profitboost / prediction.py
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import pickle
import pandas as pd
# In your model training script and your Streamlit app script (app.py)
from transformers import UnitPriceTransformer, KMeansAndLabelTransformer, DynamicOneHotEncoder
# Load the pipeline and model
# Load the pipeline object from the file
with open('full_pipeline_with_unit_price.pkl', 'rb') as file:
pipeline = pickle.load(file)
# Load the preprocessor object from the file
with open('preprocessor.pkl', 'rb') as file:
preprocessor = pickle.load(file)
# Load the model object from the file
with open('best_model.pkl', 'rb') as file:
model = pickle.load(file)
def make_prediction(input_features):
# Assuming input_features is a DataFrame with the correct structure
processed_features = pipeline.transform(input_features)
prediction = model.predict(processed_features)
return prediction[0]