Update app.py

app.py

@@ -48,6 +48,8 @@ UPLOAD_FOLDER = "uploads/"
 DATA_FOLDER = "data/"
 MODEL_FOLDER = "models/"
 
+os.makedirs(MODEL_FOLDER, exist_ok=True)
+
 # Define the model directory and label encoder directory
 MODEL_DIR = r'./Model'
 LABEL_ENCODER_DIR = r'./Label_encoders'  # Renamed for clarity
@@ -77,37 +79,44 @@ os.makedirs(app.config['MODEL_FOLDER'], exist_ok=True)
 # ------------------------------
 
 # Prediction analysis models loaded from Hugging Face.
-file_path_1 = hf_hub_download(
+src_path = hf_hub_download(
     repo_id="WebashalarForML/Diamond_model_",
     filename="models_list/mkble/StackingRegressor_best_pipeline_mkble_0_to_1.01.pkl",
     cache_dir=MODEL_FOLDER
 )
-with open(file_path_1, "rb") as f:
-    makable_model = pickle.load(f)
+dst_path = os.path.join(MODEL_FOLDER, "StackingRegressor_best_pipeline_mkble_0_to_1.01.pkl")
+shutil.copy(src_path, dst_path)
+makable_model = load(dst_path)
 
-file_path_2 = hf_hub_download(
+src_path = 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)
+dst_path = os.path.join(MODEL_FOLDER, "StackingRegressor_best_pipeline_grd_0_to_1.01.pkl")
+shutil.copy(src_path, dst_path)
+grade_model = load(dst_path)
+ 
 
-file_path_3 = hf_hub_download(
+src_path = 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)
+dst_path = os.path.join(MODEL_FOLDER, "StackingRegressor_best_pipeline_bygrad_0_to_1.01.pkl")
+shutil.copy(src_path, dst_path)
+bygrade_model = load(dst_path)
+
 
-file_path_4 = hf_hub_download(
+src_path = 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)
+dst_path = os.path.join(MODEL_FOLDER, "StackingRegressor_best_pipeline_gia_0_to_1.01.pkl")
+shutil.copy(src_path, dst_path)
+gia_model = load(dst_path)
+
 
 
 print("makable_model type:", type(makable_model))
@@ -115,10 +124,10 @@ print("grade_model type:", type(grade_model))
 print("bygrade_model type:", type(bygrade_model))
 print("gia_model type:", type(gia_model))
 
-gia_model = load("models/StackingRegressor_best_pipeline_mkble_0_to_1.01.pkl")
-grade_model = load("models/StackingRegressor_best_pipeline_grd_0_to_1.01.pkl")
-bygrade_model = load("models/StackingRegressor_best_pipeline_bygrad_0_to_1.01.pkl")
-makable_model = load("models/StackingRegressor_best_pipeline_gia_0_to_1.01.pkl")
+#gia_model = load("models/StackingRegressor_best_pipeline_mkble_0_to_1.01.pkl")
+#grade_model = load("models/StackingRegressor_best_pipeline_grd_0_to_1.01.pkl")
+#bygrade_model = load("models/StackingRegressor_best_pipeline_bygrad_0_to_1.01.pkl")
+#makable_model = load("models/StackingRegressor_best_pipeline_gia_0_to_1.01.pkl")
     
 
 # Classification models loaded using joblib.