Update app.py

app.py

@@ -1,12 +1,13 @@
 import os
 import sys
 import json
+import inspect
+
 
 # ============================================================
-# PROJECT PATH CONFIGURATION
+# PROJECT ROOT CONFIGURATION
 # ============================================================
 
-# Ensure project root is in sys.path BEFORE importing ML modules
 PROJECT_ROOT = os.path.abspath(os.path.dirname(__file__))
 
 if PROJECT_ROOT not in sys.path:
@@ -14,45 +15,38 @@ if PROJECT_ROOT not in sys.path:
 
 
 # ============================================================
-# DEPENDENCIES
+# GRADIO CONFIGURATION
 # ============================================================
 
-import spaces
-import gradio as gr
-
-from fastapi import FastAPI, HTTPException, Header, status, Query
-from pydantic import BaseModel
+# Prevent Gradio from attempting Server-Side Rendering where
+# supported. This is useful for FastAPI + mounted Gradio apps.
+os.environ["GRADIO_SSR_MODE"] = "False"
 
 
 # ============================================================
-# HUGGING FACE ZERO GPU STARTUP DETECTION
+# IMPORTS
 # ============================================================
 
-@spaces.GPU(duration=1)
-def _zerogpu_health_check():
-    """
-    ZeroGPU compatibility function.
-
-    This function exists so that Hugging Face ZeroGPU
-    can detect a GPU-decorated function during startup.
-
-    The actual Customer360 AI inference pipeline remains
-    CPU-based.
-    """
-    return "GPU available"
-
+import spaces
+import gradio as gr
 
-# ============================================================
-# ML INFERENCE SERVICE
-# ============================================================
+from fastapi import FastAPI, HTTPException, Header, status, Query
+from pydantic import BaseModel
 
 from ml.inference.unified_inference import inference_service
 
 
 # ============================================================
-# CONFIGURATION
+# API CONFIGURATION
 # ============================================================
 
+# IMPORTANT:
+# For production, set MODEL_API_KEY as a Hugging Face Space Secret.
+#
+# The fallback below is retained temporarily so the current
+# hackathon deployment continues to work if the Secret is not
+# configured yet.
+
 LIVE_API_KEY = os.getenv(
     "MODEL_API_KEY",
     "c360_live_key_9f8a2b7c4e1d"
@@ -74,20 +68,32 @@ fastapi_app = FastAPI(
 
 
 # ============================================================
-# HEALTH CHECK ENDPOINT
+# ZEROGPU COMPATIBILITY FUNCTION
+# ============================================================
+
+# ZeroGPU detects GPU-enabled functions through @spaces.GPU.
+#
+# Your current Customer360 models are already loaded successfully,
+# so this function does not perform the actual Customer360
+# inference. It simply keeps the Space compatible with ZeroGPU.
+
+@spaces.GPU(duration=1)
+def _zerogpu_health_check():
+    return "GPU available"
+
+
+# ============================================================
+# FASTAPI HEALTH ENDPOINT
 # ============================================================
 
 @fastapi_app.get("/health")
 def health_check():
-    """
-    Health endpoint for uptime monitoring.
-    """
 
     return {
         "status": "healthy",
         "service": "Customer360 AI Model",
         "models_loaded": inference_service.loaded,
-        "api_key_configured": True
+        "api_key_configured": bool(LIVE_API_KEY)
     }
 
 
@@ -101,79 +107,77 @@ def demo_predict(
     abandoned_carts: int = 2,
     support_tickets: int = 3
 ):
-    """
-    Demo prediction endpoint.
-
-    This endpoint allows external applications to test
-    the deployed Customer360 AI inference engine.
-    """
 
     cust_dict = {
+
         "customer_id": "DEMO_C1024",
+
         "name": "Ananya Sharma",
 
-        # ----------------------------
-        # RFM FEATURES
-        # ----------------------------
+        # -----------------------------
+        # RFM
+        # -----------------------------
 
         "recency_days": recency_days,
+
         "frequency_purchases": 12,
+
         "monetary_total_spend": 45000.0,
+
         "avg_order_value": 3750.0,
 
-        # ----------------------------
-        # WEBSITE BEHAVIOUR
-        # ----------------------------
+        # -----------------------------
+        # Engagement
+        # -----------------------------
 
         "website_visits_30d": 3,
+
         "product_views_30d": 5,
+
         "cart_additions_30d": 3,
+
         "cart_abandonments_30d": abandoned_carts,
 
-        # ----------------------------
-        # EMAIL ENGAGEMENT
-        # ----------------------------
+        # -----------------------------
+        # Communication
+        # -----------------------------
 
         "email_opens_30d": 1,
+
         "email_clicks_30d": 0,
 
-        # ----------------------------
-        # CUSTOMER SUPPORT
-        # ----------------------------
+        # -----------------------------
+        # Support
+        # -----------------------------
 
         "support_tickets_30d": support_tickets,
 
-        # ----------------------------
-        # ENGAGEMENT TREND
-        # ----------------------------
+        # -----------------------------
+        # Behavioral trend
+        # -----------------------------
 
         "engagement_change_pct": -65.0,
 
-        # ----------------------------
-        # CUSTOMER BEHAVIOUR
-        # ----------------------------
-
         "return_rate": 0.08,
+
         "discount_dependency_ratio": 0.35,
 
-        # ----------------------------
-        # SPENDING WINDOWS
-        # ----------------------------
+        # -----------------------------
+        # Spending
+        # -----------------------------
 
         "spend_30d": 0.0,
-        "spend_90d": 15000.0,
 
-        # ----------------------------
-        # PURCHASE VELOCITY
-        # ----------------------------
+        "spend_90d": 15000.0,
 
         "purchase_velocity": 30.0,
 
-        # ----------------------------
-        # PREFERENCES
-        # ----------------------------
+        # -----------------------------
+        # Preferences
+        # -----------------------------
 
         "preferred_channel": "WhatsApp",
+
         "preferred_category": "Electronics"
     }
 
@@ -202,15 +206,13 @@ def predict_gradio(
     preferred_category,
     api_key_input
 ):
-    """
-    Runs Customer360 AI inference through the Gradio UI.
-    """
 
     # ========================================================
     # API KEY VALIDATION
     # ========================================================
 
     if api_key_input != LIVE_API_KEY:
+
         return {
             "error": (
                 "Unauthorized: Invalid API Key. "
@@ -220,23 +222,20 @@ def predict_gradio(
 
 
     # ========================================================
-    # DATA TYPE CONVERSION
+    # CUSTOMER INPUT NORMALIZATION
     # ========================================================
 
-    frequency = int(frequency_purchases)
+    purchases = max(
+        1,
+        int(frequency_purchases)
+    )
 
-    monetary_total_spend = float(
+    total_spend = float(
         monetary_total_spend
     )
 
-
-    # ========================================================
-    # AVERAGE ORDER VALUE
-    # ========================================================
-
     avg_order_value = (
-        monetary_total_spend /
-        max(1, frequency)
+        total_spend / purchases
     )
 
 
@@ -246,109 +245,134 @@ def predict_gradio(
 
     cust_dict = {
 
-        # ----------------------------
-        # IDENTITY
-        # ----------------------------
+        "customer_id": (
+            customer_id
+            or "C1024"
+        ),
 
-        "customer_id": customer_id or "C1024",
-        "name": name or "Sample Customer",
+        "name": (
+            name
+            or "Sample Customer"
+        ),
 
-        # ----------------------------
+        # -----------------------------
         # RFM
-        # ----------------------------
+        # -----------------------------
 
-        "recency_days": int(recency_days),
+        "recency_days": int(
+            recency_days
+        ),
 
-        "frequency_purchases": frequency,
+        "frequency_purchases": int(
+            frequency_purchases
+        ),
 
-        "monetary_total_spend":
-            monetary_total_spend,
+        "monetary_total_spend": total_spend,
 
-        "avg_order_value":
-            avg_order_value,
+        "avg_order_value": avg_order_value,
 
-        # ----------------------------
-        # WEBSITE BEHAVIOUR
-        # ----------------------------
+        # -----------------------------
+        # Engagement
+        # -----------------------------
 
-        "website_visits_30d":
-            int(website_visits_30d),
+        "website_visits_30d": int(
+            website_visits_30d
+        ),
 
-        "product_views_30d":
-            int(product_views_30d),
+        "product_views_30d": int(
+            product_views_30d
+        ),
 
-        "cart_additions_30d":
-            int(cart_additions_30d),
+        "cart_additions_30d": int(
+            cart_additions_30d
+        ),
 
-        "cart_abandonments_30d":
-            int(cart_abandonments_30d),
+        "cart_abandonments_30d": int(
+            cart_abandonments_30d
+        ),
 
-        # ----------------------------
-        # EMAIL ENGAGEMENT
-        # ----------------------------
+        # -----------------------------
+        # Communication
+        # -----------------------------
 
         "email_opens_30d": 5,
-        "email_clicks_30d": 2,
 
-        # ----------------------------
-        # CUSTOMER SUPPORT
-        # ----------------------------
+        "email_clicks_30d": 2,
 
-        "support_tickets_30d":
-            int(support_tickets_30d),
+        # -----------------------------
+        # Support
+        # -----------------------------
 
-        # ----------------------------
-        # ENGAGEMENT
-        # ----------------------------
+        "support_tickets_30d": int(
+            support_tickets_30d
+        ),
 
-        "engagement_change_pct":
-            float(engagement_change_pct),
+        # -----------------------------
+        # Behavioral trend
+        # -----------------------------
 
-        # ----------------------------
-        # CUSTOMER BEHAVIOUR
-        # ----------------------------
+        "engagement_change_pct": float(
+            engagement_change_pct
+        ),
 
         "return_rate": 0.08,
 
         "discount_dependency_ratio": 0.35,
 
-        # ----------------------------
-        # SPENDING
-        # ----------------------------
+        # -----------------------------
+        # Spending
+        # -----------------------------
 
         "spend_30d": 0.0,
 
         "spend_90d": 15000.0,
 
-        # ----------------------------
-        # PURCHASE VELOCITY
-        # ----------------------------
-
         "purchase_velocity": 30.0,
 
-        # ----------------------------
-        # CUSTOMER PREFERENCES
-        # ----------------------------
+        # -----------------------------
+        # Preferences
+        # -----------------------------
 
-        "preferred_channel":
-            preferred_channel,
+        "preferred_channel": (
+            preferred_channel
+        ),
 
-        "preferred_category":
+        "preferred_category": (
             preferred_category
+        )
     }
 
 
     # ========================================================
-    # RUN ML INFERENCE
+    # RUN CUSTOMER360 ML PIPELINE
     # ========================================================
 
-    return inference_service.predict_customer_decision(
-        cust_dict
-    )
+    try:
+
+        result = (
+            inference_service
+            .predict_customer_decision(
+                cust_dict
+            )
+        )
+
+        return result
+
+    except Exception as e:
+
+        print(
+            "[Inference Error]",
+            repr(e)
+        )
+
+        return {
+            "error": "ML inference failed.",
+            "details": str(e)
+        }
 
 
 # ============================================================
-# GRADIO INTERFACE
+# GRADIO USER INTERFACE
 # ============================================================
 
 with gr.Blocks(
@@ -360,17 +384,29 @@ with gr.Blocks(
     # ========================================================
 
     gr.Markdown(
-        "# 🧠 Customer360 AI -- Live Model Decision Engine"
+        """
+        # 🧠 Customer360 AI -- Live Model Decision Engine
+        """
+    )
+
+    gr.Markdown(
+        """
+        **Production ML inference engine for customer
+        churn, purchase propensity, CLV, segmentation,
+        explainability, and Next Best Action.**
+        """
     )
 
     gr.Markdown(
-        "Direct Hugging Face 24/7 Live Deployment "
-        "with Uptime Monitoring & API Key Protection."
+        """
+        Direct Hugging Face live deployment with
+        API-key protected model inference.
+        """
     )
 
 
     # ========================================================
-    # MAIN UI
+    # MAIN LAYOUT
     # ========================================================
 
     with gr.Row():
@@ -381,6 +417,10 @@ with gr.Blocks(
 
         with gr.Column():
 
+            gr.Markdown(
+                "## Customer Information"
+            )
+
             api_key_input = gr.Textbox(
                 label="Live API Key",
                 value="c360_live_key_9f8a2b7c4e1d",
@@ -397,6 +437,15 @@ with gr.Blocks(
                 value="Ananya Sharma"
             )
 
+
+            # -----------------------------------------------
+            # RFM
+            # -----------------------------------------------
+
+            gr.Markdown(
+                "### RFM Signals"
+            )
+
             recency_days = gr.Number(
                 label="Days Inactive",
                 value=74
@@ -412,6 +461,15 @@ with gr.Blocks(
                 value=45000
             )
 
+
+            # -----------------------------------------------
+            # ENGAGEMENT
+            # -----------------------------------------------
+
+            gr.Markdown(
+                "### Engagement Signals"
+            )
+
             website_visits_30d = gr.Number(
                 label="30-Day Website Visits",
                 value=3
@@ -442,31 +500,41 @@ with gr.Blocks(
                 value=-65.0
             )
 
+
+            # -----------------------------------------------
+            # CUSTOMER PREFERENCES
+            # -----------------------------------------------
+
+            gr.Markdown(
+                "### Customer Preferences"
+            )
+
             preferred_channel = gr.Dropdown(
                 label="Preferred Channel",
-
                 choices=[
                     "WhatsApp",
                     "Email",
                     "Push",
                     "SMS"
                 ],
-
                 value="WhatsApp"
             )
 
             preferred_category = gr.Dropdown(
                 label="Preferred Category",
-
                 choices=[
                     "Electronics",
                     "Fashion",
                     "Home & Kitchen"
                 ],
-
                 value="Electronics"
             )
 
+
+            # -----------------------------------------------
+            # INFERENCE BUTTON
+            # -----------------------------------------------
+
             btn = gr.Button(
                 "🚀 Run Live Inference",
                 variant="primary"
@@ -479,13 +547,20 @@ with gr.Blocks(
 
         with gr.Column():
 
+            gr.Markdown(
+                "## Customer Decision Intelligence"
+            )
+
             output_json = gr.JSON(
-                label="ML Prediction & Next Best Action Result"
+                label=(
+                    "ML Prediction & "
+                    "Next Best Action Result"
+                )
             )
 
 
     # ========================================================
-    # BUTTON EVENT
+    # GRADIO EVENT
     # ========================================================
 
     btn.click(
@@ -509,54 +584,70 @@ with gr.Blocks(
             api_key_input
         ],
 
-        outputs=output_json
+        outputs=[
+            output_json
+        ]
     )
 
 
 # ============================================================
-# MOUNT GRADIO APPLICATION ON FASTAPI
+# MOUNT GRADIO ON FASTAPI
 # ============================================================
 
-app = gr.mount_gradio_app(
-    fastapi_app,
-    demo,
-    path="/"
-)
+# Gradio versions differ in whether mount_gradio_app()
+# accepts the ssr_mode parameter.
+#
+# We detect support dynamically so the same application works
+# with your current Gradio 4.44.x installation as well as newer
+# Gradio versions.
 
+mount_kwargs = {
+    "path": "/"
+}
 
-# ============================================================
-# APPLICATION ENTRY POINT
-# ============================================================
+try:
 
-if __name__ == "__main__":
+    mount_signature = inspect.signature(
+        gr.mount_gradio_app
+    )
 
-    import uvicorn
+    if "ssr_mode" in mount_signature.parameters:
 
+        mount_kwargs["ssr_mode"] = False
 
-    # ========================================================
-    # ZERO GPU STARTUP HOOK
-    # ========================================================
+        print(
+            "[Gradio] SSR explicitly disabled."
+        )
 
-    try:
+    else:
 
-        import spaces.zero as _spaces_zero
+        print(
+            "[Gradio] Current version does not "
+            "support ssr_mode parameter."
+        )
 
-        if hasattr(
-            _spaces_zero,
-            "startup"
-        ):
-            _spaces_zero.startup()
+except Exception as e:
 
-    except Exception as e:
+    print(
+        "[Gradio] Could not inspect mount_gradio_app:",
+        repr(e)
+    )
 
-        print(
-            f"[ZeroGPU] Startup hook warning: {e}"
-        )
 
+app = gr.mount_gradio_app(
+    fastapi_app,
+    demo,
+    **mount_kwargs
+)
 
-    # ========================================================
-    # SERVER CONFIGURATION
-    # ========================================================
+
+# ============================================================
+# APPLICATION ENTRY POINT
+# ============================================================
+
+if __name__ == "__main__":
+
+    import uvicorn
 
     port = int(
         os.environ.get(
@@ -565,10 +656,21 @@ if __name__ == "__main__":
         )
     )
 
+    print(
+        f"[Customer360 AI] Starting server on port {port}"
+    )
 
-    # ========================================================
-    # START SERVER
-    # ========================================================
+    print(
+        "[Customer360 AI] Models loaded:",
+        inference_service.loaded
+    )
+
+    print(
+        "[Customer360 AI] Gradio SSR mode:",
+        os.environ.get(
+            "GRADIO_SSR_MODE"
+        )
+    )
 
     uvicorn.run(
         app,