import json
import xgboost as xgb
import pickle
import pandas as pd
import numpy as np
def predict(json_input:str):

    with open('app/metadata.pkl','rb') as f:
        to_remove,use_small,evacuations,encoders = pickle.load(f)
    
    
    data = pd.DataFrame(json.loads(json_input[1:-1]),index=[0])
    
    data.drop(columns=['dateandtime','skiarea_id','day_of_year','minute_of_day','year'], inplace=True, errors='ignore')
    
    ##evacuation_vehicles must be explicitated and workaround for other!
    evacuation = data.evacuation_vehicles.values[0]
    if isinstance(evacuation,str):
        evacuation = [evacuation]
    for c in evacuations:
        data[c] = False
    for c in evacuation:
        data[c] = True
    
    for c in evacuation:
        if c not in evacuations:
            data['other'] = True
            break
    
    data.drop(columns=['town','province','evacuation_vehicles'],inplace=True, errors='ignore')
    
    
    data['age'] =  data['age'].astype(np.float32)

    

    for c in data.columns:
        if c not in ['india','age','season','skiarea_name','destination']:
            data[c] = data[c].astype('str')  
    if use_small:
        for c in to_remove.keys():
            for k in to_remove[c]:
                data.loc[data[c]==k,c] = 'other'
    for c in data.columns:
        if c not in ['age','season','skiarea_name','india']:
            data[c] =  data[c].fillna('None')
            if use_small:
                data[c] = pd.Categorical( encoders['small'][c].transform(data[c]), categories=list(range(len(encoders['small'][c].classes_))), ordered=False)
            else:
                data[c] = pd.Categorical( encoders['normal'][c].transform(data[c]), categories=list(range(len(encoders['normal'][c].classes_))), ordered=False)
    
    bst_FS = xgb.Booster()
    bst_FS.load_model("app/model.json")

    dtest_FS = xgb.DMatrix(data[bst_FS.feature_names],enable_categorical=True)
    preds = bst_FS.predict(dtest_FS)
    
    return preds[0]*100