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| import streamlit as st | |
| from pathlib import Path | |
| import pandas as pd | |
| import joblib | |
| from huggingface_hub import hf_hub_download | |
| # def get_root(): | |
| # # app_utils is inside streamlit_app, so parent is streamlit_app | |
| # return Path(__file__).resolve().parent.parent | |
| # # def get_model_path(): | |
| # # return get_root() / "models" / "sgdc_pipeline.joblib" | |
| # def get_data_path(): | |
| # return get_root() / "models" / "demo_data.parquet" | |
| # @st.cache_resource(show_spinner='Loading model') | |
| # def load_model(): | |
| # path = get_model_path() | |
| # if not path.exists(): | |
| # raise FileNotFoundError(f"Model file not found at: {path}") | |
| # return joblib.load(path) | |
| # Use this so it only downloads once per session | |
| def load_model(): | |
| # Download the model file from your new Model Repo | |
| model_path = hf_hub_download( | |
| repo_id="tkbarb10/ads505-prediction-model", | |
| filename="sgdc_pipeline.joblib" | |
| ) | |
| # Load the model using joblib (or whatever library you used to save it) | |
| return joblib.load(model_path) | |
| def load_demo_data(): | |
| # Download the parquet file from your Dataset Repo | |
| file_path = hf_hub_download( | |
| repo_id="tkbarb10/ads505-review-data", | |
| repo_type="dataset", | |
| filename="demo_data.parquet" | |
| ) | |
| return pd.read_parquet(file_path) |