import pandas as pd
import numpy as np
import requests
from io import BytesIO
import joblib
from pathlib import Path


try:
    MODEL_URL = "https://huggingface.co/samithcs/risk_predictor/resolve/main/risk_predictor/hist_gradient_boosting_risk_predictor.joblib"
    RESPONSE = requests.get(MODEL_URL)
    risk_model = joblib.load(BytesIO(RESPONSE.content))
    FEATURE_COLS = list(risk_model.feature_names_in_) if hasattr(risk_model, "feature_names_in_") else None
except Exception:
    risk_model = None
    FEATURE_COLS = None


REGION_BASE_RISKS = {
    "Shanghai": 0.55, "Singapore": 0.30, "Mumbai": 0.45, "Dubai": 0.35,
    "UAE": 0.35, "USA": 0.30, "Germany": 0.25, "China": 0.55,
    "India": 0.45, "Hong Kong": 0.50, "Rotterdam": 0.28, "Los Angeles": 0.40
}


EVENT_RISK_MULTIPLIERS = {
    "strike": 0.30, "port strike": 0.35, "typhoon": 0.35, "hurricane": 0.35,
    "earthquake": 0.40, "flood": 0.25, "port closure": 0.45, "supplier outage": 0.25,
    "customs delay": 0.15, "congestion": 0.20, "pandemic": 0.30, "war": 0.50,
    "sanctions": 0.40
}


def build_feature_row(feature_cols, query_dict, reference_row):

    row = reference_row.copy()
    shipping_mode = query_dict.get('shipping_mode', 'Standard Class')
    for col in feature_cols:
        if 'Shipping_Mode' in col and shipping_mode in col:
            row[col] = 1
    region = query_dict.get('region', '')
    for col in feature_cols:
        if ('Order_Country' in col or 'Order_Region' in col) and region in col:
            row[col] = 1
    for col in feature_cols:
        if 'Order_Status_COMPLETE' in col:
            row[col] = 1
    return row

def calculate_rule_based_risk(region, days, incidents):
 
    base_risk = REGION_BASE_RISKS.get(region, 0.40)
    event_risk = 0.0
    if incidents:
        for incident in incidents:
            incident_lower = str(incident).lower()
            for keyword, multiplier in EVENT_RISK_MULTIPLIERS.items():
                if keyword in incident_lower:
                    event_risk += multiplier
    time_factor = max(0.1, 1.0 - (days / 30.0))
    return min(1.0, base_risk * 0.5 + event_risk * 0.4 + time_factor * 0.1)


def predict_risk(region: str, days: int = 5, origin=None, destination=None,
                 event_type=None, incidents=None, shipping_mode=None):
  

    try:
  
        if shipping_mode is None:
            shipping_mode = "Standard Class"

       
        rule_risk = calculate_rule_based_risk(region, days, incidents or [])
        

     
        ml_risk = 0.40  

        if risk_model is not None and FEATURE_COLS is not None:
            try:
                reference_row = pd.Series({col: 0 for col in FEATURE_COLS})
                query_dict = {
                    "region": region,
                    "days": days,
                    "origin": origin,
                    "destination": destination,
                    "shipping_mode": shipping_mode,
                }

                test_features = pd.DataFrame(
                    [build_feature_row(FEATURE_COLS, query_dict, reference_row)]
                )

                ml_risk = float(risk_model.predict_proba(test_features)[0, 1])
                
            except Exception as e:
              
                ml_risk = 0.40

 
        if incidents and len(incidents) > 0:
            final_risk = (ml_risk * 0.40) + (rule_risk * 0.60)
            
        else:
            final_risk = (ml_risk * 0.70) + (rule_risk * 0.30)
            

  
        final_risk = float(np.clip(final_risk, 0.0, 1.0))
        return round(final_risk, 2)

    except Exception as e:
        
        return 0.50