Update

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 from transformers import pipeline
 import torch
 
+
 # Check if CUDA is available
 device = 0 if torch.cuda.is_available() else -1
 
@@ -22,11 +23,14 @@ model_names = [
 # Cache for pipelines to avoid reloading models
 pipelines = {}
 
+
 def process(image_file, top_k):
     labels = []
     for m in model_names:
         if m not in pipelines:
-            pipelines[m] = pipeline("image-classification", model=m, device=device)
+            pipelines[m] = pipeline(
+                "image-classification", model=m, device=device
+            )
         p = pipelines[m]
         pred = p(image_file)
         labels.append({x["label"]: x["score"] for x in pred[:top_k]})
@@ -41,9 +45,10 @@ top_k = gr.Slider(minimum=1, maximum=5, step=1, value=5, label="Top k classes")
 labels = [gr.Label(label=m) for m in model_names]
 
 description = (
-    "This Space compares popular image classifiers available on the Hugging Face hub, "
-    "including NAT and DINAT models. All models have been fine-tuned on ImageNet-1k. "
-    "The sample images were generated with Stable Diffusion."
+    "This Space compares popular image classifiers available on the "
+    "Hugging Face hub, including NAT and DINAT models. All models have "
+    "been fine-tuned on ImageNet-1k. The sample images were generated "
+    "with Stable Diffusion."
 )
 
 iface = gr.Interface(

requirements.txt

@@ -1,6 +1,5 @@
 torch==2.7.0
 transformers==4.51.3
 gradio>=3.50.0
---no-build-isolation
 natten==0.17.5
 Pillow>=9.0.0