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import sys
import os
import pandas as pd
from src.exception import CustomException
from src.utils import load_object
class PredictPipeline:
def __init__(self):
pass
def predict(self,features):
try:
model_path=os.path.join("artifacts","model.pkl")
preprocessor_path=os.path.join('artifacts','proprocessor.pkl')
print("Before Loading")
model=load_object(file_path=model_path)
preprocessor=load_object(file_path=preprocessor_path)
print("After Loading")
data_scaled=preprocessor.transform(features)
preds=model.predict(data_scaled)
return preds
except Exception as e:
raise CustomException(e,sys)
class CustomData:
def __init__( self,
gender: str,
race_ethnicity: str,
parental_level_of_education,
lunch: str,
test_preparation_course: str,
reading_score: int,
writing_score: int):
self.gender = gender
self.race_ethnicity = race_ethnicity
self.parental_level_of_education = parental_level_of_education
self.lunch = lunch
self.test_preparation_course = test_preparation_course
self.reading_score = reading_score
self.writing_score = writing_score
def get_data_as_data_frame(self):
try:
custom_data_input_dict = {
"gender": [self.gender],
"race_ethnicity": [self.race_ethnicity],
"parental_level_of_education": [self.parental_level_of_education],
"lunch": [self.lunch],
"test_preparation_course": [self.test_preparation_course],
"reading_score": [self.reading_score],
"writing_score": [self.writing_score],
}
return pd.DataFrame(custom_data_input_dict)
except Exception as e:
raise CustomException(e, sys) |