Update product_return_prediction/api.py

product_return_prediction/api.py

@@ -1,18 +1,12 @@
+import json, os, pickle, requests
+import pandas as pd
 from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel, Field
 from datasets import load_dataset
-import pandas as pd
-import json
-import os
-import pickle
-import requests
 from pathlib import Path
 from product_return_prediction.dataset import prepare_inventory, scale_data_with_trained_scaler
 from product_return_prediction.monitoring import instrumentator
-from product_return_prediction.config import MODELS_DIR, EXTERNAL_DATA_DIR
-from huggingface_hub import hf_hub_download
-
-from huggingface_hub import login
+from huggingface_hub import hf_hub_download, login
 
 login(token=os.getenv("HUGGINGFACE_TOKEN"))
 
@@ -151,18 +145,7 @@ async def predict(products: ProductRequest):
         filtered_inventory, products.total_customer_purchases, products.total_customer_returns
     )
 
-    # models_uri = "https://huggingface.co/molinari135/se4ai-models/resolve/main/"
-    # model_path = MODELS_DIR / model_name
-    # scaler_path = MODELS_DIR / scaler_name
-
-    # headers = f"Authorization: Bearer {hf_token}"
-
-    # download_file(f"{models_uri}{model_name}", model_path, headers)
-    # download_file(f"{models_uri}{scaler_name}", scaler_path, headers)
-
     model = load_model(hf_hub_download(repo_id="molinari135/se4ai-models", filename="svm.pkl"))
-
-    # model = pickle.load(hf_hub_download(repo_id="molinari135/se4ai-models", filename="svm.pkl"))
     
     scaled_inventory = apply_scaling(prepared_inventory, scaler_file)
     predictions, probabilities = make_predictions(model, scaled_inventory)