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from Support_module_dir.support_function_predict import predict_function
from Variable_artifects.artifact import CSV_FILE
from Variable_artifects.artifact import CSV_DIR
from datetime import datetime
import pandas as pd
import logging
import joblib
import os
class Prediction:
def __init__(self, saved_model_path):
self.saved_model_path = saved_model_path
@staticmethod
def convert_timestamp(timestamp):
"""
Function return timestamps
during prediction date column print into datestamps into millisecond started from 1970 till date.
to convert that back to today's time function is required
"""
try:
if isinstance(timestamp, pd.Timestamp):
timestamp = timestamp.value // 10 ** 6 # Convert nanoseconds to milliseconds
return datetime.utcfromtimestamp(timestamp / 1000).strftime('%Y-%m-%d')
except Exception as e:
raise e
def model_prediction(self, forecast_days):
"""
Function created for Data Ingestion
Returns: Pandas processed Dataframe and predicted values
"""
try:
CSV_PATH = os.path.join(CSV_DIR, CSV_FILE) # called from artifact module
logging.info(f"Model Prediction Module : Initiating module prediction")
dataset_frame = pd.read_csv(CSV_PATH)
dataset_frame.set_index('Date', inplace=True) # setting date columns as index
logging.info(f"Model Prediction Module : Loading model")
model = joblib.load(self.saved_model_path) # Load the trained model
# prediction function called from support_function material
logging.info(f"Model Prediction Module : prediction function called")
predictions, execution_time = predict_function(trained_model=model,
dataset=dataset_frame.Price,
forecast_days=forecast_days)
# Convert timestamps to a more readable date format
predictions['Date'] = predictions['Date'].apply(Prediction.convert_timestamp)
logging.info(f"Model Prediction Module : Exiting module STATUS OK")
return predictions, dataset_frame
except Exception as e:
logging.info(f"Model Prediction module: model prediction failed {e}")
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