nit: fixed formatting

DummyModel.py

@@ -1,12 +1,17 @@
 import torch
+import os
 import torch.nn
+from huggingface_hub import hf_hub_download
 
 
 def load_dummy_model(DEBUG):
     model = DummyModel()
     if not DEBUG:
-        file_path = hf_hub_download("lfolle/DeepNAPSIModel", "dummy_model.pth",
-                                    use_auth_token=os.environ['DeepNAPSIModel'])
+        file_path = hf_hub_download(
+            "lfolle/DeepNAPSIModel",
+            "dummy_model.pth",
+            use_auth_token=os.environ["DeepNAPSIModel"],
+        )
         model.load_state_dict(torch.load(file_path))
     return model
 
@@ -15,8 +20,8 @@ class DummyModel(torch.nn.Module):
     def __init__(self):
         super().__init__()
 
-    def forward(self, x:list):
+    def forward(self, x: list):
         return torch.softmax(torch.rand(len(x), 5), 1), 0
 
-    def __call__(self, x:list):
+    def __call__(self, x: list):
         return self.forward(x)

Model.py

@@ -9,8 +9,14 @@ class Model:
             base = r"C:\Users\follels\Documents\hand-ki-model-weights\DeepNAPSIModel\inference_checkpoints_v1"
             file_paths = [os.path.join(base, f"version_{v}") for v in range(10, 15)]
         else:
-            file_paths = [hf_hub_download("lfolle/DeepNAPSIModel", f"version_{v}.ckpt",
-                                    use_auth_token=os.environ['DeepNAPSIModel']) for v in [10, 11, 12, 13, 14]]
+            file_paths = [
+                hf_hub_download(
+                    "lfolle/DeepNAPSIModel",
+                    f"version_{v}.ckpt",
+                    use_auth_token=os.environ["DeepNAPSIModel"],
+                )
+                for v in [10, 11, 12, 13, 14]
+            ]
         self.inference = Inference(file_paths)
 
     def predict(self, x):

app.py

@@ -1,7 +1,4 @@
-import os
-import pip
 import gradio as gr
-from PIL import Image
 
 from backend import Infer
 
@@ -9,7 +6,11 @@ from backend import Infer
 DEBUG = False
 
 infer = Infer(DEBUG)
-example_image_path = ["assets/example_1.jpg", "assets/example_2.jpg", "assets/example_3.jpg"]
+example_image_path = [
+    "assets/example_1.jpg",
+    "assets/example_2.jpg",
+    "assets/example_3.jpg",
+]
 
 outputs = [
     gr.Image(label="Thumb"),
@@ -28,16 +29,27 @@ outputs = [
 with gr.Blocks(analytics_enabled=False, title="DeepNAPSI") as demo:
     with gr.Column():
         gr.Markdown("## Welcome to the DeepNAPSI application!")
-        gr.Markdown("Upload an image of the one hand and click **Predict NAPSI** to see the output.")
-        gr.Markdown("*Note*: Make sure there are no identifying information present in the image. The prediction can take up to 4.5 minutes." )
-        gr.Markdown("*Note*: This is not a medical product and cannot be used for a patient diagnosis in any way.")
+        gr.Markdown(
+            "Upload an image of the one hand and click **Predict NAPSI** to see the output."
+        )
+        gr.Markdown(
+            "*Note*: Make sure there are no identifying information present in the image. The prediction can take up to 4.5 minutes."
+        )
+        gr.Markdown(
+            "*Note*: This is not a medical product and cannot be used for a patient diagnosis in any way."
+        )
         with gr.Column():
             with gr.Row():
                 with gr.Column():
                     with gr.Row():
                         image_input = gr.Image()
-                        example_images = gr.Examples(example_image_path, image_input, outputs,
-                                    fn=infer.predict, cache_examples=True)
+                        example_images = gr.Examples(
+                            example_image_path,
+                            image_input,
+                            outputs,
+                            fn=infer.predict,
+                            cache_examples=True,
+                        )
                     with gr.Row():
                         image_button = gr.Button("Predict NAPSI")
                     with gr.Row():
@@ -59,4 +71,8 @@ with gr.Blocks(analytics_enabled=False, title="DeepNAPSI") as demo:
                         outputs[10].render()
         image_button.click(infer.predict, inputs=image_input, outputs=outputs)
 
-demo.launch(share=True if DEBUG else False, enable_queue=True, favicon_path="assets/favicon-32x32.png")
+demo.launch(
+    share=True if DEBUG else False,
+    enable_queue=True,
+    favicon_path="assets/favicon-32x32.png",
+)

backend.py

@@ -1,14 +1,12 @@
 import torch
 import cv2
 import numpy as np
-from huggingface_hub import hf_hub_download
 from nail_detection.main import get_nails
 
-from DummyModel import load_dummy_model
 from Model import Model
 
 
-class Infer():
+class Infer:
     def __init__(self, DEBUG):
         # self.model = load_dummy_model(DEBUG)
         self.model = Model(DEBUG)
@@ -22,7 +20,7 @@ class Infer():
                 predictions.append(-1)
             predictions.append("-1")
         else:
-            model_prediction, uncertainty = self.model(nails)
+            model_prediction, _ = self.model(nails)
             model_prediction = model_prediction[0]
             napsi_predictions = torch.argmax(model_prediction, 1)
             napsi_sum = int(napsi_predictions.sum().detach().cpu())

entrypoint.py

@@ -1,14 +1,12 @@
 import os
 import sys
-import shutil
 import subprocess
 from huggingface_hub import Repository
 
-# Bug got fixed lately
-# hooks_path = ".git/hooks/"
-# if os.path.exists(hooks_path):
-#     shutil.rmtree(hooks_path)
-Repository("repos/hand-ki-model", f"https://oauth2:{os.getenv('HANDKIGIT5')}@git5.cs.fau.de/folle/hand-ki-model.git", use_auth_token=os.getenv(""))
+Repository(
+    "repos/hand-ki-model",
+    f"https://oauth2:{os.getenv('HANDKIGIT5')}@git5.cs.fau.de/folle/hand-ki-model.git",
+    use_auth_token=os.getenv(""),
+)
 
 subprocess.check_call([sys.executable, "-m", "pip", "install", "repos/hand-ki-model/"])
-import app