import pickle
import pandas as pd
import numpy as np
import xgboost as xgb
import gradio as gr
import pathlib
#plt = platform.system()
#if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath

model_path = "model.None"
model = xgb.Booster()
model.load_model(model_path)

dv_path = "dv.bin"
with open(dv_path, 'rb') as f_out:
    dv = pickle.load(f_out)
    
scaler_path = "scaler.bin"
with open(scaler_path, 'rb') as f_out:
    scaler = pickle.load(f_out)
    
def preprocess(data):
    """Preprocessing of the data"""
    # turn json input to dataframe
    data = pd.DataFrame([data])

    # define numerical and categorical features
    numerical = ["X1", "X2", "X3", "X4", "X5", "X7"]
    categorical = ["X6", "X8"]

    # preprocess numerical features
    X_num = scaler.transform(data[numerical])
    # preprocess categorical features
    data[categorical] = data[categorical].astype("string")
    X_dicts = data[categorical].to_dict(orient="records")
    X_cat = dv.transform(X_dicts)
    # concatenate both
    X = np.concatenate((X_num, X_cat), axis=1)

    return X


def predict(X):
    """make predictions"""
    pred = model.predict(X)
    print('prediction', pred[0])
    return float(pred[0])

def main(X1,X2,X3,X4,X5,X6,X7,X8):
    """request input, preprocess it and make prediction"""
    input_data = {
    "X1": X1,
    "X2": X2,
    "X3": X3,
    "X4": X4,
    "X5": X5,
    "X6": X6,
    "X7": X7,
    "X8": X8
    }
    features = preprocess(input_data)
    features_2 = xgb.DMatrix(features)
    pred = predict(features_2)

    result = {'heat load': pred}

    return pred

def classify_image(img):
    pred,idx,probs = learn.predict(img)
    return dict(zip(categories,map(float,probs)))

#create input and output objects
#input
input1 = gr.inputs.Number()
input2 = gr.inputs.Number()
input3 = gr.inputs.Number()
input4 = gr.inputs.Number()
input5 = gr.inputs.Number()
input6 = gr.inputs.Number()
input7 = gr.inputs.Number()
input8 = gr.inputs.Number()

#output object
output = gr.outputs.Textbox() 

intf = gr.Interface(title = "Energy Efficiency",
                    description = "The objective of this project is to predict the Heating Load based on various building features.",
                    fn=main, 
                    inputs=[input1,input2,input3,input4,input5,input6,input7,input8], 
                    outputs=[output], 
                    live=True,
                    enable_queue=True
                    )
intf.launch()