import streamlit as st import pandas as pd import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from torch_geometric.nn import GATConv from torch_geometric.data import Data import os # Define FraudGNN class class FraudGNN(nn.Module): def __init__(self, input_dim, hidden_dim, output_dim): super(FraudGNN, self).__init__() self.conv1 = GATConv(input_dim, hidden_dim, heads=4, dropout=0.3) self.conv2 = GATConv(hidden_dim * 4, hidden_dim, heads=1, dropout=0.3) self.fc = nn.Linear(hidden_dim, output_dim) def forward(self, data): x, edge_index = data.x, data.edge_index x = F.relu(self.conv1(x, edge_index)) x = F.dropout(x, p=0.3, training=self.training) x = F.relu(self.conv2(x, edge_index)) x = self.fc(x) return torch.sigmoid(x).squeeze() # Device configuration device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # Load model and threshold try: # Try root directory first (Hugging Face Spaces working directory) model_path = 'fraud_gnn_model.pth' threshold_path = 'optimal_threshold.txt' # Fallback: Try relative to src/ (if files are misplaced) if not os.path.exists(model_path): model_path = os.path.join(os.path.dirname(__file__), 'fraud_gnn_model.pth') if not os.path.exists(threshold_path): threshold_path = os.path.join(os.path.dirname(__file__), 'optimal_threshold.txt') # Alternative: If files are in a 'models/' folder (uncomment if applicable) # model_path = os.path.join(os.path.dirname(__file__), '..', 'models', 'fraud_gnn_model.pth') # threshold_path = os.path.join(os.path.dirname(__file__), '..', 'models', 'optimal_threshold.txt') if not os.path.exists(model_path): raise FileNotFoundError(f"Model file not found at {model_path}. Please upload fraud_gnn_model.pth to the repository root.") if not os.path.exists(threshold_path): raise FileNotFoundError(f"Threshold file not found at {threshold_path}. Please upload optimal_threshold.txt to the repository root.") model = FraudGNN(input_dim=7, hidden_dim=16, output_dim=1).to(device) model.load_state_dict(torch.load(model_path, map_location=device)) model.eval() with open(threshold_path, 'r') as f: threshold = float(f.read()) except FileNotFoundError as e: st.error(f"Error: {e}") st.stop() except Exception as e: st.error(f"Error loading model or threshold: {e}") st.stop() # City and state mappings city_mapping = { 'Atlanta': 0, 'Bronx': 1, 'Brooklyn': 2, 'Chicago': 3, 'Dallas': 4, 'Houston': 5, 'Indianapolis': 6, 'Las Vegas': 7, 'Los Angeles': 8, 'Louisville': 9, 'Miami': 10, 'Minneapolis': 11, 'New York': 12, 'ONLINE': 13, 'Orlando': 14, 'Philadelphia': 15, 'San Antonio': 16, 'San Diego': 17, 'San Francisco': 18, 'Tucson': 19, 'other': 20 } state_mapping = { 'AK': 0, 'AL': 1, 'AR': 2, 'AZ': 3, 'Algeria': 4, 'Antigua and Barbuda': 5, 'Argentina': 6, 'Aruba': 7, 'Australia': 8, 'Austria': 9, 'Azerbaijan': 10, 'Bahrain': 11, 'Bangladesh': 12, 'Barbados': 13, 'Belarus': 14, 'Belgium': 15, 'Belize': 16, 'Bosnia and Herzegovina': 17, 'Brazil': 18, 'CA': 19, 'CO': 20, 'CT': 21, 'Cabo Verde': 22, 'Cambodia': 23, 'Canada': 24, 'Central African Republic': 25, 'Chile': 26, 'China': 27, 'Colombia': 28, 'Costa Rica': 29, "Cote d'Ivoire": 30, 'Croatia': 31, 'Czech Republic': 32, 'DC': 33, 'DE': 34, 'Denmark': 35, 'Dominica': 36, 'Dominican Republic': 37, 'East Timor (Timor-Leste)': 38, 'Ecuador': 39, 'Egypt': 40, 'Eritrea': 41, 'Estonia': 42, 'FL': 43, 'Fiji': 44, 'Finland': 45, 'France': 46, 'GA': 47, 'Georgia': 48, 'Germany': 49, 'Ghana': 50, 'Greece': 51, 'Guatemala': 52, 'Guyana': 53, 'HI': 54, 'Haiti': 55, 'Honduras': 56, 'Hong Kong': 57, 'Hungary': 58, 'IA': 59, 'ID': 60, 'IL': 61, 'IN': 62, 'Iceland': 63, 'India': 64, 'Indonesia': 65, 'Ireland': 66, 'Israel': 67, 'Italy': 68, 'Jamaica': 69, 'Japan': 70, 'Jordan': 71, 'KS': 72, 'KY': 73, 'Kenya': 74, 'Kosovo': 75, 'Kuwait': 76, 'LA': 77, 'Latvia': 78, 'Lebanon': 79, 'Liberia': 80, 'Lithuania': 81, 'Luxembourg': 82, 'MA': 83, 'MD': 84, 'ME': 85, 'MI': 86, 'MN': 87, 'MO': 88, 'MS': 89, 'MT': 90, 'Macedonia': 91, 'Malaysia': 92, 'Malta': 93, 'Mexico': 94, 'Moldova': 95, 'Monaco': 96, 'Morocco': 97, 'Mozambique': 98, 'Myanmar (Burma)': 99, 'NC': 100, 'ND': 101, 'NE': 102, 'NH': 103, 'NJ': 104, 'NM': 105, 'NV': 106, 'NY': 107, 'Nauru': 108, 'Netherlands': 109, 'New Zealand': 110, 'Nicaragua': 111, 'Niger': 112, 'Nigeria': 113, 'Norway': 114, 'OH': 115, 'OK': 116, 'OR': 117, 'Oman': 118, 'PA': 119, 'Pakistan': 120, 'Panama': 121, 'Peru': 122, 'Philippines': 123, 'Poland': 124, 'Portugal': 125, 'RI': 126, 'Romania': 127, 'Russia': 128, 'SC': 129, 'SD': 130, 'Saudi Arabia': 131, 'Senegal': 132, 'Serbia': 133, 'Seychelles': 134, 'Singapore': 135, 'Slovakia': 136, 'Slovenia': 137, 'Somalia': 138, 'South Africa': 139, 'South Korea': 140, 'Spain': 141, 'Sri Lanka': 142, 'Sudan': 143, 'Suriname': 144, 'Sweden': 145, 'Switzerland': 146, 'Syria': 147, 'TN': 148, 'TX': 149, 'Taiwan': 150, 'Thailand': 151, 'The Bahamas': 152, 'Tunisia': 153, 'Turkey': 154, 'Tuvalu': 155, 'UT': 156, 'Uganda': 157, 'Ukraine': 158, 'United Arab Emirates': 159, 'United Kingdom': 160, 'Uruguay': 161, 'Uzbekistan': 162, 'VA': 163, 'VT': 164, 'Vatican City': 165, 'Vietnam': 166, 'WA': 167, 'WI': 168, 'WV': 169, 'WY': 170, 'Yemen': 171, 'Zimbabwe': 172 } def predict_fraud(transactions): try: df = pd.DataFrame(transactions, columns=[ 'Zipcode', 'Merchant_State_Code', 'User_Frequency_Per_Day', 'Time_Difference_Hours', 'Merchant_Category_Code', 'Merchant_City_Code', 'Transaction_Amount' ]) node_features = torch.tensor(df.values, dtype=torch.float).to(device) edge_index = torch.empty((2, 0), dtype=torch.long).to(device) if len(df) > 1: zipcodes = node_features[:, 0].cpu().numpy() edge_list = [] zipcode_threshold = 1000 for i in range(len(df)): for j in range(i + 1, len(df)): if abs(zipcodes[i] - zipcodes[j]) < zipcode_threshold: edge_list.append([i, j]) edge_list.append([j, i]) if edge_list: edge_index = torch.tensor(edge_list, dtype=torch.long).t().contiguous().to(device) graph_data = Data(x=node_features, edge_index=edge_index).to(device) if model is None: raise ValueError("Model not loaded. Check if fraud_gnn_model.pth exists.") with torch.no_grad(): out = model(graph_data) out = torch.atleast_1d(out) pred_binary = (out > threshold).float().cpu().numpy() pred_proba = out.cpu().numpy() pred_binary = np.atleast_1d(pred_binary) pred_proba = np.atleast_1d(pred_proba) return pred_binary, pred_proba except Exception as e: st.error(f"Error in predict_fraud: {e}") return None, None # Custom CSS for highly compact, eye-catching design st.markdown(""" """, unsafe_allow_html=True) # Streamlit UI st.markdown("""

FraudShield

Enter transaction details to detect fraud. Provide accurate zip code, merchant details, and amount.

""", unsafe_allow_html=True) with st.form(key="fraud_form"): st.markdown("
Zipcode
", unsafe_allow_html=True) zipcode = st.number_input("", value=91750.0, step=0.01, format="%.2f", key="zipcode") st.markdown("
Merchant State
", unsafe_allow_html=True) merchant_state = st.selectbox("", sorted(state_mapping.keys()), index=sorted(state_mapping.keys()).index("TX"), key="state") st.markdown("
User Frequency Per Day
", unsafe_allow_html=True) user_freq = st.number_input("", value=1.0, step=0.01, format="%.2f", key="freq") st.markdown("
Time Difference (Hours)
", unsafe_allow_html=True) time_diff = st.number_input("", value=16601.95, step=0.01, format="%.2f", key="time") st.markdown("
Merchant Category Code
", unsafe_allow_html=True) merchant_category = st.number_input("", value=5912.0, step=0.01, format="%.2f", key="category") st.markdown("
Merchant City
", unsafe_allow_html=True) merchant_city = st.selectbox("", sorted(city_mapping.keys()), index=sorted(city_mapping.keys()).index("Houston"), key="city") st.markdown("
Transaction Amount
", unsafe_allow_html=True) transaction_amount = st.number_input("", value=128.35, step=0.01, format="%.2f", key="amount") submit_button = st.form_submit_button("Predict Fraud", use_container_width=True) if submit_button: try: if not all([zipcode, user_freq, time_diff, merchant_category, transaction_amount]): st.error("All fields are required.") elif merchant_state not in state_mapping: st.error(f"Invalid Merchant State: {merchant_state}") elif merchant_city not in city_mapping: st.error(f"Invalid Merchant City: {merchant_city}") else: transaction = { 'Zipcode': float(zipcode), 'Merchant_State_Code': int(state_mapping[merchant_state]), 'User_Frequency_Per_Day': float(user_freq), 'Time_Difference_Hours': float(time_diff), 'Merchant_Category_Code': float(merchant_category), 'Merchant_City_Code': int(city_mapping[merchant_city]), 'Transaction_Amount': float(transaction_amount) } transactions = [list(transaction.values())] predictions, probabilities = predict_fraud(transactions) if predictions is None or probabilities is None: st.error("Prediction failed. Check server logs for details.") else: result = 'Fraud' if predictions[0] == 1 else 'Not Fraud' st.markdown(f"""

Transaction: {result}

Probability of Fraud: {probabilities[0]:.4f}

""", unsafe_allow_html=True) except Exception as e: st.error(f"Error: Invalid input - {str(e)}")