diff --git a/main.py b/main.py
index 71369b2..8939656 100644
--- a/main.py
+++ b/main.py
@@ -23,6 +23,11 @@
 XGBOOST_COLOR = tuple(map(int, os.getenv("XGBOOST_COLOR", "255,0,0").split(",")))  # Default: Red
 LIGHTGBM_COLOR = tuple(map(int, os.getenv("LIGHTGBM_COLOR", "0,0,255").split(",")))  # Default: Blue
 
+# Get model execution settings
+CONV_ENABLED = os.getenv("CONV_ENABLED", "True").lower() == "true"
+XGBOOST_ENABLED = os.getenv("XGBOOST_ENABLED", "True").lower() == "true"
+LIGHTGBM_ENABLED = os.getenv("LIGHTGBM_ENABLED", "True").lower() == "true"
+
 
 def load_model(model_path, model_type="lgb"):
     with open(model_path, "rb") as model_file:
@@ -131,25 +136,32 @@
         rows.append(row)
 
     if rows:
-        fieldnames = list(rows[0].keys()) + [
-            "conv_stethoscope_x",
-            "conv_stethoscope_y",
-            "Xgboost_stethoscope_x",
-            "Xgboost_stethoscope_y",
-            "lightGBM_stethoscope_x",
-            "lightGBM_stethoscope_y",
-        ]
+        fieldnames = list(rows[0].keys())
+        if CONV_ENABLED:
+            fieldnames.extend(["conv_stethoscope_x", "conv_stethoscope_y"])
+        if XGBOOST_ENABLED:
+            fieldnames.extend(["Xgboost_stethoscope_x", "Xgboost_stethoscope_y"])
+        if LIGHTGBM_ENABLED:
+            fieldnames.extend(["lightGBM_stethoscope_x", "lightGBM_stethoscope_y"])
 
-        lgb_model_x = load_model("./models/lgb_stethoscope_calc_x_best_model-Fold4.pkl")
-        lgb_model_y = load_model("./models/lgb_stethoscope_calc_y_best_model-Fold4.pkl")
-        xg_model_x = load_model("./models/xg_stethoscope_calc_x_best_model-Fold4.pkl")
-        xg_model_y = load_model("./models/xg_stethoscope_calc_y_best_model-Fold4.pkl")
+        if LIGHTGBM_ENABLED:
+            lgb_model_x = load_model("./models/lgb_stethoscope_calc_x_best_model-Fold4.pkl")
+            lgb_model_y = load_model("./models/lgb_stethoscope_calc_y_best_model-Fold4.pkl")
+        if XGBOOST_ENABLED:
+            xg_model_x = load_model("./models/xg_stethoscope_calc_x_best_model-Fold4.pkl")
+            xg_model_y = load_model("./models/xg_stethoscope_calc_y_best_model-Fold4.pkl")
 
         with open(csv_path, "w", newline="") as csvfile:
             writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
             writer.writeheader()
 
-            prev_values = {"conv": (180, 180), "lightGBM": (180, 180), "Xgboost": (180, 180)}
+            prev_values = {}
+            if CONV_ENABLED:
+                prev_values["conv"] = (180, 180)
+            if LIGHTGBM_ENABLED:
+                prev_values["lightGBM"] = (180, 180)
+            if XGBOOST_ENABLED:
+                prev_values["Xgboost"] = (180, 180)
 
             for row in rows:
                 source_points = np.array(
@@ -165,8 +177,9 @@
                     for key in prev_values:
                         row[f"{key}_stethoscope_x"], row[f"{key}_stethoscope_y"] = prev_values[key]
                 else:
-                    conv_stethoscope = calc_position.calc_affine(source_points, *stethoscope_point)
-                    row["conv_stethoscope_x"], row["conv_stethoscope_y"] = conv_stethoscope
+                    if CONV_ENABLED:
+                        conv_stethoscope = calc_position.calc_affine(source_points, *stethoscope_point)
+                        row["conv_stethoscope_x"], row["conv_stethoscope_y"] = conv_stethoscope
 
                     normalized_points = normalize_quadrilateral_with_point(source_points.flatten(), stethoscope_point)
                     row_convert = {
@@ -180,12 +193,13 @@
                     input_data = pd.DataFrame([row_convert])
                     input_columns = list(row_convert.keys())
 
-                    for model_name, models in [
-                        ("lightGBM", (lgb_model_x, lgb_model_y)),
-                        ("Xgboost", (xg_model_x, xg_model_y)),
-                    ]:
-                        row[f"{model_name}_stethoscope_x"] = int(models[0].predict(input_data[input_columns])[0])
-                        row[f"{model_name}_stethoscope_y"] = int(models[1].predict(input_data[input_columns])[0])
+                    if LIGHTGBM_ENABLED:
+                        row["lightGBM_stethoscope_x"] = int(lgb_model_x.predict(input_data[input_columns])[0])
+                        row["lightGBM_stethoscope_y"] = int(lgb_model_y.predict(input_data[input_columns])[0])
+
+                    if XGBOOST_ENABLED:
+                        row["Xgboost_stethoscope_x"] = int(xg_model_x.predict(input_data[input_columns])[0])
+                        row["Xgboost_stethoscope_y"] = int(xg_model_y.predict(input_data[input_columns])[0])
 
                     for key in prev_values:
                         prev_values[key] = (row[f"{key}_stethoscope_x"], row[f"{key}_stethoscope_y"])
@@ -204,7 +218,13 @@
     results_dir = "images/body/results"
     os.makedirs(results_dir, exist_ok=True)
 
-    dirs = {"marked": "marked_images", "conv": "conv", "Xgboost": "Xgboost", "lightGBM": "lightGBM"}
+    dirs = {"marked": "marked_images"}
+    if CONV_ENABLED:
+        dirs["conv"] = "conv"
+    if XGBOOST_ENABLED:
+        dirs["Xgboost"] = "Xgboost"
+    if LIGHTGBM_ENABLED:
+        dirs["lightGBM"] = "lightGBM"
 
     # Create all necessary directories
     for dir_name in dirs.values():
@@ -212,7 +232,7 @@
         os.makedirs(os.path.join(results_dir, f"{dir_name}_with_trajectory"), exist_ok=True)
         os.makedirs(os.path.join(results_dir, f"{dir_name}_without_trajectory"), exist_ok=True)
 
-    points = {key: [] for key in ["conv", "Xgboost", "lightGBM"]}
+    points = {key: [] for key in dirs.keys() if key != "marked"}
     colors = {"conv": CONV_COLOR, "Xgboost": XGBOOST_COLOR, "lightGBM": LIGHTGBM_COLOR}
 
     for _, row in df.iterrows():