"""
FastAPI backend for flight price prediction — Hugging Face Spaces version.

Downloads model artifacts from HF Hub at startup and serves predictions.

Endpoints:
  GET  /           -> Health check
  GET  /metadata   -> Dropdown options for the frontend
  POST /predict    -> Predict flight price
"""

import json
import os

import joblib
import numpy as np
import pandas as pd
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from huggingface_hub import hf_hub_download
from pydantic import BaseModel, Field

# ---------- Configuration ----------
MODEL_REPO = os.getenv("HF_MODEL_REPO", "Sajjad-Ali-Shah/skypredict-flight-price")
CACHE_DIR = "/tmp/hf_cache"

# ---------- Download artifacts from HF Hub ----------
print(f"Downloading model from {MODEL_REPO}...")
model_path = hf_hub_download(repo_id=MODEL_REPO, filename="model.joblib", cache_dir=CACHE_DIR)
scaler_path = hf_hub_download(repo_id=MODEL_REPO, filename="scaler.joblib", cache_dir=CACHE_DIR)
metadata_path = hf_hub_download(repo_id=MODEL_REPO, filename="metadata.json", cache_dir=CACHE_DIR)
print("Model artifacts downloaded!")

# ---------- Load artifacts ----------
model = joblib.load(model_path)
scaler = joblib.load(scaler_path)
with open(metadata_path) as f:
    metadata = json.load(f)

FEATURE_NAMES = metadata["feature_names"]
SCALE_COLS = metadata["scale_cols"]
STOPS_MAP = metadata["stops_map"]

# ---------- App ----------
app = FastAPI(
    title="SkyPredict — Flight Price API",
    description="Predict airline flight ticket prices using a Random Forest model (R² = 0.98)",
    version="1.0.0",
)

# Allow requests from Vercel dashboard + local dev
ALLOWED_ORIGINS = [
    "http://localhost:3000",
    "http://127.0.0.1:3000",
]

# Add any Vercel production URL from env
vercel_url = os.getenv("FRONTEND_URL", "")
if vercel_url:
    ALLOWED_ORIGINS.append(vercel_url)

# Also allow all *.vercel.app preview deployments
ALLOWED_ORIGINS.append("https://*.vercel.app")

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Permissive for HF Spaces; tighten if needed
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


# ---------- Schemas ----------
class PredictionRequest(BaseModel):
    airline: str = Field(..., example="Vistara")
    source_city: str = Field(..., example="Delhi")
    destination_city: str = Field(..., example="Mumbai")
    departure_time: str = Field(..., example="Morning")
    arrival_time: str = Field(..., example="Night")
    stops: str = Field(..., example="one")
    class_type: str = Field(..., example="Business")
    duration: float = Field(..., ge=0.5, le=50, example=5.5)
    days_left: int = Field(..., ge=1, le=49, example=15)


class PredictionResponse(BaseModel):
    predicted_price: int
    currency: str = "INR"
    model: str
    r2_score: float


# ---------- Routes ----------
@app.get("/")
def health_check():
    return {
        "status": "healthy",
        "model": metadata["model_name"],
        "r2_score": metadata["r2_score"],
    }


@app.get("/metadata")
def get_metadata():
    """Return dropdown options and value ranges for the frontend."""
    return {
        "categories": metadata["categories"],
        "duration_range": metadata["duration_range"],
        "days_left_range": metadata["days_left_range"],
    }


@app.post("/predict", response_model=PredictionResponse)
def predict(req: PredictionRequest):
    """Predict the price of a flight ticket."""
    try:
        # Build a single-row DataFrame with the same columns as training
        stops_numeric = STOPS_MAP.get(req.stops, 1)
        is_direct = 1 if req.stops == "zero" else 0
        class_val = 1 if req.class_type == "Business" else 0

        # Start with numeric features
        row = {
            "duration": req.duration,
            "days_left": req.days_left,
            "is_direct": is_direct,
            "stops_numeric": stops_numeric,
            "class": class_val,
        }

        # One-hot encode categoricals (drop_first=True matches training)
        onehot_config = {
            "airline": metadata["categories"]["airline"],
            "source_city": metadata["categories"]["source_city"],
            "destination_city": metadata["categories"]["destination_city"],
            "departure_time": metadata["categories"]["departure_time"],
            "arrival_time": metadata["categories"]["arrival_time"],
        }

        for feature, all_values in onehot_config.items():
            sorted_vals = sorted(all_values)
            for val in sorted_vals[1:]:  # skip first (drop_first=True)
                col_name = f"{feature}_{val}"
                actual_val = getattr(req, feature)
                row[col_name] = 1 if actual_val == val else 0

        # Create DataFrame and align columns with training order
        df_input = pd.DataFrame([row])

        # Ensure all expected columns are present
        for col in FEATURE_NAMES:
            if col not in df_input.columns:
                df_input[col] = 0

        df_input = df_input[FEATURE_NAMES]

        # Scale numeric columns
        df_input[SCALE_COLS] = scaler.transform(df_input[SCALE_COLS])

        # Predict
        prediction = model.predict(df_input)[0]
        predicted_price = max(int(round(prediction)), 0)

        return PredictionResponse(
            predicted_price=predicted_price,
            currency="INR",
            model=metadata["model_name"],
            r2_score=metadata["r2_score"],
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))