Update app.py

app.py

@@ -82,10 +82,10 @@ 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/StackingRegressor_best_pipeline_mkble_0_to_1.01.pkl",
+    filename="models_list/mkble/DecisionTree_best_pipeline_mkble_with_assitance.pkl",
     cache_dir=MODEL_FOLDER
 )
-dst_path = os.path.join(MODEL_FOLDER, "StackingRegressor_best_pipeline_mkble_0_to_1.01.pkl")
+dst_path = os.path.join(MODEL_FOLDER, "DecisionTree_best_pipeline_mkble_with_assitance.pkl")
 shutil.copy(src_path, dst_path)
 makable_model = load(dst_path)
 
@@ -119,11 +119,25 @@ shutil.copy(src_path, dst_path)
 gia_model = load(dst_path)
 
 
+#classsification model on the task
+src_path = hf_hub_download(
+    repo_id="WebashalarForML/Diamond_model_",
+    filename="models_list/classification/3_pipeline.pkl",
+    cache_dir=MODEL_FOLDER
+)
+dst_path = os.path.join(MODEL_FOLDER, "3_pipeline.pkl")
+shutil.copy(src_path, dst_path)
+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("================================")
+print("mkble_amt_class_model type:", type(mkble_amt_class_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")
@@ -278,13 +292,19 @@ def process_dataframe(df):
         # -------------------------
         try:
             x = df_pred.copy()
-            df_pred['GIA_Predicted'] = pd.DataFrame(np.expm1(gia_model.predict(x)), columns=["Predicted"])
-            df_pred['Grade_Predicted'] = pd.DataFrame(np.expm1(grade_model.predict(x)), columns=["Predicted"])
-            df_pred['ByGrade_Predicted'] = pd.DataFrame(np.expm1(bygrade_model.predict(x)), columns=["Predicted"])
+            #df_pred['GIA_Predicted'] = pd.DataFrame(np.expm1(gia_model.predict(x)), columns=["Predicted"])
+            #df_pred['Grade_Predicted'] = pd.DataFrame(np.expm1(grade_model.predict(x)), columns=["Predicted"])
+            #df_pred['ByGrade_Predicted'] = pd.DataFrame(np.expm1(bygrade_model.predict(x)), columns=["Predicted"])
+            df_pred['change_in_amt_mkble'] = pd.DataFrame(mkble_amt_class_model.predict(x), columns=["pred_change_in_eng_to_mkble"])
+            print("df_pred")
+            df_pred = df_pred[['Tag', 'EngCts', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol',
+                               'EngSym', 'EngFlo', 'EngNts', 'EngMikly', 'EngBlk', 'EngWht', 'EngOpen',
+                               'EngPav', 'EngAmt', 'change_in_amt_mkble']]
             df_pred['Makable_Predicted'] = pd.DataFrame(np.expm1(makable_model.predict(x)), columns=["Predicted"])
-            df_pred['GIA_Diff'] = df_pred['EngAmt'] - df_pred['GIA_Predicted']
-            df_pred['Grade_Diff'] = df_pred['EngAmt'] - df_pred['Grade_Predicted']
-            df_pred['ByGrade_Diff'] = df_pred['EngAmt'] - df_pred['ByGrade_Predicted']
+            
+            #df_pred['GIA_Diff'] = df_pred['EngAmt'] - df_pred['GIA_Predicted']
+            #df_pred['Grade_Diff'] = df_pred['EngAmt'] - df_pred['Grade_Predicted']
+            #df_pred['ByGrade_Diff'] = df_pred['EngAmt'] - df_pred['ByGrade_Predicted']
             df_pred['Makable_Diff'] = df_pred['EngAmt'] - df_pred['Makable_Predicted']
             
             for col in ['Tag', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol', 'EngSym', 'EngFlo', 'EngNts', 'EngMikly','EngBlk', 'EngWht', 'EngOpen', 'EngPav']: