naidu9678/gnn-lstm-fraud-detection

Input: Transaction sequences (window=20, features=19)
         ↓
    BiLSTM × 2 layers (hidden=128, embed=64)
         ↓
    [Concat with GNN node embedding (dim=64)]
         ↓
    GraphSAGE × 2 layers (hidden=128, out=64)
         ↓
    Fusion MLP (128 → 64 → 2)
         ↓
    Fraud probability

import torch
from huggingface_hub import hf_hub_download

# Download model
path = hf_hub_download(repo_id="naidu9678/gnn-lstm-fraud-detection", filename="gnn_lstm_complete.pt")
checkpoint = torch.load(path, map_location="cpu", weights_only=False)

# Load config
cfg = checkpoint["config"]

# Rebuild model (copy class definitions from repo)
model = FraudDetectionSystem(cfg)
model.load_state_dict(checkpoint["model_state"])
model.eval()

# Inference on a single sequence
# x_seq shape: (1, 20, 19) — 1 sample, 20 timesteps, 19 features
x_seq   = torch.zeros(1, 20, 19)   # replace with real data
gnn_emb = torch.zeros(1, 64)       # node embedding or zeros
logits  = model(x_seq, gnn_emb)
prob    = torch.softmax(logits, dim=1)[0, 1].item()
print(f"Fraud probability: {prob:.4f}")