diff --git a/aimbot.py b/aimbot.py
index a69f18a..93ac225 100644
--- a/aimbot.py
+++ b/aimbot.py
@@ -1,81 +1,16 @@
+import tensorflow as tf
+import tensorflow_hub as hub
+import numpy as np
 import pyautogui
 import win32api, win32con, win32gui
 import cv2
 import math
 import time
-import argparse
-import os
-import json
-import numpy as np
-from threading import Lock
-
-# printing only warnings and error messages
-os.environ["TF_CPP_MIN_LOG_LEVEL"] = "1"
-
-try:
-    import tensorflow as tf
-    from PIL import Image
-except ImportError:
-    raise ImportError("ERROR: Failed to import libraries. Please refer to READEME.md file\n")
-
-EXPORT_MODEL_VERSION = 1
-
-
-class TFModel:
-    def __init__(self, dir_path) -> None:
-        # Assume model is in the parent directory for this file
-        self.model_dir = os.path.dirname(dir_path)
-        # make sure our exported SavedModel folder exists
-        with open(os.path.join(self.model_dir, "signature.json"), "r") as f:
-            self.signature = json.load(f)
-        self.model_file = os.path.join(self.model_dir, self.signature.get("filename"))
-        if not os.path.isfile(self.model_file):
-            raise FileNotFoundError(f"Model file does not exist")
-        self.inputs = self.signature.get("inputs")
-        self.outputs = self.signature.get("outputs")
-        self.lock = Lock()
-
-        # loading the saved model
-        self.model = tf.saved_model.load(tags=self.signature.get("tags"), export_dir=self.model_dir)
-        self.predict_fn = self.model.signatures["serving_default"]
-
-        # Look for the version in signature file.
-        # If it's not found or the doesn't match expected, print a message
-        version = self.signature.get("export_model_version")
-        if version is None or version != EXPORT_MODEL_VERSION:
-            print(
-                f"There has been a change to the model format. Please use a model with a signature 'export_model_version' that matches {EXPORT_MODEL_VERSION}."
-            )
-
-    def predict(self, image: Image.Image) -> dict:
-        with self.lock:
-            # create the feed dictionary that is the input to the model
-            feed_dict = {}
-            # first, add our image to the dictionary (comes from our signature.json file)
-            feed_dict[list(self.inputs.keys())[0]] = tf.convert_to_tensor(image)
-            # run the model!
-            outputs = self.predict_fn(**feed_dict)
-            # return the processed output
-            return self.process_output(outputs)
-
-    def process_output(self, outputs) -> dict:
-        # do a bit of postprocessing
-        out_keys = ["label", "confidence"]
-        results = {}
-        # since we actually ran on a batch of size 1, index out the items from the returned numpy arrays
-        for key, tf_val in outputs.items():
-            val = tf_val.numpy().tolist()[0]
-            if isinstance(val, bytes):
-                val = val.decode()
-            results[key] = val
-        confs = results["Confidences"]
-        labels = self.signature.get("classes").get("Label")
-        output = [dict(zip(out_keys, group)) for group in zip(labels, confs)]
-        sorted_output = {"predictions": sorted(output, key=lambda k: k["confidence"], reverse=True)}
-        return sorted_output
 
+dirname = os.path.dirname(__file__)
+detector = tf.saved_model.load(dirname)
 size_scale = 3
-model = TFModel(dir_path=os.path.dirname(__file__))
+
 while True:
     # Get rect of Window
     hwnd = win32gui.FindWindow(None, 'Roblox')
@@ -90,7 +25,7 @@ while True:
     img_w, img_h = image.shape[2], image.shape[1]
 
     # Detection
-    outputs = model.predict(image)
+    result = detector(image)
     result = {key:value.numpy() for key,value in result.items()}
     boxes = result['detection_boxes'][0]
     scores = result['detection_scores'][0]
@@ -140,4 +75,8 @@ while True:
         time.sleep(0.1)
         win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP, x, y, 0, 0)
 
-    time.sleep(0.1)
\ No newline at end of file
+    #ori_img = cv2.cvtColor(ori_img, cv2.COLOR_BGR2RGB)
+    #cv2.imshow("ori_img", ori_img)
+    #cv2.waitKey(1)
+
+    time.sleep(0.1)