Update app.py

app.py

@@ -76,102 +76,102 @@ os.makedirs("data", exist_ok=True)
 app.config['MODEL_FOLDER'] = MODEL_FOLDER
 os.makedirs(app.config['MODEL_FOLDER'], exist_ok=True)
 
-# ------------------------------
-# Load Models and Label Encoders
-# ------------------------------
-
 # Prediction analysis models loaded from Hugging Face.
 
 
 #classsification model on the task
+# ----------------------------------------------
+# Code classification models for real data.
+# ----------------------------------------------
+#black code change
 src_path = hf_hub_download(
     repo_id="WebashalarForML/Diamond_model_",
-    filename="CLASS_DUMMY/LR_best_2_clas_shp_change.pkl",
+    filename="CLASS_DUMMY/DT_best__2_class_blk(M)_change.pkl",
     cache_dir=MODEL_FOLDER
 )
-dst_path = os.path.join(MODEL_FOLDER, "LR_best_2_clas_shp_change.pkl")
+dst_path = os.path.join(MODEL_FOLDER, "DT_best__2_class_blk(M)_change.pkl")
 shutil.copy(src_path, dst_path)
-shape_change = load(dst_path)
-
+blk_change = load(dst_path)
 
+# white code change
 src_path = hf_hub_download(
     repo_id="WebashalarForML/Diamond_model_",
-    filename="CLASS_DUMMY/LR_best_2_class_blk(M)_change.pkl",
+    filename="CLASS_DUMMY/DT_best__2_class_wht(M)_change.pkl",
     cache_dir=MODEL_FOLDER
 )
-dst_path = os.path.join(MODEL_FOLDER, "LR_best_2_class_blk(M)_change.pkl")
+dst_path = os.path.join(MODEL_FOLDER, "DT_best__2_class_wht(M)_change.pkl")
 shutil.copy(src_path, dst_path)
-blk_change = load(dst_path)
-'''
-# Prediction analysis models loaded from Hugging Face.
+wht_change = load(dst_path)
+
+# pav code change
 src_path = hf_hub_download(
     repo_id="WebashalarForML/Diamond_model_",
-    filename="CLASS_DUMMY/LR_best_2_class_wht(M)_change.pkl",
+    filename="CLASS_DUMMY/DT_best__2_class_pav(M)_change.pkl",
     cache_dir=MODEL_FOLDER
 )
-dst_path = os.path.join(MODEL_FOLDER, "LR_best_2_class_wht(M)_change.pkl")
+dst_path = os.path.join(MODEL_FOLDER, "DT_best__2_class_pav(M)_change.pkl")
 shutil.copy(src_path, dst_path)
-wht_change = load(dst_path)
-
+pav_change = load(dst_path)
 
+#open code change
 src_path = hf_hub_download(
     repo_id="WebashalarForML/Diamond_model_",
-    filename="CLASS_DUMMY/LR_best_2_class_pav(M)_change.pkl",
+    filename="CLASS_DUMMY/DT_best__2_class_open(M)_change.pkl",
     cache_dir=MODEL_FOLDER
 )
-dst_path = os.path.join(MODEL_FOLDER, "LR_best_2_class_pav(M)_change.pkl")
+dst_path = os.path.join(MODEL_FOLDER, "DT_best__2_class_open(M)_change.pkl")
 shutil.copy(src_path, dst_path)
-pav_change = load(dst_path)
+open_change = load(dst_path)
+
+# ----------------------------------------------
+# parameter classification models for real data.
+# ----------------------------------------------
 
+#shape change
 src_path = hf_hub_download(
     repo_id="WebashalarForML/Diamond_model_",
-    filename="CLASS_DUMMY/LR_best_2_class_open(M)_change.pkl",
+    filename="CLASS_DUMMY/DT_best__2_class_shp_change.pkl",
     cache_dir=MODEL_FOLDER
 )
-dst_path = os.path.join(MODEL_FOLDER, "LR_best_2_class_open(M)_change.pkl")
+dst_path = os.path.join(MODEL_FOLDER, "DT_best__2_class_shp_change.pkl")
 shutil.copy(src_path, dst_path)
-open_change = load(dst_path)
+shape_change = load(dst_path)
 
-# other param class change
+# color change
 src_path = hf_hub_download(
     repo_id="WebashalarForML/Diamond_model_",
-    filename="CLASS_DUMMY/LR_best_2_class_col_change.pkl",
+    filename="CLASS_DUMMY/DT_best__2_class_col_change.pkl",
     cache_dir=MODEL_FOLDER
 )
-dst_path = os.path.join(MODEL_FOLDER, "LR_best_2_class_col_change.pkl")
+dst_path = os.path.join(MODEL_FOLDER, "DT_best__2_class_col_change.pkl")
 shutil.copy(src_path, dst_path)
 col_change = load(dst_path)
 
+# quality change
 src_path = hf_hub_download(
     repo_id="WebashalarForML/Diamond_model_",
-    filename="CLASS_DUMMY/LR_best_2_class_qua_change.pkl",
+    filename="CLASS_DUMMY/LR_best__2_class_qua_change.pkl",
     cache_dir=MODEL_FOLDER
 )
-dst_path = os.path.join(MODEL_FOLDER, "LR_best_2_class_qua_change.pkl")
+dst_path = os.path.join(MODEL_FOLDER, "LR_best__2_class_qua_change.pkl")
 shutil.copy(src_path, dst_path)
 qua_change = load(dst_path)
 
+# cut change
 src_path = hf_hub_download(
     repo_id="WebashalarForML/Diamond_model_",
-    filename="CLASS_DUMMY/LR_best_2_class_cut_change.pkl",
+    filename="CLASS_DUMMY/LR_best__2_class_cut_change.pkl",
     cache_dir=MODEL_FOLDER
 )
-dst_path = os.path.join(MODEL_FOLDER, "LR_best_2_class_cut_change.pkl")
+dst_path = os.path.join(MODEL_FOLDER, "LR_best__2_class_cut_change.pkl")
 shutil.copy(src_path, dst_path)
 cut_change = 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))
 
 # List of label encoder names.
 encoder_list = [
-    'Tag', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol', 'EngSym', 'EngFlo',
+    'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol', 'EngSym', 'EngFlo',
     'EngNts', 'EngMikly','EngBlk', 'EngWht', 'EngOpen','EngPav',
     'Change_cts_value', 'Change_shape_value', 'Change_quality_value', 'Change_color_value',
     'Change_cut_value', 'Change_Blk_Eng_to_Mkbl_value', 'Change_Wht_Eng_to_Mkbl_value',
@@ -189,15 +189,15 @@ for val in encoder_list:
     encoder_file = enc_path / f"label_encoder_{val}.joblib"
     loaded_label_encoder[val] = load(encoder_file)
 
-# ------------------------------
+# -----------------------------------------
 # Utility: Allowed File Check
-# ------------------------------
+# -----------------------------------------
 def allowed_file(filename):
     return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
 
-# ------------------------------
+# -----------------------------------------
 # Routes
-# ------------------------------
+# -----------------------------------------
 @app.route('/')
 def index():
     return render_template('index.html')
@@ -249,17 +249,15 @@ def predict():
         print('Invalid file type. Only CSV and Excel files are allowed.', 'error')
         return redirect(url_for('index'))
 
-
-
 def process_dataframe(df):
     try:
         #df = df[df["MkblAmt"].notna()]
         
         # Define the columns needed for two parts.
-        required_columns = ['Tag', 'EngCts', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol',
+        required_columns = ['EngCts', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol',
                            'EngSym', 'EngFlo', 'EngNts', 'EngMikly', 'EngBlk', 'EngWht', 'EngOpen',
                            'EngPav', 'EngAmt']
-        required_columns_2 = ['Tag', 'EngCts', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol',
+        required_columns_2 = ['EngCts', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol',
                            'EngSym', 'EngFlo', 'EngNts', 'EngMikly', 'EngAmt']
         
         # Create two DataFrames: one for prediction and one for classification.
@@ -269,7 +267,7 @@ def process_dataframe(df):
         df_class = df[required_columns_2].fillna("NA").copy()
         
         # Transform categorical columns for prediction DataFrame using the label encoders.
-        for col in ['Tag', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol', 'EngSym', 'EngFlo', 'EngNts', 'EngMikly','EngBlk', 'EngWht', 'EngOpen', 'EngPav']:
+        for col in ['EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol', 'EngSym', 'EngFlo', 'EngNts', 'EngMikly','EngBlk', 'EngWht', 'EngOpen', 'EngPav']:
             try:
                 encoder = loaded_label_encoder[col]
                 df_pred[col] = df_pred[col].map(lambda x: encoder.transform([x])[0] if x in encoder.classes_ else -1)
@@ -279,38 +277,24 @@ def process_dataframe(df):
                 return pd.DataFrame(), pd.DataFrame()
         
         # Update the classification DataFrame with the transformed prediction columns.
-        for col in ['Tag', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol', 'EngSym', 'EngFlo', 'EngNts', 'EngMikly']:
+        for col in ['EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol', 'EngSym', 'EngFlo', 'EngNts', 'EngMikly']:
             df_class[col] = df_pred[col]
         
-        # Transform the extra columns in the classification DataFrame.
-        #for col in ['EngBlk', 'EngWht', 'EngOpen', 'EngPav']:
-        #    try:
-        #        df_class[col] = loaded_label_encoder[col].transform(df_class[col])
-        #    except ValueError as e:
-        #        print(f'Invalid value in column {col}: {e}', 'error')
-        #        return pd.DataFrame(), pd.DataFrame()
-        
-        # Convert both DataFrames to float.
         df_pred = df_pred.astype(float)
         df_class = df_class.astype(float)
         
-        # -------------------------
+        # ------------------------------------
         # Prediction Report Section
-        # -------------------------
+        # ------------------------------------
         try:
         
             # for model BLK CODE
             df_pred_0 = df_pred.copy()
             df_pred_0['Change_Blk_Eng_to_Mkbl_value'] = pd.DataFrame(blk_change.predict(df_pred), columns=["Change_Blk_Eng_to_Mkbl_value"])
             print(df_pred_0.columns)
-
-            # for model SHP CODE (need change)
-            df_pred_0['Change_shape_value'] = pd.DataFrame(shape_change.predict(df_pred), columns=["Change_shape_value"])
-            print(df_pred_0.columns)
             
-            '''
             # for model WHT CODE
-            df_pred_0['Change_Wht_Eng_to_Mkbl_value'] = pd.DataFrame(shape_change.predict(df_pred), columns=["Change_Wht_Eng_to_Mkbl_value"])
+            df_pred_0['Change_Wht_Eng_to_Mkbl_value'] = pd.DataFrame(wht_change.predict(df_pred), columns=["Change_Wht_Eng_to_Mkbl_value"])
             print(df_pred_0.columns)
 
             # for model PAV CODE (need change)
@@ -321,30 +305,34 @@ def process_dataframe(df):
             df_pred_0['Change_Open_Eng_to_Mkbl_value'] = pd.DataFrame(mkble_amt_class_model.predict(df_pred), columns=["Change_Open_Eng_to_Mkbl_value"])
             print(df_pred_0.columns)
 
+            # for model SHP CODE (need change)
+            df_pred_0['Change_shape_value'] = pd.DataFrame(shape_change.predict(df_class), columns=["Change_shape_value"])
+            print(df_pred_0.columns)
+
             # for model COL CODE (need change)
-            df_pred_0['Change_color_value'] = pd.DataFrame(mkble_amt_class_model.predict(df_pred), columns=["Change_color_value"])
+            df_pred_0['Change_color_value'] = pd.DataFrame(mkble_amt_class_model.predict(df_class), columns=["Change_color_value"])
             print(df_pred_0.columns)
 
             # for model CUT CODE (need change)
-            df_pred_0['Change_cut_value'] = pd.DataFrame(mkble_amt_class_model.predict(df_pred), columns=["Change_cut_value"])
+            df_pred_0['Change_cut_value'] = pd.DataFrame(mkble_amt_class_model.predict(df_class), columns=["Change_cut_value"])
             print(df_pred_0.columns)
 
             # for model QUA CODE (need change)
-            df_pred_0['Change_quality_value'] = pd.DataFrame(mkble_amt_class_model.predict(df_pred), columns=["Change_quality_value"])
+            df_pred_0['Change_quality_value'] = pd.DataFrame(mkble_amt_class_model.predict(df_class), columns=["Change_quality_value"])
             print(df_pred_0.columns)            
-            '''
+            
             # Concatenate the DataFrames row-wise
             #df_pred_main = pd.concat([df_pred_0, df_pred_1, df_pred_0], ignore_index=True)
             df_pred_main = df_pred_0.copy()
     
-            for col in ['Tag', 'EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol', 'EngSym', 'EngFlo',
+            for col in ['EngShp', 'EngQua', 'EngCol', 'EngCut', 'EngPol', 'EngSym', 'EngFlo',
                         'EngNts', 'EngMikly','EngBlk', 'EngWht', 'EngOpen','EngPav',
                         'Change_shape_value',
-                        #'Change_cts_value','Change_quality_value', 'Change_color_value', 'Change_cut_value',
+                        'Change_cts_value','Change_quality_value', 'Change_color_value', 'Change_cut_value',
                         'Change_Blk_Eng_to_Mkbl_value', 
-                        #'Change_Wht_Eng_to_Mkbl_value',
-                        #'Change_Open_Eng_to_Mkbl_value',
-                        #'Change_Pav_Eng_to_Mkbl_value', 
+                        'Change_Wht_Eng_to_Mkbl_value',
+                        'Change_Open_Eng_to_Mkbl_value',
+                        'Change_Pav_Eng_to_Mkbl_value', 
                         #'Change_Blk_Eng_to_Grd_value','Change_Wht_Eng_to_Grd_value', 'Change_Open_Eng_to_Grd_value', 'Change_Pav_Eng_to_Grd_value',
                         #'Change_Blk_Eng_to_ByGrd_value', 'Change_Wht_Eng_to_ByGrd_value', 'Change_Open_Eng_to_ByGrd_value', 'Change_Pav_Eng_to_ByGrd_value', 
                         #'Change_Blk_Eng_to_Gia_value', 'Change_Wht_Eng_to_Gia_value', 'Change_Open_Eng_to_Gia_value', 'Change_Pav_Eng_to_Gia_value'
@@ -384,9 +372,9 @@ def process_dataframe(df):
         print(f'Error processing file: {e}', 'error')
         return pd.DataFrame(), pd.DataFrame()
 
-# ------------------------------
+# ----------------------------------------------------
 # Report View Route with Pagination & Toggle
-# ------------------------------
+# ----------------------------------------------------
 
 @app.route("/report")
 def report_view():