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app.py

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+"""
+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()