app.py

# Run the training script placed in the same directory as app.py
# The training script will train and persist a linear regression
# model with the filename 'model.joblib'
subprocess.run(['python', 'train.py'])

# Load the freshly trained model from disk
model = joblib.load("model.joblib")

# Define the predict function
def predict(csv_filename):
    # Read the CSV file
    df = pd.read_csv(csv_filename, header=None)

    # Convert the DataFrame to a list of floats
    client_data = df.iloc[0].tolist()

    # Check if the length of client_data is 29
    if len(client_data) != 29:
        raise ValueError("The CSV file must contain exactly 29 values.")

    # Unpack the list of values
    V1, V2, V3, V4, V5, V6, V7, V8, V9, V10, V11, V12, V13, V14, V15, V16, V17, V18, V19, V20, V21, V22, V23, V24, V25, V26, V27, V28, Amount = client_data

    # Create the data dictionary
    data = {
        'V1': V1, 'V2': V2, 'V3': V3, 'V4': V4, 'V5': V5, 'V6': V6, 'V7': V7, 'V8': V8, 'V9': V9, 'V10': V10,
        'V11': V11, 'V12': V12, 'V13': V13, 'V14': V14, 'V15': V15, 'V16': V16, 'V17': V17, 'V18': V18, 'V19': V19, 'V20': V20,
        'V21': V21, 'V22': V22, 'V23': V23, 'V24': V24, 'V25': V25, 'V26': V26, 'V27': V27, 'V28': V28, 'Amount': Amount
    }

    # Convert the data dictionary to a DataFrame
    input_df = pd.DataFrame([data])

    # Make predictions using the loaded model
    prediction = model.predict(input_df)

    return prediction[0], Amount  # Return both the prediction and Amount

# Define a function to map the names to their respective CSV filenames
def get_csv_filename(name):
    name_to_filename = {
        'Ted': 'Ted.csv',
        'Bill': 'Bill.csv',
        'Jill': 'Jill.csv',
        'Juan': 'Juan.csv'
    }
    return name_to_filename.get(name, 'Ted.csv')  # Default to 'Ted.csv' if name not found

# Define the Gradio interface function for single prediction
def gradio_predict(name):
    csv_filename = get_csv_filename(name)
    prediction, amount = predict(csv_filename)
    return f"The flagged transaction amount is {amount} and the prediction is {prediction}"

# Define the function for bulk analysis
def bulk_analysis(file):
    # Read the uploaded CSV file
    df = pd.read_csv(file.name)

    # Assuming the last column is 'Amount' and the rest are features
    X_test = df.iloc[:, :-1]
    y_test = df.iloc[:, -1]

    # Make predictions using the loaded model
    y_test_pred = model.predict(X_test)

    # Debugging: Print counts of anomalies in actual and predicted
    actual_anomalies = sum(y_test == 1)
    predicted_anomalies = sum(y_test_pred == 1)
    print(f"Actual anomalies: {actual_anomalies}, Predicted anomalies: {predicted_anomalies}")

    # Find rows where actual and predicted are both 1
    correctly_predicted_anomalies = X_test[(y_test == 1) & (y_test_pred == 1)]
    print(f"Correctly predicted anomalies: {len(correctly_predicted_anomalies)}")

    # Save the results to a CSV file
    result_filename = "correct_anomalies.csv"
    correctly_predicted_anomalies.to_csv(result_filename, index=False)

    return result_filename  # Return the path to the saved file


# Create the Gradio interface
iface = gr.Interface(
    fn=gradio_predict,
    inputs=gr.Dropdown(choices=['Ted', 'Bill', 'Jill', 'Juan'], label="Select a name"),
    outputs="text"
)

# Add the bulk analysis upload interface
bulk_iface = gr.Interface(
    fn=bulk_analysis,
    inputs=gr.File(label="Bulk Analysis"),
    outputs=gr.File(label="Download Results")
)

# Combine the interfaces
combined_iface = gr.TabbedInterface(
    [iface, bulk_iface],
    tab_names=["Single Prediction", "Bulk Analysis"]
)

# Launch the interface
combined_iface.launch(share=True)