diff --git a/scripts/export_to_onnx.py b/scripts/export_to_onnx.py
index 8420db2b..38417a45 100644
--- a/scripts/export_to_onnx.py
+++ b/scripts/export_to_onnx.py
@@ -1,11 +1,9 @@
-import os
-import sys
 import argparse
 from pathlib import Path
 
 import torch
 
-from src.inference.utils.inference_factory import InferenceFactory, InferenceConfig, Backend, ModelArch
+from src.inference.utils.inference_factory import Backend, InferenceConfig, InferenceFactory, ModelArch
 from src.path_utils import ensure_clean_directory
 
 NUM_CLASSES = 83
@@ -13,6 +11,7 @@
 DEVICE = "cpu"
 MODELS_DIR_PATH = Path("models")
 
+
 def main(model_name: str):
     arch = ModelArch.RESNET if "resnet" in model_name.lower() else ModelArch.MOBILENET
     backend = Backend.PYTORCH
diff --git a/scripts/onnx_validation.py b/scripts/onnx_validation.py
index 6fe64c81..e98c4312 100644
--- a/scripts/onnx_validation.py
+++ b/scripts/onnx_validation.py
@@ -1,5 +1,4 @@
 import os
-import argparse
 from pathlib import Path
 
 import numpy as np
@@ -7,9 +6,9 @@
 from PIL import Image
 
 from dataset.optimal_class_mapping import MODEL_NAMES as ID_TO_NAME
-from src.inference.utils.inference_factory import InferenceFactory, InferenceConfig, Backend, ModelArch
 from src.inference.base.classifier_inference_base import ClassifierInferenceBase
 from src.inference.base.classifier_inference_base_onnx import OnnxClassifierInferenceBase
+from src.inference.utils.inference_factory import Backend, InferenceConfig, InferenceFactory, ModelArch
 
 NUM_CLASSES = 83
 INPUT_SIZE = (256, 256)
diff --git a/services/consumer/consumer.py b/services/consumer/consumer.py
index ec2b3cf6..981362c2 100644
--- a/services/consumer/consumer.py
+++ b/services/consumer/consumer.py
@@ -1,28 +1,32 @@
-import paho.mqtt.client as mqtt
-import pymongo
 import json
 from datetime import datetime
 
+import paho.mqtt.client as mqtt
+import pymongo
+
+
 def on_connect(client, userdata, flags, rc):
     print(f"🔗 Connected: {rc}")
     client.subscribe("deepstream/predictions")
 
+
 def on_message(client, userdata, msg):
     try:
         payload = json.loads(msg.payload.decode())
         payload["received_at"] = datetime.now().isoformat()
-        
+
         mongo_client = pymongo.MongoClient("mongodb://agstream_mongo:27017/")
         db = mongo_client["agstream"]
         collection = db["predictions"]
-        
+
         collection.insert_one(payload)
-        print(f"💾 Saved to MongoDB!")
+        print("💾 Saved to MongoDB!")
         mongo_client.close()
-        
+
     except Exception as e:
         print(f"❌ Error: {e}")
 
+
 client = mqtt.Client()
 client.on_connect = on_connect
 client.on_message = on_message
diff --git a/services/consumer/mqtt_consumer.py b/services/consumer/mqtt_consumer.py
index bdafae45..28bc3010 100644
--- a/services/consumer/mqtt_consumer.py
+++ b/services/consumer/mqtt_consumer.py
@@ -1,9 +1,10 @@
 #!/usr/bin/env python3
-import paho.mqtt.client as mqtt
-import pymongo
 import json
 from datetime import datetime
 
+import paho.mqtt.client as mqtt
+import pymongo
+
 MQTT_BROKER = "agstream_mosquitto"
 MQTT_PORT = 1883
 MQTT_TOPIC = "deepstream/predictions"
@@ -11,49 +12,49 @@
 MONGO_DB = "agstream"
 MONGO_COLLECTION = "predictions"
 
+
 def on_connect(client, userdata, flags, rc):
     print(f"✅ Connected to MQTT broker with result code {rc}")
     client.subscribe(MQTT_TOPIC)
 
+
 def on_message(client, userdata, msg):
     try:
         payload = json.loads(msg.payload.decode())
         payload["received_at"] = datetime.now().isoformat()
-        
+
         # Extract classification data from nvmsgbroker format
         obj = payload.get("object", {})
         if obj.get("id") != "0" and obj.get("id"):
             class_id = int(obj["id"])
             confidence = obj.get("confidence", 0)
-            
+
             # Add extracted classification
-            payload["classification"] = {
-                "class_id": class_id,
-                "confidence": confidence
-            }
+            payload["classification"] = {"class_id": class_id, "confidence": confidence}
             print(f"🌱 FOUND CLASSIFICATION: ID {class_id}, confidence {confidence:.3f}")
-        
+
         mongo_client = pymongo.MongoClient(MONGO_URI)
         db = mongo_client[MONGO_DB]
         collection = db[MONGO_COLLECTION]
-        
+
         result = collection.insert_one(payload)
-        
+
         if "classification" in payload:
-            print(f"✅ Saved classification to MongoDB!")
+            print("✅ Saved classification to MongoDB!")
         else:
-            print(f"✅ Saved: No classification")
-        
+            print("✅ Saved: No classification")
+
         mongo_client.close()
-        
+
     except Exception as e:
         print(f"❌ Error: {e}")
 
+
 if __name__ == "__main__":
     client = mqtt.Client()
     client.on_connect = on_connect
     client.on_message = on_message
-    
+
     print("🚀 Starting Enhanced MQTT Consumer...")
     client.connect(MQTT_BROKER, MQTT_PORT, 60)
     client.loop_forever()
diff --git a/src/deepstream/helpers/load_class_labels.py b/src/deepstream/helpers/load_class_labels.py
index 1b347215..4e71d735 100644
--- a/src/deepstream/helpers/load_class_labels.py
+++ b/src/deepstream/helpers/load_class_labels.py
@@ -2,6 +2,7 @@
 
 PLANT_LABELS = "/workspace/configs/crop_and_weed_83_classes.txt"
 
+
 # Load class labels
 def load_class_labels() -> List[str]:
     try:
diff --git a/src/deepstream/helpers/meta_tensor_extractor.py b/src/deepstream/helpers/meta_tensor_extractor.py
index 486b58c9..1addc756 100644
--- a/src/deepstream/helpers/meta_tensor_extractor.py
+++ b/src/deepstream/helpers/meta_tensor_extractor.py
@@ -1,8 +1,10 @@
 import ctypes
-import numpy.typing as npt
+
 import numpy as np
+import numpy.typing as npt
 import pyds
 
+
 class TensorExtractor:
     def extract_logits(self, tensor_meta) -> npt.NDArray[np.float32]:
         """
@@ -13,10 +15,7 @@ def extract_logits(self, tensor_meta) -> npt.NDArray[np.float32]:
         dims = [layer.dims.d[i] for i in range(layer.dims.numDims)]
         numel = int(np.prod(dims))
 
-        ptr = ctypes.cast(
-            pyds.get_ptr(layer.buffer),
-            ctypes.POINTER(ctypes.c_float)
-        )
+        ptr = ctypes.cast(pyds.get_ptr(layer.buffer), ctypes.POINTER(ctypes.c_float))
         logits = np.ctypeslib.as_array(ptr, shape=(numel,))
 
         # Copy so we are not tied to DeepStream's memory lifetime
diff --git a/src/deepstream/helpers/plant_msg_meta_builder.py b/src/deepstream/helpers/plant_msg_meta_builder.py
index e8867686..433b7497 100644
--- a/src/deepstream/helpers/plant_msg_meta_builder.py
+++ b/src/deepstream/helpers/plant_msg_meta_builder.py
@@ -1,14 +1,12 @@
 import sys
-from pydantic import BaseModel
 
 import gi
-gi.require_version("Gst", "1.0")
-
-from gi.repository import Gst
 import pyds
+from pydantic import BaseModel
 
 from src.deepstream.helpers.softmax_topk_classifier import ClassificationPrediction
 
+
 class PlantEvent(BaseModel):
     frame_id: int
     plant_id: str
diff --git a/src/deepstream/helpers/remove_background.py b/src/deepstream/helpers/remove_background.py
index f5e08d36..992c3165 100644
--- a/src/deepstream/helpers/remove_background.py
+++ b/src/deepstream/helpers/remove_background.py
@@ -1,8 +1,9 @@
-import numpy as np
 import cv2
+import numpy as np
 
 MORPH_KERNEL = 34
 
+
 def remove_background(frame_bgr: np.ndarray) -> np.ndarray:
     rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB).astype(np.float32)
     R, G, B = rgb[..., 0], rgb[..., 1], rgb[..., 2]
@@ -12,10 +13,7 @@ def remove_background(frame_bgr: np.ndarray) -> np.ndarray:
 
     _, mask = cv2.threshold(exg_norm, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
 
-    kernel = cv2.getStructuringElement(
-        cv2.MORPH_ELLIPSE,
-        (MORPH_KERNEL, MORPH_KERNEL)
-    )
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (MORPH_KERNEL, MORPH_KERNEL))
     mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
 
     out = np.zeros_like(frame_bgr)
diff --git a/src/deepstream/helpers/should_skip_frame.py b/src/deepstream/helpers/should_skip_frame.py
index d22900ac..38f459a7 100644
--- a/src/deepstream/helpers/should_skip_frame.py
+++ b/src/deepstream/helpers/should_skip_frame.py
@@ -1,19 +1,22 @@
-from typing import Any
 from enum import Enum
+from typing import Any
+
+import cv2
 import gi
 import numpy as np
 import pyds
-import cv2
 
 gi.require_version("Gst", "1.0")
 from gi.repository import Gst
 
 from src.frame_comparison.frame_change_detector import FrameChangeDetector
 
+
 class FrameProcessDecision(str, Enum):
     PROCESS = "process"
     SKIP = "skip"
 
+
 def should_skip_frame(info: Any, frame_meta: Any, batch_meta: Any, frame_change_detector: FrameChangeDetector) -> int:
     """Pad probe to drop frames based on frame difference analysis."""
     gst_buffer = info.get_buffer()
@@ -25,12 +28,8 @@ def should_skip_frame(info: Any, frame_meta: Any, batch_meta: Any, frame_change_
     frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGBA2BGR)
 
     should_process, metrics = frame_change_detector.should_process(frame_bgr)
-    
-    decision = (
-        FrameProcessDecision.PROCESS
-        if should_process
-        else FrameProcessDecision.SKIP
-    )
+
+    decision = FrameProcessDecision.PROCESS if should_process else FrameProcessDecision.SKIP
 
     print(
         f"Frame {frame_meta.frame_num:05d}: {decision.value} "
diff --git a/src/deepstream/helpers/softmax_topk_classifier.py b/src/deepstream/helpers/softmax_topk_classifier.py
index cd10e149..20f47f54 100644
--- a/src/deepstream/helpers/softmax_topk_classifier.py
+++ b/src/deepstream/helpers/softmax_topk_classifier.py
@@ -1,5 +1,6 @@
-import numpy as np
 from typing import List
+
+import numpy as np
 from pydantic import BaseModel
 
 
@@ -19,7 +20,7 @@ def predict_from_logits(self, logits: np.ndarray) -> List[ClassificationPredicti
         exp = np.exp(logits - np.max(logits))
         probs = exp / np.sum(exp)
 
-        top_idx = np.argsort(probs)[-self.top_k:][::-1]
+        top_idx = np.argsort(probs)[-self.top_k :][::-1]
 
         results: List[ClassificationPrediction] = []
         for idx in top_idx:
diff --git a/src/deepstream/pipelines/access_metadata.py b/src/deepstream/pipelines/access_metadata.py
index fe70d0f8..4a800c90 100644
--- a/src/deepstream/pipelines/access_metadata.py
+++ b/src/deepstream/pipelines/access_metadata.py
@@ -10,7 +10,7 @@
 import os
 
 import pyds
-from gi.repository import GLib, Gst
+from gi.repository import Gst
 
 from src.deepstream.helpers.pipeline_runner import run_pipeline
 
@@ -22,6 +22,7 @@
 MESSAGE_TEMPLATE = "Frame processed: {}"
 os.makedirs(OUTPUT_DIR, exist_ok=True)
 
+
 def modify_metadata(frame_meta, batch_meta, message_template=MESSAGE_TEMPLATE):
     """
     Modify the metadata by adding user-defined metadata.
@@ -79,9 +80,7 @@ def read_metadata(frame_meta):
     class_id = label_info.result_class_id
     confidence = label_info.result_prob
 
-    print(
-        f"[Frame {frame_meta.frame_num}] Class ID: {class_id}, Confidence: {confidence:.4f}"
-    )
+    print(f"[Frame {frame_meta.frame_num}] Class ID: {class_id}, Confidence: {confidence:.4f}")
 
 
 def access_metadata(pad, info, u_data):
@@ -189,8 +188,8 @@ def on_pad_added_decode(src, pad):
 
     return pipeline
 
+
 if __name__ == "__main__":
     Gst.init(None)
     pipeline = build_pipeline()
     run_pipeline(pipeline)
-    
\ No newline at end of file
diff --git a/src/deepstream/pipelines/access_raw_frames_pipeline.py b/src/deepstream/pipelines/access_raw_frames_pipeline.py
index 5541debb..f033c27f 100644
--- a/src/deepstream/pipelines/access_raw_frames_pipeline.py
+++ b/src/deepstream/pipelines/access_raw_frames_pipeline.py
@@ -11,7 +11,7 @@
 import gi
 import numpy as np
 import pyds
-from gi.repository import GLib, Gst
+from gi.repository import Gst
 
 gi.require_version("Gst", "1.0")
 
@@ -36,6 +36,7 @@ def frames_manipulation(pad: Gst.Pad, info: Gst.PadProbeInfo) -> Gst.PadProbeRet
     np.copyto(np.array(surface, copy=False, order="C"), frame)
     return Gst.PadProbeReturn.OK
 
+
 def build_pipeline() -> Gst.Pipeline:
     pipeline: Gst.Pipeline = Gst.Pipeline.new("simple-pipeline")
 
@@ -71,9 +72,7 @@ def build_pipeline() -> Gst.Pipeline:
 
     rtspsrc.set_property("location", RTSP_URL)
     rtspsrc.set_property("latency", 200)
-    capsfilter.set_property(
-        "caps", Gst.Caps.from_string("video/x-raw(memory:NVMM), format=RGBA")
-    )
+    capsfilter.set_property("caps", Gst.Caps.from_string("video/x-raw(memory:NVMM), format=RGBA"))
     streammux.set_property("batch-size", 1)
     streammux.set_property("width", 640)
     streammux.set_property("height", 480)
@@ -121,6 +120,7 @@ def on_decode_pad_added(src: Gst.Element, new_pad: Gst.Pad) -> None:
     srcpad.add_probe(Gst.PadProbeType.BUFFER, frames_manipulation)
     return pipeline
 
+
 def main() -> None:
     os.makedirs(OUTPUT_DIR, exist_ok=True)
     Gst.init(None)
diff --git a/src/deepstream/pipelines/cpu_frames_skipping_pipeline.py b/src/deepstream/pipelines/cpu_frames_skipping_pipeline.py
index 08926515..0817c3ff 100644
--- a/src/deepstream/pipelines/cpu_frames_skipping_pipeline.py
+++ b/src/deepstream/pipelines/cpu_frames_skipping_pipeline.py
@@ -1,19 +1,14 @@
-import sys
 import os
-
 from typing import Any
 
 import gi
-import numpy as np
-import pyds
 
 gi.require_version("Gst", "1.0")
-import cv2
-from gi.repository import GLib, Gst
+from gi.repository import Gst
 
+from src.deepstream.helpers.pipeline_runner import run_pipeline
 from src.deepstream.probes.frame_comparison.cpu.frame_skipping_probe import frame_skip_probe
 from src.frame_comparison.frame_change_detector import FrameChangeDetector
-from src.deepstream.helpers.pipeline_runner import run_pipeline
 
 rtsp_port = os.environ.get("RTSP_PORT", "8554")
 RTSP_URL = f"rtsp://127.0.0.1:{rtsp_port}/test"
@@ -108,6 +103,7 @@ def on_pad_added_decode(src: Any, pad: Any) -> None:
 
     return pipeline
 
+
 if __name__ == "__main__":
     Gst.init(None)
     pipeline = build_pipeline()
diff --git a/src/deepstream/pipelines/deepstream_image_utils.py b/src/deepstream/pipelines/deepstream_image_utils.py
index 05a6ec1b..baad28ed 100644
--- a/src/deepstream/pipelines/deepstream_image_utils.py
+++ b/src/deepstream/pipelines/deepstream_image_utils.py
@@ -4,6 +4,7 @@
 from pathlib import Path
 
 from gi.repository import Gst
+
 from src.path_utils import ensure_clean_directory  # noqa: E402
 
 Gst.init(None)
diff --git a/src/deepstream/pipelines/deepstream_pipeline_background_removal_cpu.py b/src/deepstream/pipelines/deepstream_pipeline_background_removal_cpu.py
index 03bc8768..8e135705 100644
--- a/src/deepstream/pipelines/deepstream_pipeline_background_removal_cpu.py
+++ b/src/deepstream/pipelines/deepstream_pipeline_background_removal_cpu.py
@@ -1,20 +1,18 @@
-from typing import Optional
 import os
-from datetime import datetime
 
 import gi
-gi.require_version("Gst", "1.0")
-from gi.repository import GLib, Gst
 
+gi.require_version("Gst", "1.0")
 import cv2
 import numpy as np
 import pyds
+from gi.repository import GLib, Gst
 
 from src.deepstream.helpers.pipeline_runner import run_pipeline
 
 rtsp_port = os.environ.get("RTSP_PORT", "8554")
 RTSP_URL = f"rtsp://127.0.0.1:{rtsp_port}/test"
-OUTPUT_DIR = f"/workspace/output/frames/"
+OUTPUT_DIR = "/workspace/output/frames/"
 MORPH_KERNEL = 34
 STRICT_ERRORS = True
 
diff --git a/src/deepstream/pipelines/deepstream_pipeline_background_removal_gpu.py b/src/deepstream/pipelines/deepstream_pipeline_background_removal_gpu.py
index 29c3278a..7381be79 100644
--- a/src/deepstream/pipelines/deepstream_pipeline_background_removal_gpu.py
+++ b/src/deepstream/pipelines/deepstream_pipeline_background_removal_gpu.py
@@ -1,9 +1,8 @@
-import sys
 import os
-
-from typing import Optional
 from datetime import datetime
+
 import gi
+
 from src.deepstream.probes.background_removal.gpu import gpu_background_probe
 
 gi.require_version("Gst", "1.0")
@@ -19,6 +18,7 @@
 
 os.makedirs(OUTPUT_DIR, exist_ok=True)
 
+
 def _post_error_from_pad(pad: Gst.Pad, text: str, debug: str = "") -> None:
     """Post an error message to the GStreamer bus from a pad."""
     elem = pad.get_parent_element()
@@ -28,6 +28,7 @@ def _post_error_from_pad(pad: Gst.Pad, text: str, debug: str = "") -> None:
     msg = Gst.Message.new_error(elem, gerr, debug)
     elem.post_message(msg)
 
+
 def background_removal_gpu_probe(pad: Gst.Pad, info: Gst.PadProbeInfo) -> Gst.PadProbeReturn:
     """GPU pad probe that applies background removal to each frame."""
     gst_buffer = info.get_buffer()
@@ -40,7 +41,7 @@ def background_removal_gpu_probe(pad: Gst.Pad, info: Gst.PadProbeInfo) -> Gst.Pa
     try:
         buffer_ptr: int = hash(gst_buffer)
         batch_id: int = 0
-        
+
         success: bool = gpu_background_probe.remove_background_gpu_probe_pipeline(buffer_ptr, batch_id)
         if not success:
             _post_error_from_pad(pad, "GPU background removal failed")
@@ -56,6 +57,7 @@ def background_removal_gpu_probe(pad: Gst.Pad, info: Gst.PadProbeInfo) -> Gst.Pa
 
     return Gst.PadProbeReturn.OK
 
+
 def build_pipeline() -> Gst.Pipeline:
     """Build the GStreamer pipeline for RTSP → GPU background removal → output."""
     pipeline = Gst.Pipeline.new("rtsp-gpu-background-removal")
@@ -116,6 +118,7 @@ def on_pad_added_rtspsrc(src: Gst.Element, pad: Gst.Pad) -> None:
 
     return pipeline
 
+
 if __name__ == "__main__":
     Gst.init(None)
     pipeline = build_pipeline()
diff --git a/src/deepstream/pipelines/deepstream_pipeline_cpu.py b/src/deepstream/pipelines/deepstream_pipeline_cpu.py
index 9f4b15b0..6c7c6533 100644
--- a/src/deepstream/pipelines/deepstream_pipeline_cpu.py
+++ b/src/deepstream/pipelines/deepstream_pipeline_cpu.py
@@ -1,26 +1,21 @@
 import os
-from datetime import datetime
-from typing import Any, Dict, List
+from typing import Any
+
 import gi
-import numpy as np
-import cv2
-import pyds
 
 gi.require_version("Gst", "1.0")
-from gi.repository import GLib, Gst
-
-from src.frame_comparison.frame_change_detector import FrameChangeDetector
-from src.model_conversion.onnx_to_trt import build_engine_if_missing
+from gi.repository import Gst
 
 from src.deepstream.helpers.load_class_labels import load_class_labels
 from src.deepstream.helpers.meta_tensor_extractor import TensorExtractor
-from src.deepstream.helpers.softmax_topk_classifier import ClassificationPrediction, SoftmaxTopKClassifier
-from src.deepstream.helpers.plant_msg_meta_builder import NvdsPlantEventBuilder, PlantEvent
 from src.deepstream.helpers.pipeline_runner import run_pipeline
-
+from src.deepstream.helpers.plant_msg_meta_builder import NvdsPlantEventBuilder
+from src.deepstream.helpers.softmax_topk_classifier import SoftmaxTopKClassifier
 from src.deepstream.probes.background_removal.cpu.background_removal_probe import remove_background_probe
 from src.deepstream.probes.db_message_meta_probe import DbMessageMetaProbe
 from src.deepstream.probes.frame_comparison.cpu.frame_skipping_probe import frame_skip_probe
+from src.frame_comparison.frame_change_detector import FrameChangeDetector
+from src.model_conversion.onnx_to_trt import build_engine_if_missing
 
 # Configuration
 RTSP_PORT = os.environ.get("RTSP_PORT", "8554")
@@ -33,6 +28,7 @@
 
 CLASS_LABELS = load_class_labels()
 
+
 def build_pipeline() -> Gst.Pipeline:
     """Build DeepStream pipeline with background removal, frame skipping, and message broker."""
     pipeline = Gst.Pipeline.new("final-cpu-pipeline")
@@ -127,6 +123,7 @@ def on_pad_added_decode(src: Any, pad: Any) -> None:
 
     return pipeline
 
+
 if __name__ == "__main__":
     Gst.init(None)
     build_engine_if_missing(CONFIG_FILE)
diff --git a/src/deepstream/pipelines/deepstream_pipeline_cpu_gpu.py b/src/deepstream/pipelines/deepstream_pipeline_cpu_gpu.py
index 14dded4f..189570cf 100644
--- a/src/deepstream/pipelines/deepstream_pipeline_cpu_gpu.py
+++ b/src/deepstream/pipelines/deepstream_pipeline_cpu_gpu.py
@@ -1,26 +1,23 @@
 import os
-from datetime import datetime
-from typing import Any, Dict, List
+from typing import Any, Dict
+
 import gi
-import numpy as np
-import cv2
-import pyds
 
 gi.require_version("Gst", "1.0")
-from gi.repository import GLib, Gst
-
-from src.frame_comparison.frame_change_detector import FrameChangeDetector
-from src.model_conversion.onnx_to_trt import build_engine_if_missing
+from gi.repository import Gst
 
 from src.deepstream.helpers.load_class_labels import load_class_labels
 from src.deepstream.helpers.meta_tensor_extractor import TensorExtractor
-from src.deepstream.helpers.softmax_topk_classifier import ClassificationPrediction, SoftmaxTopKClassifier
-from src.deepstream.helpers.plant_msg_meta_builder import NvdsPlantEventBuilder, PlantEvent
 from src.deepstream.helpers.pipeline_runner import run_pipeline
-
+from src.deepstream.helpers.plant_msg_meta_builder import NvdsPlantEventBuilder
+from src.deepstream.helpers.softmax_topk_classifier import SoftmaxTopKClassifier
 from src.deepstream.probes.background_removal.cpu.background_removal_probe import remove_background_probe
 from src.deepstream.probes.db_message_meta_probe import DbMessageMetaProbe
-from src.deepstream.probes.frame_comparison.gpu.frame_skipping_probe import frame_skipping_probe, GPUFrameChangeDetector
+from src.deepstream.probes.frame_comparison.gpu.frame_skipping_probe import (
+    GPUFrameChangeDetector,
+    frame_skipping_probe,
+)
+from src.model_conversion.onnx_to_trt import build_engine_if_missing
 
 # Configuration
 RTSP_PORT = os.environ.get("RTSP_PORT", "8554")
@@ -35,19 +32,21 @@
 
 stats: Dict[str, int] = {"total": 0, "skipped": 0, "processed": 0}
 
+
 def gpu_frame_skip_probe(pad: Gst.Pad, info: Gst.PadProbeInfo, detector: GPUFrameChangeDetector) -> Gst.PadProbeReturn:
     buffer_ptr: int = hash(info.get_buffer())
     batch_id: int = 0
     should_process: bool = frame_skipping_probe(buffer_ptr, batch_id, detector)
     if should_process:
-            stats["processed"] += 1
-            print(f"✅ PROCESSING frame {stats['total']}")
-            return Gst.PadProbeReturn.OK
+        stats["processed"] += 1
+        print(f"✅ PROCESSING frame {stats['total']}")
+        return Gst.PadProbeReturn.OK
     else:
         stats["skipped"] += 1
         print(f"⏭️ SKIPPING frame {stats['total']}")
         return Gst.PadProbeReturn.DROP
 
+
 def build_pipeline() -> Gst.Pipeline:
     """Build DeepStream pipeline with background removal, frame skipping, and message broker."""
     pipeline = Gst.Pipeline.new("final-cpu-pipeline")
@@ -142,6 +141,7 @@ def on_pad_added_decode(src: Any, pad: Any) -> None:
 
     return pipeline
 
+
 if __name__ == "__main__":
     Gst.init(None)
     build_engine_if_missing(CONFIG_FILE)
diff --git a/src/deepstream/pipelines/deepstream_pipeline_gpu.py b/src/deepstream/pipelines/deepstream_pipeline_gpu.py
index 0c37da05..463fe6f5 100644
--- a/src/deepstream/pipelines/deepstream_pipeline_gpu.py
+++ b/src/deepstream/pipelines/deepstream_pipeline_gpu.py
@@ -1,26 +1,23 @@
 import os
-from datetime import datetime
-from typing import Any, Dict, List
+from typing import Any, Dict
+
 import gi
-import numpy as np
-import cv2
-import pyds
 
 gi.require_version("Gst", "1.0")
 from gi.repository import GLib, Gst
 
-from src.frame_comparison.frame_change_detector import FrameChangeDetector
-from src.model_conversion.onnx_to_trt import build_engine_if_missing
-
 from src.deepstream.helpers.load_class_labels import load_class_labels
 from src.deepstream.helpers.meta_tensor_extractor import TensorExtractor
-from src.deepstream.helpers.softmax_topk_classifier import ClassificationPrediction, SoftmaxTopKClassifier
-from src.deepstream.helpers.plant_msg_meta_builder import NvdsPlantEventBuilder, PlantEvent
 from src.deepstream.helpers.pipeline_runner import run_pipeline
-
+from src.deepstream.helpers.plant_msg_meta_builder import NvdsPlantEventBuilder
+from src.deepstream.helpers.softmax_topk_classifier import SoftmaxTopKClassifier
 from src.deepstream.probes.background_removal.gpu import gpu_background_probe
 from src.deepstream.probes.db_message_meta_probe import DbMessageMetaProbe
-from src.deepstream.probes.frame_comparison.gpu.frame_skipping_probe import frame_skipping_probe, GPUFrameChangeDetector
+from src.deepstream.probes.frame_comparison.gpu.frame_skipping_probe import (
+    GPUFrameChangeDetector,
+    frame_skipping_probe,
+)
+from src.model_conversion.onnx_to_trt import build_engine_if_missing
 
 # Configuration
 RTSP_PORT = os.environ.get("RTSP_PORT", "8554")
@@ -35,19 +32,21 @@
 
 stats: Dict[str, int] = {"total": 0, "skipped": 0, "processed": 0}
 
+
 def gpu_frame_skip_probe(pad: Gst.Pad, info: Gst.PadProbeInfo, detector: GPUFrameChangeDetector) -> Gst.PadProbeReturn:
     buffer_ptr: int = hash(info.get_buffer())
     batch_id: int = 0
     should_process: bool = frame_skipping_probe(buffer_ptr, batch_id, detector)
     if should_process:
-            stats["processed"] += 1
-            print(f"✅ PROCESSING frame {stats['total']}")
-            return Gst.PadProbeReturn.OK
+        stats["processed"] += 1
+        print(f"✅ PROCESSING frame {stats['total']}")
+        return Gst.PadProbeReturn.OK
     else:
         stats["skipped"] += 1
         print(f"⏭️ SKIPPING frame {stats['total']}")
         return Gst.PadProbeReturn.DROP
 
+
 def _post_error_from_pad(pad: Gst.Pad, text: str, debug: str = "") -> None:
     """Post an error message to the GStreamer bus from a pad."""
     elem = pad.get_parent_element()
@@ -57,6 +56,7 @@ def _post_error_from_pad(pad: Gst.Pad, text: str, debug: str = "") -> None:
     msg = Gst.Message.new_error(elem, gerr, debug)
     elem.post_message(msg)
 
+
 def background_removal_gpu_probe(pad: Gst.Pad, info: Gst.PadProbeInfo) -> Gst.PadProbeReturn:
     """GPU pad probe that applies background removal to each frame."""
     gst_buffer = info.get_buffer()
@@ -69,7 +69,7 @@ def background_removal_gpu_probe(pad: Gst.Pad, info: Gst.PadProbeInfo) -> Gst.Pa
     try:
         buffer_ptr: int = hash(gst_buffer)
         batch_id: int = 0
-        
+
         success: bool = gpu_background_probe.remove_background_gpu_probe_pipeline(buffer_ptr, batch_id)
         if not success:
             _post_error_from_pad(pad, "GPU background removal failed")
@@ -85,6 +85,7 @@ def background_removal_gpu_probe(pad: Gst.Pad, info: Gst.PadProbeInfo) -> Gst.Pa
 
     return Gst.PadProbeReturn.OK
 
+
 def build_pipeline() -> Gst.Pipeline:
     """Build DeepStream pipeline with background removal, frame skipping, and message broker."""
     pipeline = Gst.Pipeline.new("final-cpu-pipeline")
@@ -179,6 +180,7 @@ def on_pad_added_decode(src: Any, pad: Any) -> None:
 
     return pipeline
 
+
 if __name__ == "__main__":
     Gst.init(None)
     build_engine_if_missing(CONFIG_FILE)
diff --git a/src/deepstream/pipelines/gpu_frames_skipping_pipeline.py b/src/deepstream/pipelines/gpu_frames_skipping_pipeline.py
index 19c731f0..32c684e1 100644
--- a/src/deepstream/pipelines/gpu_frames_skipping_pipeline.py
+++ b/src/deepstream/pipelines/gpu_frames_skipping_pipeline.py
@@ -1,14 +1,16 @@
 import os
+from typing import Dict
+
 import gi
-import cv2
-import pyds
-import ctypes
-from src.deepstream.probes.frame_comparison.gpu.frame_skipping_probe import frame_skipping_probe, GPUFrameChangeDetector
 
-from typing import Any, Dict, Optional
+from src.deepstream.probes.frame_comparison.gpu.frame_skipping_probe import (
+    GPUFrameChangeDetector,
+    frame_skipping_probe,
+)
 
 gi.require_version("Gst", "1.0")
-from gi.repository import GLib, Gst
+from gi.repository import Gst
+
 from src.deepstream.helpers.pipeline_runner import run_pipeline
 
 # Configurations
@@ -20,23 +22,25 @@
 
 stats: Dict[str, int] = {"total": 0, "skipped": 0, "processed": 0}
 
+
 def gpu_frame_skip_probe(pad: Gst.Pad, info: Gst.PadProbeInfo, detector: GPUFrameChangeDetector) -> Gst.PadProbeReturn:
     buffer_ptr: int = hash(info.get_buffer())
     batch_id: int = 0
     should_process: bool = frame_skipping_probe(buffer_ptr, batch_id, detector)
     if should_process:
-            stats["processed"] += 1
-            print(f"✅ PROCESSING frame {stats['total']}")
-            return Gst.PadProbeReturn.OK
+        stats["processed"] += 1
+        print(f"✅ PROCESSING frame {stats['total']}")
+        return Gst.PadProbeReturn.OK
     else:
         stats["skipped"] += 1
         print(f"⏭️ SKIPPING frame {stats['total']}")
         return Gst.PadProbeReturn.DROP
 
+
 def build_pipeline() -> Gst.Pipeline:
     """Build GPU-compatible pipeline"""
     pipeline: Gst.Pipeline = Gst.Pipeline.new("gpu-frame-skipping-pipeline")
-    
+
     # Elements
     rtspsrc: Gst.Element = Gst.ElementFactory.make("rtspsrc", "source")
     depay: Gst.Element = Gst.ElementFactory.make("rtph264depay", "depay")
@@ -51,11 +55,24 @@ def build_pipeline() -> Gst.Pipeline:
     nvvideoconvert2: Gst.Element = Gst.ElementFactory.make("nvvideoconvert", "nvvideoconvert2")
     jpegenc: Gst.Element = Gst.ElementFactory.make("jpegenc", "jpegenc")
     sink: Gst.Element = Gst.ElementFactory.make("multifilesink", "sink")
-    
-    for e in [rtspsrc, depay, parse, decode, convert, nvvideoconvert, 
-              capsfilter, streammux, nvinfer, nvosd, nvvideoconvert2, jpegenc, sink]:
+
+    for e in [
+        rtspsrc,
+        depay,
+        parse,
+        decode,
+        convert,
+        nvvideoconvert,
+        capsfilter,
+        streammux,
+        nvinfer,
+        nvosd,
+        nvvideoconvert2,
+        jpegenc,
+        sink,
+    ]:
         pipeline.add(e)
-    
+
     # Properties
     rtspsrc.set_property("location", RTSP_URL)
     streammux.set_property("batch-size", 1)
@@ -63,10 +80,10 @@ def build_pipeline() -> Gst.Pipeline:
     streammux.set_property("height", 480)
     nvinfer.set_property("config-file-path", CONFIG_FILE)
     sink.set_property("location", os.path.join(OUTPUT_DIR, "frame_%05d.jpg"))
-    
+
     caps: Gst.Caps = Gst.Caps.from_string("video/x-raw(memory:NVMM), format=RGBA")
     capsfilter.set_property("caps", caps)
-    
+
     # Links
     depay.link(parse)
     parse.link(decode)
@@ -77,33 +94,34 @@ def build_pipeline() -> Gst.Pipeline:
     nvosd.link(nvvideoconvert2)
     nvvideoconvert2.link(jpegenc)
     jpegenc.link(sink)
-    
+
     # Dynamic pads
     def on_pad_added_rtspsrc(src: Gst.Element, pad: Gst.Pad) -> None:
         sinkpad: Gst.Pad = depay.get_static_pad("sink")
         if not sinkpad.is_linked():
             pad.link(sinkpad)
-    
+
     def on_pad_added_decode(src: Gst.Element, pad: Gst.Pad) -> None:
         sinkpad: Gst.Pad = convert.get_static_pad("sink")
         if not sinkpad.is_linked():
             pad.link(sinkpad)
-    
+
     rtspsrc.connect("pad-added", on_pad_added_rtspsrc)
     decode.connect("pad-added", on_pad_added_decode)
-    
+
     srcpad: Gst.Pad = capsfilter.get_static_pad("src")
     sinkpad: Gst.Pad = streammux.get_request_pad("sink_0")
     srcpad.link(sinkpad)
-    
+
     # Add probe
     detector = GPUFrameChangeDetector()
 
     streammux_src_pad: Gst.Pad = streammux.get_static_pad("src")
     streammux_src_pad.add_probe(Gst.PadProbeType.BUFFER, gpu_frame_skip_probe, detector)
-    
+
     return pipeline
 
+
 if __name__ == "__main__":
     Gst.init(None)
     pipeline: Gst.Pipeline = build_pipeline()
@@ -111,7 +129,7 @@ def on_pad_added_decode(src: Gst.Element, pad: Gst.Pad) -> None:
 
     if stats["total"] > 0:
         skip_ratio: float = stats["skipped"] / stats["total"]
-        print(f"\n🔥 Final Stats:")
+        print("\n🔥 Final Stats:")
         print(f"   Total: {stats['total']}")
         print(f"   Processed: {stats['processed']}")
         print(f"   Skipped: {stats['skipped']}")
diff --git a/src/deepstream/pipelines/nvmsgbroker_pipeline.py b/src/deepstream/pipelines/nvmsgbroker_pipeline.py
index 72da2d9f..5b0bdd9f 100644
--- a/src/deepstream/pipelines/nvmsgbroker_pipeline.py
+++ b/src/deepstream/pipelines/nvmsgbroker_pipeline.py
@@ -4,7 +4,7 @@
 
 import gi
 import pyds
-from gi.repository import GLib, Gst
+from gi.repository import Gst
 from pydantic import BaseModel
 
 gi.require_version("Gst", "1.0")
@@ -12,9 +12,9 @@
 
 from src.deepstream.helpers.load_class_labels import load_class_labels
 from src.deepstream.helpers.meta_tensor_extractor import TensorExtractor
+from src.deepstream.helpers.pipeline_runner import run_pipeline
 from src.deepstream.helpers.softmax_topk_classifier import ClassificationPrediction, SoftmaxTopKClassifier
 from src.model_conversion.onnx_to_trt import build_engine_if_missing
-from src.deepstream.helpers.pipeline_runner import run_pipeline
 
 RTSP_PORT = os.environ.get("RTSP_PORT", "8554")
 RTSP_URL = f"rtsp://127.0.0.1:{RTSP_PORT}/test"
@@ -224,5 +224,6 @@ def main() -> None:
     pipeline = build_pipeline()
     run_pipeline(pipeline)
 
+
 if __name__ == "__main__":
     main()
diff --git a/src/deepstream/pipelines/pipeline_drop_frames.py b/src/deepstream/pipelines/pipeline_drop_frames.py
index c37b2e07..4b53e341 100644
--- a/src/deepstream/pipelines/pipeline_drop_frames.py
+++ b/src/deepstream/pipelines/pipeline_drop_frames.py
@@ -10,7 +10,7 @@
 import pyds
 
 gi.require_version("Gst", "1.0")
-from gi.repository import GLib, Gst  # noqa: E402
+from gi.repository import Gst  # noqa: E402
 
 from src.deepstream.helpers.pipeline_runner import run_pipeline
 
@@ -126,6 +126,7 @@ def on_pad_added_decode(src, pad):
 
     return pipeline
 
+
 if __name__ == "__main__":
     Gst.init(None)
     pipeline = build_pipeline()
diff --git a/src/deepstream/pipelines/pipeline_main.py b/src/deepstream/pipelines/pipeline_main.py
index 69a35ab8..0f12feed 100644
--- a/src/deepstream/pipelines/pipeline_main.py
+++ b/src/deepstream/pipelines/pipeline_main.py
@@ -2,7 +2,7 @@
 from datetime import datetime
 from pathlib import Path
 
-from gi.repository import GLib, Gst
+from gi.repository import Gst
 
 from src.deepstream.helpers.pipeline_runner import run_pipeline
 from src.model_conversion.onnx_to_trt import build_engine_if_missing
@@ -103,7 +103,7 @@ def main():
 
     print("🚀 Starting DeepStream End-to-End Pipeline!")
     print(f"📁 Output directory: {OUTPUT_DIR}")
-    print(f"📡 RTSP → Decode → Convert → Classification Model → OSD → Save")
+    print("📡 RTSP → Decode → Convert → Classification Model → OSD → Save")
 
     pipeline = build_pipeline()
 
@@ -113,7 +113,7 @@ def main():
     run_pipeline(pipeline)
 
     frame_count = len([f for f in OUTPUT_DIR.iterdir() if f.suffix == ".jpg"])
-    print(f"✅ Pipeline completed!")
+    print("✅ Pipeline completed!")
     print(f"📊 Generated {frame_count} frames")
     print(f"📁 Check results: ls -la {OUTPUT_DIR}")
 
diff --git a/src/deepstream/pipelines/pipeline_onnx_real_input.py b/src/deepstream/pipelines/pipeline_onnx_real_input.py
index e5b82ed9..060e7ac9 100644
--- a/src/deepstream/pipelines/pipeline_onnx_real_input.py
+++ b/src/deepstream/pipelines/pipeline_onnx_real_input.py
@@ -10,10 +10,10 @@
 
 from src.deepstream.helpers.load_class_labels import load_class_labels
 from src.deepstream.helpers.meta_tensor_extractor import TensorExtractor
+from src.deepstream.helpers.pipeline_runner import run_pipeline
 from src.deepstream.helpers.softmax_topk_classifier import (
     SoftmaxTopKClassifier,
 )
-from src.deepstream.helpers.pipeline_runner import run_pipeline
 
 
 def list_image_files(folder: str) -> List[str]:
diff --git a/src/deepstream/pipelines/pipeline_onnx_test.py b/src/deepstream/pipelines/pipeline_onnx_test.py
index c530df78..4c987fda 100755
--- a/src/deepstream/pipelines/pipeline_onnx_test.py
+++ b/src/deepstream/pipelines/pipeline_onnx_test.py
@@ -10,12 +10,12 @@
 
 from src.deepstream.helpers.load_class_labels import load_class_labels
 from src.deepstream.helpers.meta_tensor_extractor import TensorExtractor
+from src.deepstream.helpers.pipeline_runner import run_pipeline
 from src.deepstream.helpers.softmax_topk_classifier import (
     ClassificationPrediction,
     SoftmaxTopKClassifier,
 )
 from src.model_conversion.onnx_to_trt import build_engine_if_missing
-from src.deepstream.helpers.pipeline_runner import run_pipeline
 
 CLASS_LABELS = load_class_labels()
 
@@ -62,6 +62,7 @@ def buffer_probe(pad, info, user_data):
 
     return Gst.PadProbeReturn.OK
 
+
 def build_pipeline(config_file: str) -> Gst.Pipeline:
     pipeline = Gst.Pipeline()
 
@@ -137,6 +138,7 @@ def build_pipeline(config_file: str) -> Gst.Pipeline:
     infer_src_pad.add_probe(Gst.PadProbeType.BUFFER, buffer_probe, None)
     return pipeline
 
+
 def main(config_file: str) -> int:
     # Make sure TRT engine exists for this config
     build_engine_if_missing(config_file)
diff --git a/src/deepstream/probes/db_message_meta_probe.py b/src/deepstream/probes/db_message_meta_probe.py
index 0edf235a..8810b221 100644
--- a/src/deepstream/probes/db_message_meta_probe.py
+++ b/src/deepstream/probes/db_message_meta_probe.py
@@ -1,14 +1,20 @@
-from typing import Any, List
+from typing import List
+
 import pyds
 from gi.repository import Gst
 
 from src.deepstream.helpers.meta_tensor_extractor import TensorExtractor
-from src.deepstream.helpers.softmax_topk_classifier import ClassificationPrediction, SoftmaxTopKClassifier
 from src.deepstream.helpers.plant_msg_meta_builder import NvdsPlantEventBuilder, PlantEvent
+from src.deepstream.helpers.softmax_topk_classifier import ClassificationPrediction, SoftmaxTopKClassifier
 
 
 class DbMessageMetaProbe:
-    def __init__(self, tensor_extractor : TensorExtractor, classifier: SoftmaxTopKClassifier, plant_event_builder: NvdsPlantEventBuilder):
+    def __init__(
+        self,
+        tensor_extractor: TensorExtractor,
+        classifier: SoftmaxTopKClassifier,
+        plant_event_builder: NvdsPlantEventBuilder,
+    ):
         self.tensor_extractor = tensor_extractor
         self.classifier = classifier
         self.plant_event_builder = plant_event_builder
@@ -21,7 +27,9 @@ def extract_tensor_output(self, frame_meta) -> List[ClassificationPrediction]:
         logits = self.tensor_extractor.extract_logits(tensor_meta)
         return self.classifier.predict_from_logits(logits)
 
-    def attach_msgmeta(self, batch_meta, frame_meta, frame_number: int, predictions: List[ClassificationPrediction]) -> None:
+    def attach_msgmeta(
+        self, batch_meta, frame_meta, frame_number: int, predictions: List[ClassificationPrediction]
+    ) -> None:
         best = predictions[0]
         event = PlantEvent(
             frame_id=frame_number,
diff --git a/src/deepstream/probes/frame_comparison/cpu/frame_skipping_probe.py b/src/deepstream/probes/frame_comparison/cpu/frame_skipping_probe.py
index 8f769b7a..995dd314 100644
--- a/src/deepstream/probes/frame_comparison/cpu/frame_skipping_probe.py
+++ b/src/deepstream/probes/frame_comparison/cpu/frame_skipping_probe.py
@@ -1,14 +1,13 @@
 from typing import Any
+
 import gi
-import numpy as np
 import pyds
-import cv2
 
 gi.require_version("Gst", "1.0")
 from gi.repository import Gst
 
-from src.frame_comparison.frame_change_detector import FrameChangeDetector
 from src.deepstream.helpers.should_skip_frame import should_skip_frame
+from src.frame_comparison.frame_change_detector import FrameChangeDetector
 
 
 def frame_skip_probe(pad: Any, info: Any, frame_change_detector: FrameChangeDetector) -> int:
diff --git a/src/deepstream/speed_benchmark.py b/src/deepstream/speed_benchmark.py
index f94e44f1..f4d2b0dd 100644
--- a/src/deepstream/speed_benchmark.py
+++ b/src/deepstream/speed_benchmark.py
@@ -8,8 +8,8 @@
 import pandas as pd
 from pydantic import BaseModel
 
-from src.deepstream.pipelines.pipeline_main import build_pipeline
 from src.deepstream.helpers.pipeline_runner import run_pipeline
+from src.deepstream.pipelines.pipeline_main import build_pipeline
 
 gi.require_version("Gst", "1.0")
 from gi.repository import Gst
diff --git a/src/evaluation/base/evaluator.py b/src/evaluation/base/evaluator.py
index 4ba072ca..f070ac37 100644
--- a/src/evaluation/base/evaluator.py
+++ b/src/evaluation/base/evaluator.py
@@ -1,16 +1,14 @@
-import os
-import sys
 import time
 from dataclasses import dataclass
 from pathlib import Path
 from typing import List, Optional, Tuple
 
-
 import torch
 from PIL import Image
 
-from src.inference.base.classifier_inference_base import ClassifierInferenceBase
 from src.evaluation.metrics.metrics_api import ClassificationReport, compute_metrics
+from src.inference.base.classifier_inference_base import ClassifierInferenceBase
+
 
 @dataclass
 class EvaluationConfig:
@@ -53,8 +51,6 @@ def evaluate_model(self, model: ClassifierInferenceBase) -> ClassificationReport
             end_time = time.perf_counter()
             latencies.append(end_time - start_time)
             predictions.append(result[0]["class_id"])
-        report = compute_metrics(
-            y_true=true_labels, y_pred=predictions, latencies=latencies
-        )
+        report = compute_metrics(y_true=true_labels, y_pred=predictions, latencies=latencies)
         print("✅ Evaluation completed!")
         return report
diff --git a/src/evaluation/run_evaluation.py b/src/evaluation/run_evaluation.py
index a32c5e84..f1ddd976 100644
--- a/src/evaluation/run_evaluation.py
+++ b/src/evaluation/run_evaluation.py
@@ -1,20 +1,18 @@
-import os
-import sys
-import time
-import json
 import argparse
-import pandas as pd
-from dataclasses import dataclass
+import json
 from pathlib import Path
-from typing import List, Optional, Tuple
 
-from dataset.optimal_class_mapping import MODEL_NAMES as class_mapping, map_prediction
-from src.inference.utils.inference_factory import InferenceFactory, InferenceConfig, Backend, ModelArch
+import pandas as pd
+
+from dataset.optimal_class_mapping import MODEL_NAMES as class_mapping
+from dataset.optimal_class_mapping import map_prediction
 from src.evaluation.base.evaluator import EvaluationConfig, ModelEvaluator
+from src.inference.utils.inference_factory import Backend, InferenceConfig, InferenceFactory, ModelArch
 from src.path_utils import ensure_clean_directory
 
 MODELS_DIR_PATH = Path("models")
 
+
 class MappedModelWrapper:
     """Wrapper that adds mapping between 83 model classes to 76 dataset classes"""
 
@@ -33,6 +31,7 @@ def infer(self, image):
             }
         ]
 
+
 def create_model(model_name: str, model_type: str, device: str):
     weights_path = MODELS_DIR_PATH / model_type / f"{model_name}"
 
@@ -59,26 +58,24 @@ def create_model(model_name: str, model_type: str, device: str):
     base_model = InferenceFactory.create(config)
     return MappedModelWrapper(base_model)
 
+
 def parse_arguments():
-    parser = argparse.ArgumentParser(
-        description="Evaluate models on classification dataset"
-    )
+    parser = argparse.ArgumentParser(description="Evaluate models on classification dataset")
     parser.add_argument("--dataset", type=str, required=True)
     parser.add_argument("--models", type=str, default="resnet18,mobilenet")
-    parser.add_argument(
-        "--model_type", type=str, choices=["pytorch", "onnx"], required=True
-    )
+    parser.add_argument("--model_type", type=str, choices=["pytorch", "onnx"], required=True)
     parser.add_argument("--device", type=str, default="cpu")
     parser.add_argument("--output", type=str, default="outputs/evaluation_results.json")
     return parser.parse_args()
 
+
 def main():
     args = parse_arguments()
     models_to_eval = [m.strip() for m in args.models.split(",")]
     config = EvaluationConfig(dataset_path=Path(args.dataset), device=args.device)
     evaluator = ModelEvaluator(config)
     results = {}
-    
+
     print("📊 EVALUATION STARTING")
     print(f"Dataset: {args.dataset}, Models: {models_to_eval}, Device: {args.device}")
     print("=" * 50)
@@ -89,7 +86,7 @@ def main():
             report = evaluator.evaluate_model(model)
             result_key = f"{model_name}_{args.model_type}"
             results[result_key] = report.dict()
-            
+
             print(f"\n✅ {model_name.upper()} Results:")
             print(f"   Accuracy: {report.accuracy:.4f}")
             print(f"   Precision (Micro): {report.precision_micro:.4f}")
@@ -123,5 +120,6 @@ def main():
         print("=" * 80)
         print(df.to_string(index=False))
 
+
 if __name__ == "__main__":
     main()
diff --git a/src/evaluation/run_hierarchical_evaluation.py b/src/evaluation/run_hierarchical_evaluation.py
index f5b8e4b8..4a3c5f38 100644
--- a/src/evaluation/run_hierarchical_evaluation.py
+++ b/src/evaluation/run_hierarchical_evaluation.py
@@ -1,15 +1,15 @@
-import os
-import sys
 import json
 import time
 from pathlib import Path
 
+from metrics.metrics_api import compute_metrics
 from PIL import Image
+
+from dataset.optimal_class_mapping import MODEL_NAMES as class_mapping
+from dataset.optimal_class_mapping import map_prediction
 from dataset.utilities.datasets import DATASETS
-from metrics.metrics_api import compute_metrics
 from src.evaluation.base.evaluator import EvaluationConfig, ModelEvaluator
-from src.inference.utils.inference_factory import InferenceFactory, InferenceConfig, Backend, ModelArch
-from dataset.optimal_class_mapping import MODEL_NAMES as class_mapping, map_prediction
+from src.inference.utils.inference_factory import Backend, InferenceConfig, InferenceFactory, ModelArch
 
 
 class MappedModelWrapper:
@@ -29,7 +29,7 @@ def evaluate_all_hierarchies(y_true, y_pred, latencies):
     report = compute_metrics(y_true, y_pred, latencies)
     results["CropAndWeed"] = report.model_dump()
 
-    # Fine24, CropsOrWeed9, CropOrWeed2 
+    # Fine24, CropsOrWeed9, CropOrWeed2
     for name in ["Fine24", "CropsOrWeed9", "CropOrWeed2"]:
         try:
             dataset = DATASETS[name]
@@ -74,7 +74,7 @@ def main():
         device="cpu",
         class_mapping=class_mapping,
     )
-    
+
     resnet = InferenceFactory.create(resnet_config)
 
     wrapped_resnet = MappedModelWrapper(resnet)
@@ -120,7 +120,7 @@ def main():
     with open("src/evaluation/results/hierarchical_evaluation_results.json", "w") as f:
         json.dump(results, f, indent=2)
 
-    print("\n📊 תוצאות:")
+    print("\nresults:")
     for model, model_results in results.items():
         print(f"\n{model.upper()}:")
         for hierarchy, metrics in model_results.items():
diff --git a/src/frame_comparison/compare_frames_methods.py b/src/frame_comparison/compare_frames_methods.py
index 5b4d067a..1c5e1083 100644
--- a/src/frame_comparison/compare_frames_methods.py
+++ b/src/frame_comparison/compare_frames_methods.py
@@ -1,6 +1,7 @@
+import time
+
 import cv2
 import numpy as np
-import time
 from numpy.typing import NDArray
 
 
@@ -30,35 +31,27 @@ def simple_ssim(imgA: NDArray[np.uint8], imgB: NDArray[np.uint8]) -> float:
     C2 = (K2 * L) ** 2
 
     num = (2 * mu_x * mu_y + C1) * (2 * sigma_xy + C2)
-    den = (mu_x ** 2 + mu_y ** 2 + C1) * (sigma_x ** 2 + sigma_y ** 2 + C2)
+    den = (mu_x**2 + mu_y**2 + C1) * (sigma_x**2 + sigma_y**2 + C2)
     return float(num / den)
 
 
 def histogram_diff(imgA: NDArray[np.uint8], imgB: NDArray[np.uint8]) -> float:
-    histA = cv2.calcHist([imgA], [0, 1, 2], None, [8, 8, 8],
-                         [0, 256, 0, 256, 0, 256])
-    histB = cv2.calcHist([imgB], [0, 1, 2], None, [8, 8, 8],
-                         [0, 256, 0, 256, 0, 256])
+    histA = cv2.calcHist([imgA], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256])
+    histB = cv2.calcHist([imgB], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256])
     cv2.normalize(histA, histA)
     cv2.normalize(histB, histB)
     return float(cv2.compareHist(histA, histB, cv2.HISTCMP_CORREL))
 
 
-def optical_flow_diff(
-    imgA: NDArray[np.uint8],
-    imgB: NDArray[np.uint8]
-) -> float:
+def optical_flow_diff(imgA: NDArray[np.uint8], imgB: NDArray[np.uint8]) -> float:
     grayA = cv2.cvtColor(imgA, cv2.COLOR_BGR2GRAY)
     grayB = cv2.cvtColor(imgB, cv2.COLOR_BGR2GRAY)
-    flow = cv2.calcOpticalFlowFarneback(
-        grayA, grayB, None, 0.5, 3, 15, 3, 5, 1.2, 0
-    )
+    flow = cv2.calcOpticalFlowFarneback(grayA, grayB, None, 0.5, 3, 15, 3, 5, 1.2, 0)
     mag, _ = cv2.cartToPolar(flow[..., 0], flow[..., 1])
     return float(np.mean(mag))
 
 
-def motion_mask_diff(imgA: NDArray[np.uint8], imgB: NDArray[np.uint8],
-                     threshold: int = 25) -> float:
+def motion_mask_diff(imgA: NDArray[np.uint8], imgB: NDArray[np.uint8], threshold: int = 25) -> float:
     grayA = cv2.cvtColor(imgA, cv2.COLOR_BGR2GRAY)
     grayB = cv2.cvtColor(imgB, cv2.COLOR_BGR2GRAY)
     diff = cv2.absdiff(grayA, grayB)
@@ -67,8 +60,7 @@ def motion_mask_diff(imgA: NDArray[np.uint8], imgB: NDArray[np.uint8],
     return float(motion_ratio)
 
 
-def orb_feature_diff(imgA: NDArray[np.uint8], imgB: NDArray[np.uint8],
-                     max_features: int = 500) -> float:
+def orb_feature_diff(imgA: NDArray[np.uint8], imgB: NDArray[np.uint8], max_features: int = 500) -> float:
     orb = cv2.ORB_create(nfeatures=max_features)
     kp1, des1 = orb.detectAndCompute(imgA, None)
     kp2, des2 = orb.detectAndCompute(imgB, None)
@@ -83,9 +75,14 @@ def orb_feature_diff(imgA: NDArray[np.uint8], imgB: NDArray[np.uint8],
     return float(match_ratio)
 
 
-def should_skip_inference(mse_val: float, ssim_val: float, flow_val: float,
-                          mse_thresh: float = 50, ssim_thresh: float = 0.995,
-                          flow_thresh: float = 0.1) -> bool:
+def should_skip_inference(
+    mse_val: float,
+    ssim_val: float,
+    flow_val: float,
+    mse_thresh: float = 50,
+    ssim_thresh: float = 0.995,
+    flow_thresh: float = 0.1,
+) -> bool:
     return mse_val < mse_thresh and ssim_val > ssim_thresh and flow_val < flow_thresh
 
 
diff --git a/src/frame_comparison/create_sample_videos.py b/src/frame_comparison/create_sample_videos.py
index 561564e6..47759f7a 100644
--- a/src/frame_comparison/create_sample_videos.py
+++ b/src/frame_comparison/create_sample_videos.py
@@ -1,9 +1,9 @@
-import cv2
 import os
+
+import cv2
 import numpy as np
 from numpy.typing import NDArray
 
-
 OUTPUT_DIR = "assets/videos/frame_change_videos/"
 os.makedirs(OUTPUT_DIR, exist_ok=True)
 
@@ -58,7 +58,7 @@ def save_video(frames: list[NDArray[np.uint8]], filename: str, fps: int = 2) ->
         return
     h, w, _ = frames[0].shape
     path = os.path.join(OUTPUT_DIR, filename)
-    writer = cv2.VideoWriter(path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
+    writer = cv2.VideoWriter(path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
     for f in frames:
         writer.write(f)
     writer.release()
diff --git a/src/frame_comparison/cv_frame_skip_debug.py b/src/frame_comparison/cv_frame_skip_debug.py
index faafbbc3..810ffe1f 100644
--- a/src/frame_comparison/cv_frame_skip_debug.py
+++ b/src/frame_comparison/cv_frame_skip_debug.py
@@ -1,10 +1,12 @@
-import cv2
 import os
+
+import cv2
 from frame_change_detector import FrameChangeDetector
 
 VIDEO_DIR = "/workspace/assets/videos/frame_change_videos"
 VIDEO_EXTENSIONS = (".mp4", ".avi", ".mov", ".mkv")
 
+
 def analyze_video_skip_decisions(video_path: str) -> None:
     cap = cv2.VideoCapture(video_path)
     if not cap.isOpened():
diff --git a/src/inference/base/classifier_inference_base.py b/src/inference/base/classifier_inference_base.py
index 4176d284..56de908e 100644
--- a/src/inference/base/classifier_inference_base.py
+++ b/src/inference/base/classifier_inference_base.py
@@ -9,6 +9,7 @@
 from torch import Tensor
 from torchvision import transforms
 
+
 class ClassifierInferenceBase(ABC):
     """
     Abstract base for image classification inference.
@@ -24,14 +25,10 @@ def __init__(
         transform: Optional[transforms.Compose] = None,
     ) -> None:
         self.device = torch.device(device)
-        self.weights_path = (
-            Path(str(weights_path)) if weights_path is not None else None
-        )
+        self.weights_path = Path(str(weights_path)) if weights_path is not None else None
         self.class_mapping = class_mapping
         self.topk = max(1, int(topk))
-        self._transform = (
-            transform if transform is not None else self._build_preprocess()
-        )
+        self._transform = transform if transform is not None else self._build_preprocess()
         self._initialize_model()
 
     @abstractmethod
@@ -45,7 +42,7 @@ def _forward(self, x: Tensor) -> Tensor:
         raise NotImplementedError
 
     def _build_preprocess(self) -> transforms.Compose:
-        """Default preprocessing - תואם לאימון המודל"""
+        """Default preprocessing for the model."""
         return transforms.Compose(
             [
                 transforms.Resize((256, 256)),
diff --git a/src/inference/base/classifier_inference_base_onnx.py b/src/inference/base/classifier_inference_base_onnx.py
index 1c635cfd..bd08fcad 100644
--- a/src/inference/base/classifier_inference_base_onnx.py
+++ b/src/inference/base/classifier_inference_base_onnx.py
@@ -1,9 +1,6 @@
-import os
-import sys
 from pathlib import Path
 from typing import Dict, Optional, Union
 
-
 import numpy as np
 import onnxruntime as ort
 import torch
@@ -69,7 +66,7 @@ def _initialize_model(self) -> None:
             so.graph_optimization_level = level_map.get(
                 self._opt_level.upper(), ort.GraphOptimizationLevel.ORT_ENABLE_ALL
             )
-        
+
         so.add_session_config_entry("session.disable_mem_pattern", "1")
         so.add_session_config_entry("session.disable_mem_reuse", "1")
         so.graph_optimization_level = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
diff --git a/src/inference/pytorch/mobilenet_inference.py b/src/inference/pytorch/mobilenet_inference.py
index ef745901..57ade41d 100644
--- a/src/inference/pytorch/mobilenet_inference.py
+++ b/src/inference/pytorch/mobilenet_inference.py
@@ -1,5 +1,3 @@
-import os
-import sys
 from pathlib import Path
 from typing import Dict, Optional, Union
 
@@ -9,6 +7,7 @@
 
 from src.inference.base.classifier_inference_base import ClassifierInferenceBase
 
+
 class MobileNetInference(ClassifierInferenceBase):
     """MobileNetV2 classifier that strictly requires a checkpoint at `weights_path`."""
 
@@ -50,9 +49,7 @@ def _initialize_model(self) -> None:
         if isinstance(ckpt, dict):
             state_dict = ckpt.get("model_state_dict") or ckpt.get("state_dict") or ckpt
         else:
-            raise RuntimeError(
-                "Unrecognized checkpoint format (expected dict/state_dict)"
-            )
+            raise RuntimeError("Unrecognized checkpoint format (expected dict/state_dict)")
 
         if any(k.startswith("model.") for k in state_dict.keys()):
             state_dict = {k.replace("model.", "", 1): v for k, v in state_dict.items()}
diff --git a/src/inference/pytorch/resnet_inference.py b/src/inference/pytorch/resnet_inference.py
index 087c44e3..b61154b5 100644
--- a/src/inference/pytorch/resnet_inference.py
+++ b/src/inference/pytorch/resnet_inference.py
@@ -1,5 +1,3 @@
-import os
-import sys
 from pathlib import Path
 from typing import Dict, Optional, Union
 
@@ -49,9 +47,7 @@ def _initialize_model(self) -> None:
         if isinstance(ckpt, dict):
             state_dict = ckpt.get("model_state_dict") or ckpt.get("state_dict") or ckpt
         else:
-            raise RuntimeError(
-                "Unrecognized checkpoint format (expected dict/state_dict)"
-            )
+            raise RuntimeError("Unrecognized checkpoint format (expected dict/state_dict)")
 
         if any(k.startswith("model.") for k in state_dict.keys()):
             state_dict = {k.replace("model.", "", 1): v for k, v in state_dict.items()}
diff --git a/src/inference/utils/inference_factory.py b/src/inference/utils/inference_factory.py
index 4c397f21..f5b0c93c 100644
--- a/src/inference/utils/inference_factory.py
+++ b/src/inference/utils/inference_factory.py
@@ -1,11 +1,12 @@
 from enum import Enum
-from pydantic import BaseModel
 from pathlib import Path
 from typing import Optional, Type, Union
 
-from src.inference.pytorch.resnet_inference import ResNetInference
-from src.inference.pytorch.mobilenet_inference import MobileNetInference
+from pydantic import BaseModel
+
 from src.inference.base.classifier_inference_base_onnx import OnnxClassifierInferenceBase
+from src.inference.pytorch.mobilenet_inference import MobileNetInference
+from src.inference.pytorch.resnet_inference import ResNetInference
 
 
 class Backend(str, Enum):
diff --git a/src/inference/utils/onnx_predict_images.py b/src/inference/utils/onnx_predict_images.py
index 9bda588d..66e73acf 100644
--- a/src/inference/utils/onnx_predict_images.py
+++ b/src/inference/utils/onnx_predict_images.py
@@ -1,11 +1,12 @@
 import argparse
 import os
 import pathlib
+
 import numpy as np
 import torch
 from PIL import Image
 
-from src.inference.utils.inference_factory import InferenceFactory, InferenceConfig, Backend
+from src.inference.utils.inference_factory import Backend, InferenceConfig, InferenceFactory
 
 IMAGE_SIZE = (256, 256)
 COLOR_MODE = "RGB"
diff --git a/src/inference/utils/run_inference_main.py b/src/inference/utils/run_inference_main.py
index b493ac60..4952cd9c 100644
--- a/src/inference/utils/run_inference_main.py
+++ b/src/inference/utils/run_inference_main.py
@@ -1,15 +1,14 @@
+import argparse
+import json
 import os
-import sys
 import time
-import json
-import argparse
 from pathlib import Path
 
-from PIL import Image
 import torch
+from PIL import Image
 
 from dataset.optimal_class_mapping import MODEL_NAMES as ID_TO_NAME
-from src.inference.utils.inference_factory import InferenceFactory, InferenceConfig, Backend, ModelArch
+from src.inference.utils.inference_factory import Backend, InferenceConfig, InferenceFactory, ModelArch
 
 
 # Main image processing logic
@@ -29,7 +28,6 @@ def run_inference(model_type: str, model_path: str, image_dir: str, device: str)
         class_mapping=class_mapping,
     )
 
-
     clf = InferenceFactory.create(config)
 
     results = {}
@@ -56,10 +54,7 @@ def run_inference(model_type: str, model_path: str, image_dir: str, device: str)
                 "inference_time_ms": inference_time,
             }
 
-            print(
-                f"  {predictions[0]['class_name']} "
-                f"({predictions[0]['probability']:.1%})"
-            )
+            print(f"  {predictions[0]['class_name']} " f"({predictions[0]['probability']:.1%})")
 
             total_time += inference_time
             image_count += 1
diff --git a/src/model_conversion/onnx_to_trt.py b/src/model_conversion/onnx_to_trt.py
index 643aa4b6..b5576f00 100644
--- a/src/model_conversion/onnx_to_trt.py
+++ b/src/model_conversion/onnx_to_trt.py
@@ -42,13 +42,7 @@ def build_engine_if_missing(config_path: str) -> str:
     print(f"[INFO] Engine file not found. Generating {engine_path} from {onnx_path} ...")
     os.makedirs(os.path.dirname(engine_path), exist_ok=True)
 
-    cmd = [
-    "trtexec",
-    f"--onnx={onnx_path}",
-    f"--saveEngine={engine_path}",
-    "--memPoolSize=workspace:2048",  
-    "--fp16" 
-]
+    cmd = ["trtexec", f"--onnx={onnx_path}", f"--saveEngine={engine_path}", "--memPoolSize=workspace:2048", "--fp16"]
     subprocess.run(cmd, check=True)
 
     # Verify created file
diff --git a/src/model_conversion/pytorch_to_onnx.py b/src/model_conversion/pytorch_to_onnx.py
index 1f83057d..106551f2 100644
--- a/src/model_conversion/pytorch_to_onnx.py
+++ b/src/model_conversion/pytorch_to_onnx.py
@@ -1,7 +1,6 @@
 #!/usr/bin/env python3
 import argparse
 from pathlib import Path
-import subprocess
 
 import torch
 
diff --git a/src/model_conversion/validate_onnx_vs_pytorch.py b/src/model_conversion/validate_onnx_vs_pytorch.py
index d2f044e3..b8397dff 100644
--- a/src/model_conversion/validate_onnx_vs_pytorch.py
+++ b/src/model_conversion/validate_onnx_vs_pytorch.py
@@ -9,9 +9,9 @@
 
 from dataset.optimal_class_mapping import MODEL_NAMES as ID_TO_NAME
 from src.inference.base.classifier_inference_base import ClassifierInferenceBase
+from src.inference.base.classifier_inference_base_onnx import OnnxClassifierInferenceBase
 from src.inference.pytorch.mobilenet_inference import MobileNetInference
 from src.inference.pytorch.resnet_inference import ResNetInference
-from src.inference.base.classifier_inference_base_onnx import OnnxClassifierInferenceBase
 
 NUM_CLASSES = 83
 INPUT_SIZE = (256, 256)
diff --git a/src/model_conversion/validate_trt_vs_onnx.py b/src/model_conversion/validate_trt_vs_onnx.py
index 2d7a3c5d..c5059c82 100644
--- a/src/model_conversion/validate_trt_vs_onnx.py
+++ b/src/model_conversion/validate_trt_vs_onnx.py
@@ -1,5 +1,4 @@
 import argparse
-import ctypes
 from pathlib import Path
 from threading import Timer
 from typing import Dict, Optional, Tuple
diff --git a/src/rtsp/rtsp_server.py b/src/rtsp/rtsp_server.py
index dbd63c95..103c792d 100644
--- a/src/rtsp/rtsp_server.py
+++ b/src/rtsp/rtsp_server.py
@@ -1,4 +1,5 @@
 import os
+
 import gi
 
 gi.require_version("Gst", "1.0")
@@ -7,6 +8,7 @@
 
 VIDEO_PATH = "/workspace/assets/videos/test_videos/video.mp4"
 
+
 def main():
     # Initialize GStreamer
     Gst.init(None)
@@ -38,7 +40,7 @@ def main():
     # Attach the server to the default main context and run the loop
     server.attach(None)
     print(f"RTSP server is up at rtsp://0.0.0.0:{port}/test")
-    
+
     loop = GObject.MainLoop()
     loop.run()