| from fastapi import FastAPI, HTTPException |
| from pydantic import BaseModel, Field |
| from handler import FastApiHandler |
|
|
| app = FastAPI(title="TrueNest Rent Prediction API") |
| handler = None |
|
|
|
|
| |
|
|
| class PredictRequest(BaseModel): |
| model_params: dict = Field( |
| ..., |
| json_schema_extra={ |
| "example": { |
| "bathrooms": 1, |
| "bedrooms": 2, |
| "propertyType": "Flat", |
| "deposit": False, |
| "letType": "Long term", |
| "furnishType": "Furnished", |
| "latitude": 51.49199, |
| "longitude": -0.17134 |
| } |
| }, |
| ) |
|
|
|
|
|
|
| |
| @app.on_event("startup") |
| def load_model_once(): |
| global handler |
| handler = FastApiHandler() |
| print("✅ MLflow model loaded at startup") |
|
|
|
|
| |
| @app.get("/") |
| def root(): |
| return {"message": "🏡 Rent Prediction API is running", "run_id": handler.run_id} |
|
|
|
|
| @app.post("/predict") |
| def predict(req: PredictRequest): |
| result = handler.handle(req.dict()) |
| if "error" in result: |
| raise HTTPException(status_code=400, detail=result["error"]) |
| return result |
|
|
|
|
| @app.post("/explain") |
| def explain(req: PredictRequest): |
| try: |
| explanation = handler.explain_prediction(req.model_params) |
| return explanation |
| except Exception as e: |
| raise HTTPException(status_code=400, detail=str(e)) |
|
|
|
|
