import gradio as gr
import numpy as np
from PIL import Image
import requests
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import joblib
import hopsworks
from tqdm import tqdm

# Login to hopsworks and get the feature store



columnHeaders = ['area','streetName','number','sqm','rooms','soldDate','monthlyFee','monthlyCost','floor','yearBuilt','agency','lat','lon','gdp','unemployment','interestRate']


def downloadModel():
    # Download saved Autogluon model from Hopsworks 
    project = hopsworks.login()
    mr = project.get_model_registry()
    temp = mr.get_model("xgboost_model", version=3)
    model_path = temp.download()

    xgb_model = joblib.load(model_path + "/xgboost_model.pkl")
    print(xgb_model)
    return xgb_model

def getAddressInfo(streetName, number):
    address = getAddress(streetName, number)
    ...
    lat = None
    lon = None
    return lat, lon

def getAddress(streetName, number):
    ...
    return None

def getFinancialInfo():
    ...
    gdp, unemployment, interestRate = None, None, None
    return gdp, unemployment, interestRate

def dateToFloat(date):
    year, month, day = str(date).split('-')
    day = day.split(' ')[0]
    return int(year) + int(month) / 12 + int(day) / 365

def normalize(x, minVal, maxVal, feature):
    # Not fantastic, but it suffices
    if feature in ['lat', 'lon'] and x == 0:
        return 0

    res = (x - minVal) / (maxVal - minVal)
    return min(max(res, 0), 1)

def normalizeData(df):
    # We do this manually because we want the UI to be able to transform the input data the same way
    featureToMinMax = {
        'sqm': (10, 800),
        'rooms': (1, 20),
        'monthlyFee': (0, 60000),
        'monthlyCost': (0, 20000),
        'floor': (-3, 35),
        'yearBuilt': (1850, 2023),
        'lat': (58.8, 60.2),
        'lon': (17.5, 19.1),
        'gdp': (505.1, 630.14),
        'unemployment': (6.36, 8.66),
        'interestRate': (-0.5, 2.64),
        'price': (1.5e5, 7e7),
        'number': (0, 300),
        'soldDate': (2010, 2025)
    } # Extracted from the data

    # Normalize select numerical values to a value between 0 and 1
    print('Normalizing data...')
    for feature, minMax in tqdm(featureToMinMax.items()):
        min = minMax[0]
        max = minMax[1]
        if feature == 'soldDate':
            df[feature] = df[feature].apply(lambda x: dateToFloat(x))

        df[feature] = df[feature].apply(lambda x: normalize(x, min, max, feature))

    return df


model = downloadModel()

def sthlm(area, streetName, number, sqm, rooms, monthlyFee, monthlyCost, floor, yearBuilt):
    soldDate = '2021-01-01' # TODO
    price = None
    agency = None
    brf = None
    lat, lon = getAddressInfo(streetName, number)
    gdp, unemployment, interestRate = getFinancialInfo(soldDate)

    # Parse the input so we can run it through the model
    # Create a dataframe from the input values
    input_variables = pd.DataFrame(
        [[area,streetName,number,sqm,rooms,soldDate,monthlyFee,monthlyCost,floor,yearBuilt,agency,lat,lon,gdp,unemployment,interestRate]], columns=columnHeaders)
    
    df = normalizeData(input_variables)

    # Save first row as a numpy array
    input_list = df.iloc[0].to_numpy()

    # 'res' is a list of predictions returned as the label.
    res = model.predict(np.asarray(input_list).reshape(1, -1))

    print(res)

    return 100


# All features present in the sthlm dataset
numericalInputs = ['number', 'sqm','rooms', 'monthlyFee','monthlyCost','floor','yearBuilt']
categoricalInputs = ['area']
inputs = [gr.inputs.Textbox(lines=1, label='streetName')]
catToInput = {
    'feature': ['a', 'b', 'c']
}
    
# Generate the input form
for feature in numericalInputs:
    inputs.append(gr.inputs.Number(default=0, label=feature))

for feature in categoricalInputs:
    inputs.append(gr.inputs.Dropdown(
        choices=catToInput.get('feature'), default="a", label=feature))

# Create the interface
demo = gr.Interface(
    fn=sthlm,
    title="Stockholm Housing Valuation",
    description="Predict the price of an apartment in Stockholm",
    allow_flagging="never",
    inputs=inputs,
    outputs=['number'])

demo.launch()