Update app.py

app.py

@@ -45,19 +45,46 @@ os.makedirs(app.config['MODEL_FOLDER'], exist_ok=True)
 
 # prediction analysis
 # Download the model file to the specified location
-file_path = hf_hub_download(
+file_path_1 = hf_hub_download(
     repo_id="WebashalarForML/Diamond_model_",
-    filename="models_list/bygrad/CatBoost_best_pipeline_BYGRADE.pkl",
-    cache_dir=specific_location
+    filename="models_list/mkble/StackingRegressor_best_pipeline_mkble_0_to_1.01.pkl",
+    cache_dir=MODEL_FOLDER
 )
 
-with open(file_path, "rb") as f:
-    model = pickle.load(f)
+with open(file_path_1, "rb") as f:
+    makable_model = pickle.load(f)
 
-gia_model = load(os.path.join(MODEL_DIR, 'linear_regression_model_gia_price.joblib'))
-grade_model = load(os.path.join(MODEL_DIR, 'linear_regression_model_grade_price.joblib'))
-bygrade_model = load(os.path.join(MODEL_DIR, 'linear_regression_model_bygrade_price.joblib'))
-makable_model = load(os.path.join(MODEL_DIR, 'linear_regression_model_makable_price.joblib'))
+file_path_2 = hf_hub_download(
+    repo_id="WebashalarForML/Diamond_model_",
+    filename="models_list/grd/StackingRegressor_best_pipeline_grd_0_to_1.01.pkl",
+    cache_dir=MODEL_FOLDER
+)
+
+with open(file_path_2, "rb") as f:
+    grade_model = pickle.load(f)
+
+file_path_3 = hf_hub_download(
+    repo_id="WebashalarForML/Diamond_model_",
+    filename="models_list/bygrad/StackingRegressor_best_pipeline_bygrad_0_to_1.01.pkl",
+    cache_dir=MODEL_FOLDER
+)
+
+with open(file_path_3, "rb") as f:
+    bygrade_model = pickle.load(f)
+
+file_path_4 = hf_hub_download(
+    repo_id="WebashalarForML/Diamond_model_",
+    filename="models_list/gia/StackingRegressor_best_pipeline_gia_0_to_1.01.pkl",
+    cache_dir=MODEL_FOLDER
+)
+
+with open(file_path_4, "rb") as f:
+    gia_model = pickle.load(f)
+
+#gia_model = load(os.path.join(MODEL_DIR, 'linear_regression_model_gia_price.joblib'))
+#grade_model = load(os.path.join(MODEL_DIR, 'linear_regression_model_grade_price.joblib'))
+#bygrade_model = load(os.path.join(MODEL_DIR, 'linear_regression_model_bygrade_price.joblib'))
+#makable_model = load(os.path.join(MODEL_DIR, 'linear_regression_model_makable_price.joblib'))
 
 # classifcation analysis
 col_model = load(os.path.join(MODEL_DIR, 'classification_LogisticRegression_col.joblib'))