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app.py

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+"""
+Lead.AI Customer Predictor — Live Demo
+Predicts customer churn and purchase likelihood for small businesses.
+Visit https://www.lead-ai.us for a custom deployment.
+"""
+
+import gradio as gr
+import numpy as np
+from sklearn.ensemble import GradientBoostingClassifier
+import warnings
+warnings.filterwarnings("ignore")
+
+# ── Train demo models ─────────────────────────────────────────────────────────
+np.random.seed(99)
+n = 3000
+
+tenure      = np.random.randint(1, 60, n)
+purchases   = np.random.randint(0, 50, n)
+avg_spend   = np.random.uniform(10, 800, n)
+support_tix = np.random.randint(0, 15, n)
+days_since  = np.random.randint(1, 180, n)
+email_opens = np.random.uniform(0, 1, n)
+
+X = np.column_stack([tenure, purchases, avg_spend, support_tix, days_since, email_opens])
+
+# Churn: high support + long since last purchase + low email engagement
+churn_score = (support_tix * 0.3 + days_since * 0.01 - purchases * 0.05
+               - email_opens * 0.5 - tenure * 0.005)
+y_churn     = (churn_score > np.percentile(churn_score, 65)).astype(int)
+
+# Purchase: recent + engaged + history
+buy_score   = (purchases * 0.4 + email_opens * 0.3 - days_since * 0.008 + avg_spend * 0.001)
+y_buy       = (buy_score > np.percentile(buy_score, 50)).astype(int)
+
+churn_model = GradientBoostingClassifier(n_estimators=100, random_state=42)
+churn_model.fit(X, y_churn)
+
+buy_model = GradientBoostingClassifier(n_estimators=100, random_state=42)
+buy_model.fit(X, y_buy)
+
+SEGMENTS = {
+    (False, True):  ("🌟 High-Value Active",    "This customer is engaged and ready to buy. Prioritize for upsell offers."),
+    (False, False): ("✅ Stable Retained",       "Low churn risk but not currently primed to buy. Nurture with content."),
+    (True,  True):  ("⚡ At-Risk, Still Buying", "Buying but showing churn signals. Act now with a retention offer."),
+    (True,  False): ("🚨 High Churn Risk",       "This customer is disengaging. Send a personal win-back message today."),
+}
+
+def predict_customer(tenure, purchases, avg_spend, support_tickets,
+                     days_since_purchase, email_open_rate):
+    feats = np.array([[tenure, purchases, avg_spend, support_tickets,
+                       days_since_purchase, email_open_rate]])
+
+    churn_prob = churn_model.predict_proba(feats)[0][1]
+    buy_prob   = buy_model.predict_proba(feats)[0][1]
+
+    churn_pct  = round(churn_prob * 100, 1)
+    buy_pct    = round(buy_prob   * 100, 1)
+
+    is_churn = churn_pct >= 50
+    is_buy   = buy_pct   >= 50
+
+    segment, action = SEGMENTS[(is_churn, is_buy)]
+
+    churn_bar = f"{'█' * int(churn_pct // 5)}{'░' * (20 - int(churn_pct // 5))} {churn_pct}%"
+    buy_bar   = f"{'█' * int(buy_pct   // 5)}{'░' * (20 - int(buy_pct   // 5))} {buy_pct}%"
+
+    report  = f"## {segment}\n\n"
+    report += f"**Recommended Action:** {action}\n\n"
+    report += "---\n\n"
+    report += "### Prediction Scores\n\n"
+    report += f"**Churn Risk:**    `{churn_bar}`\n\n"
+    report += f"**Purchase Likelihood:** `{buy_bar}`\n\n"
+    report += "---\n\n"
+    report += "### Key Signals\n\n"
+
+    signals = []
+    if days_since_purchase > 60:
+        signals.append(f"⚠️  Last purchase was **{days_since_purchase} days ago** — engagement is dropping")
+    if support_tickets >= 5:
+        signals.append(f"⚠️  **{support_tickets} support tickets** — customer may be frustrated")
+    if email_open_rate < 0.2:
+        signals.append("⚠️  **Low email engagement** — re-engagement campaign recommended")
+    if purchases >= 10:
+        signals.append(f"✓  **{purchases} purchases** — loyal customer history")
+    if email_open_rate >= 0.5:
+        signals.append("✓  **High email engagement** — customer is paying attention")
+    if tenure >= 12:
+        signals.append(f"✓  **{tenure} months** customer — long-term relationship")
+
+    if signals:
+        report += "\n".join(signals) + "\n\n"
+
+    report += "---\n\n"
+    report += "> ⚠️ Demo model trained on synthetic data. Your production system will learn "
+    report += "from your actual customer history for accurate predictions.\n\n"
+    report += "**[→ Get a Custom Customer Predictor for Your Business](https://www.lead-ai.us)**"
+
+    return report
+
+
+with gr.Blocks(
+    title="Lead.AI Customer Predictor",
+    theme=gr.themes.Soft(primary_hue="blue"),
+    css=".footer { text-align:center; margin-top:20px; color:#666; }"
+) as demo:
+
+    gr.Markdown("""
+# 🎯 Lead.AI Customer Predictor
+### Know Which Customers Are About to Leave — Before They Do
+
+Enter customer data below. The AI will predict churn risk, purchase likelihood,
+and tell you exactly what action to take.
+
+> 💼 This is a live proof-of-concept. [Request a custom system →](https://www.lead-ai.us)
+""")
+
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### Customer Profile")
+            tenure_in    = gr.Slider(1, 60, value=12, step=1,
+                                     label="Customer Tenure (months)")
+            purchases_in = gr.Slider(0, 50, value=8, step=1,
+                                     label="Total Purchases")
+            avg_spend_in = gr.Slider(10, 800, value=150, step=5,
+                                     label="Average Order Value ($)")
+            support_in   = gr.Slider(0, 15, value=1, step=1,
+                                     label="Support Tickets (last 90 days)")
+            days_in      = gr.Slider(1, 180, value=30, step=1,
+                                     label="Days Since Last Purchase")
+            email_in     = gr.Slider(0.0, 1.0, value=0.4, step=0.05,
+                                     label="Email Open Rate (0 = never, 1 = always)")
+            btn          = gr.Button("🔍 Analyze This Customer", variant="primary", size="lg")
+
+        with gr.Column():
+            gr.Markdown("### AI Prediction")
+            report_md = gr.Markdown()
+
+    btn.click(
+        fn=predict_customer,
+        inputs=[tenure_in, purchases_in, avg_spend_in, support_in, days_in, email_in],
+        outputs=report_md,
+    )
+
+    gr.Examples(
+        examples=[
+            [2,  1,  45,  8, 90, 0.05],
+            [36, 28, 320, 0,  5, 0.75],
+            [8,  12, 180, 4, 45, 0.30],
+            [18,  3,  90, 0, 120, 0.10],
+        ],
+        inputs=[tenure_in, purchases_in, avg_spend_in, support_in, days_in, email_in],
+        label="Try These Customer Profiles",
+    )
+
+    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-customer-predictor">GitHub</a> &nbsp;|&nbsp;
+🤗 <a href="https://huggingface.co/lead-ai-labs">Hugging Face</a>
+<br><br>
+<strong>Need this running on your real customer data?</strong>
+<a href="https://www.lead-ai.us">Request a Custom Lead.AI Setup →</a>
+</div>
+""")
+
+if __name__ == "__main__":
+    demo.launch()