app.py

# app.py
import gradio as gr
import pandas as pd
import numpy as np
import pickle
from datetime import datetime

# Load model
with open("anomaly_detector_rf_model.pkl", "rb") as f:
    model = pickle.load(f)

def detect_anomalies(df):
    df["log_amount"] = np.log1p(df["amount"])
    df["amount_zscore"] = (df["amount"] - df["amount"].mean()) / df["amount"].std()
    df["transaction_date"] = pd.to_datetime(df["transaction_date"])
    df["day_of_week"] = df["transaction_date"].dt.dayofweek
    df["hour"] = df["transaction_date"].dt.hour
    df["is_weekend"] = df["day_of_week"].isin([5, 6]).astype(int)

    features = ["amount", "log_amount", "amount_zscore", "day_of_week", "hour", "is_weekend"]
    df["is_anomalous"] = model.predict(df[features])

    anomalies = df[df["is_anomalous"] == 1][["transaction_id", "amount", "merchant", "location", "transaction_date"]]

    # Save anomalies to CSV
    csv_path = "/tmp/anomalies.csv"
    anomalies.to_csv(csv_path, index=False)

    return anomalies, csv_path

# Summary function
def generate_summary(df):
    total = len(df)
    anomalous = df["is_anomalous"].sum()
    return f"Total Transactions: {total} | 🚨 Anomalies: {anomalous} | ⚠️ Rate: {anomalous/total:.2%}"

# Gradio UI
with gr.Blocks() as app:
    gr.Markdown("## 💸 Financial Transaction Anomaly Detector")
    gr.Markdown("Upload your CSV with columns: `transaction_id`, `amount`, `merchant`, `location`, `transaction_date`")

    with gr.Row():
        file_input = gr.File(file_types=[".csv"], label="📁 Upload Transaction File")
        download_button = gr.File(label="⬇️ Download Anomalies CSV")

    summary_text = gr.Textbox(label="📊 Summary", interactive=False)
    output_table = gr.Dataframe(label="🚨 Detected Anomalies")

    def app_interface(file):
        df = pd.read_csv(file.name)
        anomalies, csv_path = detect_anomalies(df)
        summary = generate_summary(df)
        return summary, anomalies, csv_path

    file_input.change(fn=app_interface, inputs=[file_input], outputs=[summary_text, output_table, download_button])

app.launch()