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import sys
import pandas as pd
import logging
from src.exception import CustomException
from src.logger import logging
from src.utils import load_object
class PredictPipeline:
def __init__(self):
pass
def predict(self, features):
try:
model_path = 'artifacts/model.pkl'
preprocessor_path = 'artifacts/preprocessor.pkl'
# Load model and preprocessor
model = load_object(file_path=model_path)
preprocessor = load_object(file_path=preprocessor_path)
# Check if model and preprocessor are loaded
if model is None:
raise Exception("Model failed to load")
if preprocessor is None:
raise Exception("Preprocessor failed to load")
# Transform features and make prediction
data_scaled = preprocessor.transform(features)
preds = model.predict(data_scaled)
return preds
except Exception as e:
logging.error(f"Error in prediction: {str(e)}")
raise CustomException(e, sys)
class CustomData:
def __init__(self, gender: str, race: str,
parental_level_of_education: str,
lunch: str,
test_preparation_course: str,
reading_score: float,
writing_score: float):
self.gender = gender
self.race = race
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:
# Note: Column names must match your training data exactly
custom_data_input_dict = {
"gender": [self.gender],
"race/ethnicity": [self.race],
"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]
}
df = pd.DataFrame(custom_data_input_dict)
logging.info("Dataframe created successfully")
return df
except Exception as e:
logging.error(f"Error in Dataframe Creation: {str(e)}")
raise CustomException(e, sys) |