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]