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| import os | |
| import sys | |
| from typing import Dict, Any, List, Optional | |
| from fastapi import FastAPI, HTTPException, Security, Depends, status, Query | |
| from fastapi.security import APIKeyHeader | |
| from fastapi.middleware.cors import CORSMiddleware | |
| from fastapi.responses import HTMLResponse, JSONResponse | |
| from pydantic import BaseModel, Field | |
| # Ensure project root is in sys.path | |
| PROJECT_ROOT = os.path.abspath(os.path.dirname(__file__)) | |
| if PROJECT_ROOT not in sys.path: | |
| sys.path.insert(0, PROJECT_ROOT) | |
| from ml.inference.unified_inference import inference_service | |
| # Configuration: Live API Key | |
| LIVE_API_KEY = os.getenv("MODEL_API_KEY", "c360_live_key_9f8a2b7c4e1d") | |
| api_key_header = APIKeyHeader(name="X-API-Key", auto_error=False) | |
| def verify_api_key(api_key: Optional[str] = Depends(api_key_header)): | |
| if not api_key or api_key != LIVE_API_KEY: | |
| raise HTTPException( | |
| status_code=status.HTTP_401_UNAUTHORIZED, | |
| detail="Unauthorized: Invalid or missing 'X-API-Key' header. Pass X-API-Key in request headers." | |
| ) | |
| return api_key | |
| # Initialize FastAPI App for Live Model | |
| app = FastAPI( | |
| title="Customer360 AI -- Live Model REST API", | |
| description="Live Machine Learning Model Inference Service secured with API Key authentication.", | |
| version="1.0.0", | |
| docs_url="/docs", | |
| redoc_url="/redoc" | |
| ) | |
| # Enable CORS for cross-origin requests | |
| app.add_middleware( | |
| CORSMiddleware, | |
| allow_origins=["*"], | |
| allow_credentials=True, | |
| allow_methods=["*"], | |
| allow_headers=["*"], | |
| ) | |
| # Input Pydantic Schema | |
| class CustomerInputSchema(BaseModel): | |
| customer_id: str = Field("C1024", description="Unique customer identifier") | |
| name: Optional[str] = Field("Ananya Sharma", description="Customer full name") | |
| recency_days: int = Field(74, description="Days since last purchase") | |
| frequency_purchases: int = Field(12, description="Total purchases in 12m") | |
| monetary_total_spend: float = Field(45000.0, description="Lifetime spend in INR") | |
| avg_order_value: Optional[float] = Field(3750.0, description="Average order value") | |
| website_visits_30d: int = Field(3, description="Website visits in 30d") | |
| product_views_30d: int = Field(5, description="Product views in 30d") | |
| cart_additions_30d: int = Field(3, description="Cart additions in 30d") | |
| cart_abandonments_30d: int = Field(2, description="Abandoned carts in 30d") | |
| email_opens_30d: int = Field(1, description="Email opens in 30d") | |
| email_clicks_30d: int = Field(0, description="Email clicks in 30d") | |
| support_tickets_30d: int = Field(3, description="Support tickets in 30d") | |
| engagement_change_pct: float = Field(-65.0, description="Engagement change %") | |
| return_rate: Optional[float] = Field(0.08, description="Return rate %") | |
| discount_dependency_ratio: Optional[float] = Field(0.35, description="Discount dependency") | |
| spend_30d: Optional[float] = Field(0.0, description="30d spend") | |
| spend_90d: Optional[float] = Field(15000.0, description="90d spend") | |
| purchase_velocity: Optional[float] = Field(30.0, description="Days between purchases") | |
| preferred_channel: Optional[str] = Field("WhatsApp", description="WhatsApp, Email, Push, SMS") | |
| preferred_category: Optional[str] = Field("Electronics", description="Product category") | |
| def read_root(): | |
| return """ | |
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <title>Customer360 AI -- Live Model API</title> | |
| <style> | |
| body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; background: #0f172a; color: #f8fafc; padding: 40px; line-height: 1.6; } | |
| .card { background: #1e293b; padding: 25px; border-radius: 12px; border: 1px solid #334155; max-width: 800px; margin: 0 auto; shadow: 0 10px 25px rgba(0,0,0,0.5); } | |
| h1 { color: #818cf8; margin-top: 0; } | |
| .key-badge { background: #312e81; color: #a5b4fc; padding: 4px 10px; border-radius: 6px; font-family: monospace; font-weight: bold; border: 1px solid #4338ca; } | |
| .btn { display: inline-block; background: #4f46e5; color: white; padding: 12px 20px; border-radius: 8px; text-decoration: none; font-weight: bold; margin-top: 15px; } | |
| .btn:hover { background: #4338ca; } | |
| pre { background: #020617; padding: 15px; border-radius: 8px; overflow-x: auto; color: #38bdf8; } | |
| </style> | |
| </head> | |
| <body> | |
| <div class="card"> | |
| <h1>🧠 Customer360 AI -- Live Model REST API Server</h1> | |
| <p>The machine learning model engine is <strong>ONLINE and active</strong>.</p> | |
| <p><strong>Live API Key:</strong> <span class="key-badge">c360_live_key_9f8a2b7c4e1d</span></p> | |
| <p><strong>Header:</strong> <span class="key-badge">X-API-Key: c360_live_key_9f8a2b7c4e1d</span></p> | |
| <h3>Browser Test Links:</h3> | |
| <ul> | |
| <li><a href="/demo-predict" style="color:#a5b4fc;" target="_blank">🔍 Click Here to Run Live Browser Prediction Demo (/demo-predict)</a></li> | |
| <li><a href="/docs" style="color:#a5b4fc;" target="_blank">📘 Click Here to Open Interactive Swagger API Docs (/docs)</a></li> | |
| <li><a href="/health" style="color:#a5b4fc;" target="_blank">💚 Click Here to Check Health Status (/health)</a></li> | |
| </ul> | |
| <a href="/demo-predict" class="btn">🚀 Test Model Prediction in Browser Now</a> | |
| </div> | |
| </body> | |
| </html> | |
| """ | |
| def check_health(): | |
| return { | |
| "status": "healthy", | |
| "model_loaded": inference_service.loaded, | |
| "api_key_configured": True | |
| } | |
| def demo_predict_browser( | |
| recency_days: int = 74, | |
| abandoned_carts: int = 2, | |
| support_tickets: int = 3, | |
| engagement_trend: float = -65.0, | |
| api_key: str = Query("c360_live_key_9f8a2b7c4e1d") | |
| ): | |
| """ | |
| Browser-friendly GET endpoint to test live ML inference directly in Chrome/Edge address bar! | |
| """ | |
| if api_key != LIVE_API_KEY: | |
| return JSONResponse(status_code=401, content={"error": "Invalid API key"}) | |
| monetary = 45000.0 | |
| freq = 12 | |
| cust_dict = { | |
| "customer_id": "DEMO_BROWSER_C1024", | |
| "name": "Ananya Sharma", | |
| "recency_days": recency_days, | |
| "frequency_purchases": freq, | |
| "monetary_total_spend": monetary, | |
| "avg_order_value": monetary / max(1, freq), | |
| "website_visits_30d": 3, | |
| "product_views_30d": 5, | |
| "cart_additions_30d": 3, | |
| "cart_abandonments_30d": abandoned_carts, | |
| "email_opens_30d": 1, | |
| "email_clicks_30d": 0, | |
| "support_tickets_30d": support_tickets, | |
| "engagement_change_pct": engagement_trend, | |
| "return_rate": 0.08, | |
| "discount_dependency_ratio": 0.35, | |
| "spend_30d": 0.0, | |
| "spend_90d": 15000.0, | |
| "purchase_velocity": 30.0, | |
| "preferred_channel": "WhatsApp", | |
| "preferred_category": "Electronics" | |
| } | |
| result = inference_service.predict_customer_decision(cust_dict) | |
| return result | |
| def predict_single_customer(payload: CustomerInputSchema): | |
| """ | |
| Live ML Inference Endpoint for POST requests (Requires X-API-Key in HTTP headers). | |
| """ | |
| customer_dict = payload.model_dump() | |
| result = inference_service.predict_customer_decision(customer_dict) | |
| return result | |
| def predict_batch_customers(payload: List[CustomerInputSchema]): | |
| """ | |
| Batch ML Inference Endpoint for multiple customers. | |
| """ | |
| results = [] | |
| for item in payload: | |
| cdict = item.model_dump() | |
| res = inference_service.predict_customer_decision(cdict) | |
| results.append(res) | |
| return {"total_processed": len(results), "predictions": results} | |
| if __name__ == "__main__": | |
| import uvicorn | |
| uvicorn.run("server:app", host="0.0.0.0", port=8000, reload=True) | |