Update app.py

app.py

@@ -82,13 +82,14 @@ os.makedirs(app.config['MODEL_FOLDER'], exist_ok=True)
 # Prediction analysis models loaded from Hugging Face.
 src_path = hf_hub_download(
     repo_id="WebashalarForML/Diamond_model_",
-    filename="models_list/mkble/DecisionTree_best_pipeline_mkble_with_assitance.pkl",
+    filename="models_list/mkble/DecisionTree_best_pipeline_mkble_0_to_0.99.pkl",
     cache_dir=MODEL_FOLDER
 )
-dst_path = os.path.join(MODEL_FOLDER, "DecisionTree_best_pipeline_mkble_with_assitance.pkl")
+dst_path = os.path.join(MODEL_FOLDER, "DecisionTree_best_pipeline_mkble_0_to_0.99.pkl")
 shutil.copy(src_path, dst_path)
 makable_model = load(dst_path)
 
+'''
 src_path = hf_hub_download(
     repo_id="WebashalarForML/Diamond_model_",
     filename="models_list/grd/StackingRegressor_best_pipeline_grd_0_to_1.01.pkl",
@@ -117,7 +118,7 @@ src_path = hf_hub_download(
 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)
-
+'''
 
 #classsification model on the task
 src_path = hf_hub_download(
@@ -133,9 +134,9 @@ mkble_amt_class_model = load(dst_path)
 
 
 print("makable_model type:", type(makable_model))
-print("grade_model type:", type(grade_model))
-print("bygrade_model type:", type(bygrade_model))
-print("gia_model type:", type(gia_model))
+#print("grade_model type:", type(grade_model))
+#print("bygrade_model type:", type(bygrade_model))
+#print("gia_model type:", type(gia_model))
 print("================================")
 print("mkble_amt_class_model type:", type(mkble_amt_class_model))
 
@@ -260,7 +261,9 @@ def process_dataframe(df):
         
         # Create two DataFrames: one for prediction and one for classification.
         df_pred = df[required_columns].copy()
+        df_pred = df_pred[(df_pred[['EngCts']] > 0.00).all(axis=1) & (df_pred[['EngCts']] <= 0.99).all(axis=1)]
         df_pred[['EngBlk', 'EngWht', 'EngOpen', 'EngPav']]=df_pred[['EngBlk', 'EngWht', 'EngOpen', 'EngPav']].fillna("NA")
+        df_pred = df_pred[(df_pred[['MkblAmt', 'GrdAmt', 'ByGrdAmt', 'GiaAmt', 'EngCts']] != 0).all(axis=1)]
         df_class = df[required_columns_2].fillna("NA").copy()
         
         # Transform categorical columns for prediction DataFrame using the label encoders.