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app.py

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+import os
+import joblib
+import pandas as pd
+from typing import Any, Dict, List
+from imblearn.over_sampling import SMOTE
+from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+import gradio as gr
+
+# Constants for directories and file names
+DIR = 'C:\\Users\\Afshin\\Desktop\\10_Projects\\Project_4_Fraud_Detection_Etherium\\'
+MODEL_DIR = os.path.join(DIR, 'models')
+DATA_DIR = os.path.join(DIR, 'datasets')
+DATA_FILE = 'cleaned_transaction_dataset.csv'
+MODEL_NAMES = [
+    'Ada Boost Classifier',
+    'Extra Trees Classifier',
+    'Gradient Boosting Classifier',
+    'LGBM Classifier', 
+    'Random Forest Classifier',
+    'XGBoost Classifier', 
+]
+
+# Load dataset
+data_path = os.path.join(DATA_DIR, DATA_FILE)
+df = pd.read_csv(data_path)
+
+# Load models
+def load_models(model_names: List[str]) -> Dict[str, Any]:
+    """Load machine learning models from disk."""
+    models = {}
+    for name in model_names:
+        path = os.path.join(MODEL_DIR, f"{name.replace(' ', '')}.joblib")
+        try:
+            models[name] = joblib.load(path)
+        except Exception as e:
+            print(f"Error loading model {name}: {str(e)}")
+    return models
+
+models = load_models(MODEL_NAMES)
+
+# Prepare features and target
+X = df.drop(columns=['FLAG'])
+y = df['FLAG']
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=123)
+
+# Standardize the features
+scaler = StandardScaler().fit(X_train)
+
+# Prediction and metrics evaluation function
+def calculate_metrics(y_true, y_pred, average_type='binary'):
+    """Calculate and return accuracy, recall, F1, and precision scores."""
+    acc = accuracy_score(y_true, y_pred)
+    rec = recall_score(y_true, y_pred, average=average_type)
+    f1 = f1_score(y_true, y_pred, average=average_type)
+    prec = precision_score(y_true, y_pred, average=average_type)
+    return acc, rec, f1, prec
+
+def load_and_predict(input_data):
+    try:
+        # Scale the input sample using the already-fitted scaler
+        sample_trans = scaler.transform(input_data)
+
+        # Using SMOTE to handle class imbalance
+        X_resampled, y_resampled = SMOTE(random_state=123).fit_resample(scaler.transform(X_train), y_train)
+
+        results = []
+
+        for name, model in models.items():
+            flag_pred = model.predict(sample_trans)
+            y_resampled_pred = model.predict(X_resampled)
+            acc, rec, f1, prec = calculate_metrics(y_resampled, y_resampled_pred)
+
+            results.append({
+                'Model': name,
+                'Predicted Fraud': 'Yes' if flag_pred[0] == 1 else 'No',
+                'Accuracy %': acc * 100, 
+                'Recall %': rec * 100, 
+                'F1 %': f1 * 100, 
+                'Precision %': prec * 100
+            })
+
+        return pd.DataFrame(results).sort_values(by='Accuracy %', ascending=False)
+
+    except Exception as e:
+        return f"An error occurred during prediction: {str(e)}"
+
+# Gradio interface
+def predict(avg_min_sent, avg_min_received, time_diff, sent_tnx, received_tnx, num_created_contracts,
+            max_value_received, avg_value_received, avg_value_sent, total_sent,
+            total_balance, erc20_received, erc20_sent, erc20_sent_contract,
+            erc20_unique_sent, erc20_unique_received):
+
+    input_features = [
+        avg_min_sent,
+        avg_min_received,
+        time_diff,
+        sent_tnx,
+        received_tnx,
+        num_created_contracts,
+        max_value_received,
+        avg_value_received,
+        avg_value_sent,
+        total_sent,
+        total_balance,
+        erc20_received,
+        erc20_sent,
+        erc20_sent_contract,
+        erc20_unique_sent,
+        erc20_unique_received
+    ]
+    
+    input_data = pd.DataFrame([input_features])
+    results_df = load_and_predict(input_data)
+
+    return results_df
+
+# Gradio inputs based on the features you have
+inputs = [
+    gr.Number(label="Avg min between sent tnx", value=df["Avg min between sent tnx"].mean()),
+    gr.Number(label="Avg min between received tnx", value=df["Avg min between received tnx"].mean()),
+    gr.Number(label="Time difference between first and last (mins)", value=df["Time difference between first and last (mins)"].mean()),
+    gr.Number(label="Sent tnx", value=df["Sent tnx"].mean()),
+    gr.Number(label="Received tnx", value=df["Received tnx"].mean()),
+    gr.Number(label="Number of created contracts", value=int(df["Number of created contracts"].mean())),
+    gr.Number(label="Max value received", value=df["Max value received"].mean()),
+    gr.Number(label="Avg value received", value=df["Avg value received"].mean()),
+    gr.Number(label="Avg value sent", value=df["Avg value sent"].mean()),
+    gr.Number(label="Total either sent", value=df["Total either sent"].mean()),
+    gr.Number(label="Total either balance", value=df["Total either balance"].mean()),
+    gr.Number(label="ERC20 total either received", value=df["ERC20 total either received"].mean()),
+    gr.Number(label="ERC20 total either sent", value=df["ERC20 total either sent"].mean()),
+    gr.Number(label="ERC20 total either sent contract", value=df["ERC20 total either sent contract"].mean()),
+    gr.Number(label="ERC20 unique sent address", value=df["ERC20 unique sent address"].mean()),
+    gr.Number(label="ERC20 unique received token name", value=df["ERC20 unique received token name"].mean()),
+]
+
+output = gr.Dataframe(label="Prediction Results")
+
+# Create the Gradio interface
+gr.Interface(
+    fn=predict, 
+    inputs=inputs, 
+    outputs=output,
+    title="Fraud Detection Etherium Prediction App",
+    description="This application predicts fraud in Ethereum transactions using multiple machine learning models.",
+    theme="compact"
+).launch()

requirements.txt

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+pandas
+numpy
+joblib
+scikit-learn
+imbalanced-learn
+lightgbm 
+xgboost
+catboost