app.py

"""
Lead.AI Fraud Shield — Live Demo
Explainable AI fraud detection for small business transactions.
Visit https://www.lead-ai.us for a custom deployment.
"""

import gradio as gr
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
import warnings
warnings.filterwarnings("ignore")

# ── Train a demo model on synthetic data ─────────────────────────────────────
np.random.seed(42)
n = 2000

amounts     = np.concatenate([np.random.uniform(1, 500, 1700), np.random.uniform(500, 5000, 300)])
hours       = np.concatenate([np.random.randint(8, 22, 1700), np.random.randint(0, 6, 300)])
freq_7d     = np.concatenate([np.random.randint(1, 8, 1700), np.random.randint(10, 30, 300)])
is_new      = np.concatenate([np.random.binomial(1, 0.2, 1700), np.random.binomial(1, 0.8, 300)])
intl        = np.concatenate([np.random.binomial(1, 0.05, 1700), np.random.binomial(1, 0.6, 300)])
labels      = np.concatenate([np.zeros(1700), np.ones(300)])

X = np.column_stack([amounts, hours, freq_7d, is_new, intl])
y = labels

model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X, y)

MERCHANT_TYPES = ["Retail", "Restaurant", "Online Store", "Gas Station",
                  "ATM / Cash", "Subscription Service", "Unknown"]

RISK_REASONS = {
    "amount":   ("Amount is unusually high for this merchant type",
                 "Transaction amount is within normal range"),
    "hour":     ("Transaction occurred outside normal business hours",
                 "Transaction time is within normal hours"),
    "freq_7d":  ("Unusually high transaction frequency in the last 7 days",
                 "Transaction frequency is normal"),
    "is_new":   ("Payment method was registered recently",
                 "Established payment method with history"),
    "intl":     ("International transaction detected",
                 "Domestic transaction"),
}

def analyze_transaction(amount, merchant_type, hour, freq_7d, is_new_card, is_international):
    features = np.array([[amount, hour, freq_7d,
                          int(is_new_card), int(is_international)]])
    prob      = model.predict_proba(features)[0][1]
    risk_pct  = round(prob * 100, 1)

    if risk_pct < 25:
        level, color, verdict = "LOW", "🟢", "APPROVED"
    elif risk_pct < 60:
        level, color, verdict = "MEDIUM", "🟡", "REVIEW RECOMMENDED"
    else:
        level, color, verdict = "HIGH", "🔴", "FLAG FOR INVESTIGATION"

    # Plain-English explanation
    thresholds = {
        "amount":  (amount > 800,   amount,    "$"),
        "hour":    (hour < 6 or hour > 22, hour, "h"),
        "freq_7d": (freq_7d > 8,    freq_7d,   " txns/7d"),
        "is_new":  (is_new_card,    "",        ""),
        "intl":    (is_international, "",      ""),
    }

    flags, clears = [], []
    for key, (triggered, val, unit) in thresholds.items():
        if triggered:
            flags.append(f"⚠️  {RISK_REASONS[key][0]}")
        else:
            clears.append(f"✓  {RISK_REASONS[key][1]}")

    explanation  = f"## {color} Risk Level: {level} ({risk_pct}%)\n\n"
    explanation += f"**Verdict: {verdict}**\n\n"
    explanation += "---\n\n"
    explanation += "### Why This Score?\n\n"

    if flags:
        explanation += "**Risk Factors Detected:**\n"
        explanation += "\n".join(flags) + "\n\n"
    if clears:
        explanation += "**Factors Within Normal Range:**\n"
        explanation += "\n".join(clears) + "\n\n"

    explanation += "---\n\n"
    explanation += "> ⚠️ This is a demo system. "
    explanation += "Your production Lead.AI Fraud Shield will be trained on your actual transaction history.\n\n"
    explanation += "**[→ Get a Custom Fraud Shield for Your Business](https://www.lead-ai.us)**"

    bar = f"Risk Score: {'█' * int(risk_pct // 5)}{'░' * (20 - int(risk_pct // 5))} {risk_pct}%"
    return bar, explanation


# ── Gradio UI ─────────────────────────────────────────────────────────────────
with gr.Blocks(
    title="Lead.AI Fraud Shield",
    theme=gr.themes.Soft(primary_hue="red"),
    css=".footer { text-align:center; margin-top:20px; color:#666; }"
) as demo:

    gr.Markdown("""
# 🛡 Lead.AI Fraud Shield
### Explainable AI Fraud Detection for Small Businesses

Enter a transaction below. The AI will score it for risk and explain exactly why — in plain English.

> 💼 This is a live proof-of-concept. [Request a custom system →](https://www.lead-ai.us)
""")

    with gr.Row():
        with gr.Column():
            gr.Markdown("### Transaction Details")
            amount       = gr.Slider(1, 5000, value=120, step=1,
                                     label="Transaction Amount ($)")
            merchant     = gr.Dropdown(MERCHANT_TYPES, value="Retail",
                                       label="Merchant Type")
            hour         = gr.Slider(0, 23, value=14, step=1,
                                     label="Hour of Day (0=midnight, 14=2pm)")
            freq_7d_in   = gr.Slider(1, 30, value=3, step=1,
                                     label="Transactions in Last 7 Days (same card)")
            is_new_card  = gr.Checkbox(label="New Payment Method (registered < 7 days ago)")
            is_intl      = gr.Checkbox(label="International Transaction")
            btn          = gr.Button("🔍 Analyze Transaction", variant="primary", size="lg")

        with gr.Column():
            gr.Markdown("### AI Analysis")
            score_bar  = gr.Textbox(label="Risk Score", lines=1)
            result_md  = gr.Markdown()

    btn.click(
        fn=analyze_transaction,
        inputs=[amount, merchant, hour, freq_7d_in, is_new_card, is_intl],
        outputs=[score_bar, result_md],
    )

    gr.Examples(
        examples=[
            [4800, "ATM / Cash",       2,  18, True,  True],
            [45,   "Restaurant",       12,  2, False, False],
            [299,  "Online Store",     20,  5, False, False],
            [1500, "Unknown",           3, 15, True,  True],
        ],
        inputs=[amount, merchant, hour, freq_7d_in, is_new_card, is_intl],
        label="Try These Examples",
    )

    gr.Markdown("""
---
<div class="footer">
🌐 <a href="https://www.lead-ai.us">www.lead-ai.us</a> &nbsp;|&nbsp;
💻 <a href="https://github.com/Lead-AI-US/lead-ai-fraud-shield">GitHub</a> &nbsp;|&nbsp;
🤗 <a href="https://huggingface.co/lead-ai-labs">Hugging Face</a>
<br><br>
<strong>Need this customized for your business?</strong>
<a href="https://www.lead-ai.us">Request a Custom Lead.AI Setup →</a>
</div>
""")

if __name__ == "__main__":
    demo.launch()