Upload app.py with huggingface_hub

app.py

@@ -30,7 +30,7 @@ def load_resources():
         vectorizer = joblib.load(os.path.join(ARTIFACTS_DIR, 'tfidf_vectorizer.pkl'))
 
         # Load Price Artifacts
-        price_scaler = joblib.load(os.path.join(ARTIFACTS_DIR, 'scaler.pkl'))
+        price_preprocess = joblib.load(os.path.join(ARTIFACTS_DIR, 'scaler.pkl'))
         price_cols = joblib.load(os.path.join(ARTIFACTS_DIR, 'feature_columns.pkl'))
 
         # Load Models
@@ -40,12 +40,12 @@ def load_resources():
             "MLP": joblib.load(os.path.join(ARTIFACTS_DIR, 'MLP_price.pkl'))
         }
 
-        return vectorizer, price_scaler, price_cols, price_models, final_stopwords
+        return vectorizer, price_preprocess, price_cols, price_models, final_stopwords
     except Exception as e:
         st.error(f"Error loading resources: {e}")
         return None, None, None, None, None
 
-(vectorizer, price_scaler, price_cols,
+(vectorizer, price_preprocess, price_cols,
  price_models, final_stopwords) = load_resources()
 
 @st.cache_data
@@ -217,7 +217,7 @@ if price_models:
 
     if st.button("Predict Price", type="primary"):
         raw_df = prepare_raw_dataframe(base_input, price_cols, vectorizer)
-        X_scaled = price_scaler.transform(raw_df)
+        X_scaled = price_preprocess.transform(raw_df)
 
         log_pred = price_models[model_name].predict(X_scaled)[0]
         price_pred = np.expm1(log_pred)