diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 437541d7..88888037 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -7,8 +7,13 @@ repos:
     -   id: end-of-file-fixer
     -   id: trailing-whitespace
     -   id: check-added-large-files
-- repo: https://github.com/pre-commit/mirrors-clang-format
-  rev: v14.0.6
-  hooks:
-  - id: clang-format
-    types_or: [c++, c, cuda]
+-   repo: https://github.com/pre-commit/mirrors-clang-format
+    rev: v14.0.6
+    hooks:
+    -   id: clang-format
+        types_or: [c++, c, cuda]
+-   repo: https://github.com/PyCQA/flake8
+    rev: 7.0.0
+    hooks:
+    -   id: flake8
+        args: [--max-line-length=120]
diff --git a/yolov9/yolov9_trt.py b/yolov9/yolov9_trt.py
index 15a7737a..fe9d1df8 100644
--- a/yolov9/yolov9_trt.py
+++ b/yolov9/yolov9_trt.py
@@ -10,7 +10,7 @@
 import time
 import cv2
 import numpy as np
-import pycuda.autoinit
+import pycuda.autoinit  # noqa: F401
 import pycuda.driver as cuda
 import tensorrt as trt
 
@@ -36,7 +36,7 @@ def plot_one_box(x, img, color=None, label=None, line_thickness=None):
     """
     description: Plots one bounding box on image img,
                  this function comes from yolov9 project.
-    param: 
+    param:
         x:      a box likes [x1,y1,x2,y2]
         img:    a opencv image object
         color:  color to draw rectangle, such as (0,255,0)
@@ -129,7 +129,6 @@ def infer(self, raw_image_generator):
         # Restore
         stream = self.stream
         context = self.context
-        engine = self.engine
         host_inputs = self.host_inputs
         cuda_inputs = self.cuda_inputs
         host_outputs = self.host_outputs
@@ -274,11 +273,12 @@ def xywh2xyxy(self, origin_h, origin_w, x):
             y /= r_h
 
         return y
+
     def post_process(self, output, origin_h, origin_w):
         """
         description: postprocess the prediction
         param:
-            output:     A numpy likes [num_boxes,cx,cy,w,h,conf,cls_id, cx,cy,w,h,conf,cls_id, ...] 
+            output:     A numpy likes [num_boxes,cx,cy,w,h,conf,cls_id, cx,cy,w,h,conf,cls_id, ...]
             origin_h:   height of original image
             origin_w:   width of original image
         return:
@@ -302,7 +302,7 @@ def bbox_iou(self, box1, box2, x1y1x2y2=True):
         description: compute the IoU of two bounding boxes
         param:
             box1: A box coordinate (can be (x1, y1, x2, y2) or (x, y, w, h))
-            box2: A box coordinate (can be (x1, y1, x2, y2) or (x, y, w, h))            
+            box2: A box coordinate (can be (x1, y1, x2, y2) or (x, y, w, h))
             x1y1x2y2: select the coordinate format
         return:
             iou: computed iou
@@ -325,7 +325,7 @@ def bbox_iou(self, box1, box2, x1y1x2y2=True):
         inter_rect_y2 = np.minimum(b1_y2, b2_y2)
         # Intersection area
         inter_area = np.clip(inter_rect_x2 - inter_rect_x1 + 1, 0, None) * \
-                     np.clip(inter_rect_y2 - inter_rect_y1 + 1, 0, None)
+            np.clip(inter_rect_y2 - inter_rect_y1 + 1, 0, None)
         # Union Area
         b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)
         b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)