dtype=torch.float32)
with torch.no_grad():
    predictions = model(inputs)
predicted_classes = torch.argmax(predictions, dim=1)

predicted_classes_df = pd.DataFrame(predicted_classes.numpy(), columns=['Predicted_Price_Range'])

predicted_classes_df['Actual_Price_Range'] = test_data['price_range'].values

output_path = os.path.join("C:", os.sep, "Users", "reyva", "OneDrive", "Pulpit", "studia", "InzynieriaUczeniaMaszynowego", 'predictions.csv')
predicted_classes_df.to_csv(output_path, index=False)