main.py

# main.py - FastAPI application for Flood Vulnerability Assessment
from fastapi import FastAPI, File, UploadFile, HTTPException, Request
from fastapi.responses import StreamingResponse, HTMLResponse
from fastapi.templating import Jinja2Templates
from pydantic import BaseModel, field_validator
from typing import Optional, Dict
import pandas as pd
import io
import asyncio
from concurrent.futures import ThreadPoolExecutor

from spatial_queries import get_terrain_metrics, distance_to_water
from vulnerability import calculate_vulnerability_index
from gee_auth import initialize_gee
from height_predictor.inference import get_predictor
from height_predictor.get_height_gba import GlobalBuildingAtlasHeight

# SHAP Explainer Initialization
try:
    from explainability import VulnerabilityExplainer
    explainer = VulnerabilityExplainer()  # Automatically loads rf_explainer.pkl if present
    print("✅ SHAP model initialized successfully.")
except Exception as e:
    print(f"⚠️ SHAP explainer not available: {e}")
    explainer = None

# Initialize GEE once at startup
try:
    initialize_gee()
    print("✅ GEE initialized once at startup.")
except Exception as e:
    print(f"⚠️ GEE initialization failed at startup: {e}")

# APP INITIALIZATION
app = FastAPI(title="Flood Vulnerability Assessment API", version="1.0")

# Frontend templates setup
templates = Jinja2Templates(directory="templates")

# Thread pool for batch processing
executor = ThreadPoolExecutor(max_workers=10)

gba_getter = GlobalBuildingAtlasHeight()


# DATA MODEL
class SingleAssessment(BaseModel):
    latitude: float
    longitude: float
    height: Optional[float] = 0.0
    basement: Optional[float] = 0.0

    @field_validator('latitude')
    @classmethod
    def check_lat(cls, v: float) -> float:
        if not -90 <= v <= 90:
            raise ValueError('Latitude must be between -90 and 90')
        return v

    @field_validator('longitude')
    @classmethod
    def check_lon(cls, v: float) -> float:
        if not -180 <= v <= 180:
            raise ValueError('Longitude must be between -180 and 180')
        return v

    @field_validator('basement')
    @classmethod
    def check_basement(cls, v: float) -> float:
        if v > 0:
            raise ValueError('Basement height must be 0 or negative (e.g., -1, -2, -3)')
        return v


# FRONTEND ROUTE
@app.get("/", response_class=HTMLResponse)
async def home(request: Request):
    """Serve the main web interface"""
    return templates.TemplateResponse("index.html", {"request": request})


# API ROUTES
@app.get("/api")
async def root() -> Dict:
    """API info endpoint"""
    return {
        "service": "Flood Vulnerability Assessment API",
        "version": "1.0",
        "endpoints": {
            "POST /assess": "Assess single location",
            "POST /assess_batch": "Assess batch from CSV file",
            "GET /health": "Health check"
        }
    }


@app.post("/assess")
async def assess_single(data: SingleAssessment) -> Dict:
    """Assess flood vulnerability for a single location (non-blocking)."""
    loop = asyncio.get_event_loop()

    try:
        # Run slow terrain + water queries in a background thread
        terrain, water_dist = await loop.run_in_executor(
            None,
            lambda: (
                get_terrain_metrics(data.latitude, data.longitude),
                distance_to_water(data.latitude, data.longitude)
            )
        )

        # Calculate vulnerability after terrain + water distance retrieved
        result = calculate_vulnerability_index(
            lat=data.latitude,
            lon=data.longitude,
            height=data.height,
            basement=data.basement,
            terrain_metrics=terrain,
            water_distance=water_dist
        )

        return {
            "status": "success",
            "input": data.dict(),
            "assessment": result
        }

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Assessment failed: {e}")


@app.post("/predict_height")
async def predict_height(data: SingleAssessment) -> Dict:
    try:
        predictor = get_predictor()
        result = predictor.predict_from_coordinates(data.latitude, data.longitude)
        
        if result['status'] == 'error':
             raise HTTPException(status_code=500, detail=result['error'])
             
        return result
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


def process_single_row(row, use_predicted_height=False, use_gba_height=False):
    """Process a single row from CSV - used for parallel processing."""
    try:
        lat = row['latitude']
        lon = row['longitude']
        height = row.get('height', 0.0)
        basement = row.get('basement', 0.0)
        if use_gba_height:
            try:
                result = gba_getter.get_height_m(lat, lon, buffer_m=5.0)
                if result.get('status') == 'success' and result.get('predicted_height') is not None:
                    h = result['predicted_height']
                    if h >= 0:  # Only use valid positive heights
                        height = h
            except Exception as e:
                print(f"GBA height failed for {lat},{lon}: {e}")
        elif use_predicted_height:
            try:
                predictor = get_predictor()
                pred = predictor.predict_from_coordinates(lat, lon)
                if pred['status'] == 'success' and pred['predicted_height'] is not None:
                     height = pred['predicted_height']
            except Exception as e:
                print(f"Height prediction failed for {lat},{lon}: {e}")

        terrain = get_terrain_metrics(lat, lon)
        water_dist = distance_to_water(lat, lon)

        result = calculate_vulnerability_index(
            lat=lat,
            lon=lon,
            height=height,
            basement=basement,
            terrain_metrics=terrain,
            water_distance=water_dist
        )

        # CSV output - essential columns
        return {
            'latitude': lat,
            'longitude': lon,
            'height': height,
            'basement': basement,
            'vulnerability_index': result['vulnerability_index'],
            'ci_lower_95': result['confidence_interval']['lower_bound_95'],
            'ci_upper_95': result['confidence_interval']['upper_bound_95'],
            'vulnerability_level': result['risk_level'],
            'confidence': result['uncertainty_analysis']['confidence'],
            'confidence_interpretation': result['uncertainty_analysis']['interpretation'],
            'elevation_m': result['elevation_m'],
            'tpi_m': result['relative_elevation_m'],
            'slope_degrees': result['slope_degrees'],
            'distance_to_water_m': result['distance_to_water_m'],
            'quality_flags': ','.join(result['uncertainty_analysis']['data_quality_flags']) if result['uncertainty_analysis']['data_quality_flags'] else ''
        }

    except Exception as e:
        return {
            'latitude': row.get('latitude'),
            'longitude': row.get('longitude'),
            'height': row.get('height', 0.0),
            'basement': row.get('basement', 0.0),
            'error': str(e),
            'vulnerability_index': None,
            'ci_lower_95': None,
            'ci_upper_95': None,
            'risk_level': None,
            'confidence': None,
            'confidence_interpretation': None,
            'elevation_m': None,
            'tpi_m': None,
            'slope_degrees': None,
            'distance_to_water_m': None,
            'quality_flags': ''
        }


@app.post("/assess_batch")
async def assess_batch(file: UploadFile = File(...), use_predicted_height:bool=False, use_gba_height:bool=False) -> StreamingResponse:
    """Assess flood vulnerability for multiple locations from a CSV file."""
    try:
        contents = await file.read()
        df = pd.read_csv(io.StringIO(contents.decode('utf-8')))

        if 'latitude' not in df.columns or 'longitude' not in df.columns:
            raise HTTPException(
                status_code=400,
                detail="CSV must contain 'latitude' and 'longitude' columns"
            )

        import numpy as np
        df = df[(np.abs(df['latitude']) <= 90) & (np.abs(df['longitude']) <= 180)]
        if len(df) == 0:
            raise HTTPException(status_code=400, detail="No valid coordinates in CSV (lat -90..90, lon -180..180)")

        # Set defaults for optional columns
        if 'height' not in df.columns:
            df['height'] = 0.0
        if 'basement' not in df.columns:
            df['basement'] = 0.0

        loop = asyncio.get_event_loop()
        results = await loop.run_in_executor(
            executor,
            lambda: [process_single_row(row, use_predicted_height, use_gba_height) for _, row in df.iterrows()]
        )

        results_df = pd.DataFrame(results)
        output = io.StringIO()
        results_df.to_csv(output, index=False)
        output.seek(0)
        return StreamingResponse(
            io.BytesIO(output.getvalue().encode('utf-8')),
            media_type="text/csv",
            headers={
                "Content-Disposition": (
                    "attachment; filename=vulnerability_results.csv; "
                    "filename*=UTF-8''vulnerability_results.csv"
                )
            }
        )
    
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Batch processing failed: {str(e)}")
@app.post("/assess_batch_multihazard")
async def assess_batch_multihazard(file: UploadFile = File(...), use_predicted_height: bool = False, use_gba_height: bool = False) -> StreamingResponse:
    try:
        contents = await file.read()
        df = pd.read_csv(io.StringIO(contents.decode('utf-8')))

        if 'latitude' not in df.columns or 'longitude' not in df.columns:
            raise HTTPException(
                status_code=400,
                detail="CSV must contain 'latitude' and 'longitude' columns"
            )

        loop = asyncio.get_event_loop()
        from vulnerability import calculate_multi_hazard_vulnerability
        results = await loop.run_in_executor(
            executor,
            lambda: [process_single_row_multihazard(row, use_predicted_height, use_gba_height) for _, row in df.iterrows()]
        )

        results_df = pd.DataFrame(results)
        output = io.StringIO()
        results_df.to_csv(output, index=False)
        output.seek(0)
        return StreamingResponse(
            io.BytesIO(output.getvalue().encode('utf-8')),
            media_type="text/csv",
            headers={
                "Content-Disposition": (
                    "attachment; filename=multihazard_results.csv; "
                    "filename*=UTF-8''multihazard_results.csv"
                )
            }
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Batch multihazard failed: {str(e)}")
@app.post("/explain")
async def explain_assessment(data: SingleAssessment) -> Dict:
    """Assess vulnerability with SHAP explanation"""
    loop = asyncio.get_event_loop()

    try:
        # Run slow terrain + water queries in a background thread
        terrain, water_dist = await loop.run_in_executor(
            None,
            lambda: (
                get_terrain_metrics(data.latitude, data.longitude),
                distance_to_water(data.latitude, data.longitude)
            )
        )

        result = calculate_vulnerability_index(
            lat=data.latitude,
            lon=data.longitude,
            height=data.height,
            basement=data.basement,
            terrain_metrics=terrain,
            water_distance=water_dist
        )

        # Generate explanation if explainer available
        explanation = None
        if explainer:
            try:
                explanation = explainer.explain(result['components'])
            except Exception as e:
                print(f"SHAP explanation failed: {e}")

        return {
            "status": "success",
            "input": data.dict(),
            "assessment": result,
            "explanation": explanation
        }

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Assessment failed: {e}")


def process_single_row_multihazard(row, use_predicted_height=False, use_gba_height=False):
    """Process a single row with multi-hazard assessment."""
    try:
        from vulnerability import calculate_multi_hazard_vulnerability

        lat = row['latitude']
        lon = row['longitude']
        height = row.get('height', 0.0)
        basement = row.get('basement', 0.0)

        if use_gba_height:
            try:
                result = gba_getter.get_height_m(lat, lon, buffer_m=5.0)
                if result.get('status') == 'success' and result.get('predicted_height') is not None:
                    h = result['predicted_height']
                    if h >= 0:  # Only use valid positive heights
                        height = h
            except Exception as e:
                print(f"GBA height failed for {lat},{lon}: {e}")
        elif use_predicted_height:
             try:
                predictor = get_predictor()
                pred = predictor.predict_from_coordinates(lat, lon)
                if pred['status'] == 'success' and pred['predicted_height'] is not None:
                    height = pred['predicted_height']
             except Exception as e:
                print(f"Height prediction failed for {lat},{lon}: {e}")

        terrain = get_terrain_metrics(lat, lon)
        water_dist = distance_to_water(lat, lon)

        result = calculate_multi_hazard_vulnerability(
            lat=lat,
            lon=lon,
            height=height,
            basement=basement,
            terrain_metrics=terrain,
            water_distance=water_dist
        )

        return {
            'latitude': lat,
            'longitude': lon,
            'height': height,
            'basement': basement,
            'vulnerability_index': result['vulnerability_index'],
            'ci_lower_95': result['confidence_interval']['lower_bound_95'],
            'ci_upper_95': result['confidence_interval']['upper_bound_95'],
            'vulnerability_level': result['risk_level'],
            'confidence': result['uncertainty_analysis']['confidence'],
            'confidence_interpretation': result['uncertainty_analysis']['interpretation'],
            'elevation_m': result['elevation_m'],
            'tpi_m': result['relative_elevation_m'],
            'slope_degrees': result['slope_degrees'],
            'distance_to_water_m': result['distance_to_water_m'],
            'dominant_hazard': result['dominant_hazard'],
            'fluvial_risk': result['hazard_breakdown']['fluvial_riverine'],
            'coastal_risk': result['hazard_breakdown']['coastal_surge'],
            'pluvial_risk': result['hazard_breakdown']['pluvial_drainage'],
            'combined_risk': result['hazard_breakdown']['combined_index'],
            'quality_flags': ','.join(result['uncertainty_analysis']['data_quality_flags'])
                     if result['uncertainty_analysis']['data_quality_flags'] else ''
        }

    except Exception as e:
        return {
            'latitude': row.get('latitude'),
            'longitude': row.get('longitude'),
            'height': row.get('height', 0.0),
            'basement': row.get('basement', 0.0),
            'error': str(e),
            'vulnerability_index': None
        }


@app.post("/assess_multihazard")
async def assess_multihazard(data: SingleAssessment) -> Dict:
    """Multi-hazard assessment (fluvial + coastal + pluvial)"""
    loop = asyncio.get_event_loop()

    try:
        from vulnerability import calculate_multi_hazard_vulnerability

        # Run slow terrain + water queries in a background thread
        terrain, water_dist = await loop.run_in_executor(
            None,
            lambda: (
                get_terrain_metrics(data.latitude, data.longitude),
                distance_to_water(data.latitude, data.longitude)
            )
        )

        result = calculate_multi_hazard_vulnerability(
            lat=data.latitude,
            lon=data.longitude,
            height=data.height,
            basement=data.basement,
            terrain_metrics=terrain,
            water_distance=water_dist
        )

        return {
            "status": "success",
            "input": data.dict(),
            "assessment": result
        }

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Assessment failed: {e}")

@app.post("/get_height_gba")
async def get_height_gba(data: SingleAssessment):
    try:
        result = gba_getter.get_height_m(data.latitude, data.longitude, buffer_m=5.0)

        if result.get("status") != "success":
            raise HTTPException(status_code=404, detail="GBA height not found for this location. Please try predicting the height.")

        return result
    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


@app.get("/health")
async def health_check() -> Dict:
    """Health check endpoint."""
    return {"status": "healthy", "gee_initialized": True}