import os
import pandas as pd
import math
import numpy as np
import pickle
import dill
def load_object(file_path):
      with open(file_path,"rb") as file_obj:
           return dill.load(file_obj)

class PredictPipeline:
    def __init__(self):
        pass

    def predict(self,features):
            model_path=os.path.join("model.pkl")
            preprocessor_path=os.path.join('preprocessor.pkl')
            model=load_object(model_path)
            preprocessor=load_object(preprocessor_path)
            data_scaled=preprocessor.transform(features)
            result=model.predict(data_scaled)
            output=math.floor(np.exp(result[0]))
            return output

class CustomData:
    def __init__(self,company:str,typename:str,ram:int,weight:float,touchscrren:int,ips:int,ppi:float,cpu:str,gpu:str,os:str,HDD:int,SDD:int):
        self.company=company
        self.typename=typename
        self.ram=ram
        self.weight=weight
        self.touchscrren=touchscrren
        self.ips=ips
        self.ppi=ppi
        self.cpu=cpu
        self.gpu=gpu
        self.os=os
        self.HDD=HDD
        self.SDD=SDD

    def get_data_as_dataframe(self):
            custom_data_input_dict={
            "Company": [self.company],
            "TypeName":[self.typename],
            "Ram":[self.ram],
            "Weight":[self.weight],
            "Touchscreen":[self.touchscrren],
            "IPS":[self.ips],
            "ppi":[self.ppi],
            "CPU_brand":[self.cpu],
            "Gpu_brand":[self.gpu],
            "os":[self.os],
            "HDD":[self.HDD],
            "SDD":[self.SDD],
            }
            return pd.DataFrame(custom_data_input_dict)