# Install required packages
!pip install -q transformers scikit-learn

# Imports
import pandas as pd
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MultiLabelBinarizer
from transformers import BertTokenizer, BertModel
from torch.optim import Adam

# Constants
TEXT_COLUMN = 'Sanction_Context'
LABEL_COLUMNS = [
    'Red_Flag_Reason',
    'Maker_Action',
    'Escalation_Level',
    'Risk_Category',
    'Risk_Drivers',
    'Investigation_Outcome'
]

# Load and clean data
df = pd.read_csv('/kaggle/input/systhesis/synthetic_transactions_samples_5000.csv')
df = df.dropna(subset=[TEXT_COLUMN])
df[LABEL_COLUMNS] = df[LABEL_COLUMNS].fillna('Unknown')  # Fill missing labels

# Encode labels using MultiLabelBinarizer
mlb = MultiLabelBinarizer()
Y = mlb.fit_transform(df[LABEL_COLUMNS].astype(str).values.tolist())
X = df[TEXT_COLUMN].tolist()

# Save the label classes for decoding later
import pickle
with open("mlb_classes.pkl", "wb") as f:
    pickle.dump(mlb.classes_, f)

# Tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Dataset Class
class BertMultiLabelDataset(Dataset):
    def __init__(self, texts, labels, tokenizer, max_len=128):
        self.texts = texts
        self.labels = labels
        self.tokenizer = tokenizer
        self.max_len = max_len

    def __getitem__(self, idx):
        encoding = self.tokenizer(
            self.texts[idx],
            padding='max_length',
            truncation=True,
            max_length=self.max_len,
            return_tensors="pt"
        )
        item = {key: val.squeeze(0) for key, val in encoding.items()}
        item['labels'] = torch.FloatTensor(self.labels[idx])
        return item

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

# Model Definition
class BertForMultiLabel(nn.Module):
    def __init__(self, num_labels):
        super().__init__()
        self.bert = BertModel.from_pretrained('bert-base-uncased')
        self.dropout = nn.Dropout(0.3)
        self.classifier = nn.Linear(self.bert.config.hidden_size, num_labels)

    def forward(self, input_ids, attention_mask):
        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
        pooled_output = self.dropout(outputs.pooler_output)
        logits = self.classifier(pooled_output)
        return logits

# Prepare data
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)

train_dataset = BertMultiLabelDataset(X_train, Y_train, tokenizer)
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)

# Device setup
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Training on device: {device}")

# Model, optimizer, loss
model = BertForMultiLabel(num_labels=Y.shape[1]).to(device)
optimizer = Adam(model.parameters(), lr=2e-5)
loss_fn = nn.BCEWithLogitsLoss()

# Training loop
for epoch in range(3):
    model.train()
    total_loss = 0
    for i, batch in enumerate(train_loader):
        input_ids = batch['input_ids'].to(device)
        attention_mask = batch['attention_mask'].to(device)
        labels = batch['labels'].to(device)

        optimizer.zero_grad()
        logits = model(input_ids, attention_mask)
        loss = loss_fn(logits, labels)
        loss.backward()
        optimizer.step()

        total_loss += loss.item()
        if i % 10 == 0:
            print(f"Epoch {epoch+1}, Step {i}, Loss: {loss.item():.4f}")

    avg_loss = total_loss / len(train_loader)
    print(f"Epoch {epoch+1} finished. Avg Loss: {avg_loss:.4f}")