Prevent compile

app.py

@@ -1,11 +1,16 @@
-import torch, random, re
+import random
+import re
+
+import albumentations as A
+import gradio as gr
 import numpy as np
+import torch
 from PIL import Image, ImageDraw
 from datasets import load_dataset
-from transformers import AutoProcessor, Gemma3ForConditionalGeneration
-import albumentations as A
-import gradio as gr
+from gradio.themes.soft import Soft
 from spaces import GPU
+from transformers import AutoProcessor, Gemma3ForConditionalGeneration
+
 
 # --- Config ---
 dataset_id = "ariG23498/license-detection-paligemma"
@@ -27,7 +32,7 @@ def load_model(checkpoint_id):
         processor = AutoProcessor.from_pretrained(checkpoint_id)
         model = Gemma3ForConditionalGeneration.from_pretrained(
             checkpoint_id,
-            torch_dtype=torch.bfloat16 if model_id == "google/gemma-3-4b-pt" else "auto",
+            torch_dtype="auto",
             device_map="auto",
         )
         model.eval()
@@ -74,7 +79,7 @@ def detect_random_image(model_choice):
     inputs = {k: v.to(model.device) if torch.is_tensor(v) else v for k, v in inputs.items()}
 
     with torch.no_grad():
-        generation = model.generate(**inputs, max_new_tokens=100)
+        generation = model.generate(**inputs, max_new_tokens=100, disable_compile=True)
 
     decoded = processor.batch_decode(generation, skip_special_tokens=True)[0]
     return visualize_bounding_boxes(image_resized, decoded)
@@ -86,15 +91,14 @@ button#gradio-share-link-button-0 {
 """
 
 # --- Gradio Blocks Interface ---
-with gr.Blocks(css=css_hide_share) as demo:
+with gr.Blocks(theme=Soft(), css=css_hide_share) as demo:
+
     gr.Markdown("# Gemma3 Object Detector")
     gr.Markdown("""
 ### 🔍 About the Models
 This demo compares two fine-tuned versions of **Gemma 3 (4B)** for object detection:
-
 - **🔵 Fine-tuned for object detection**: trained to predict bounding boxes and class labels using the original tokenizer.
 - **🟣 Fine-tuned (updated tokenizer with `<loc>` tokens)**: same task, but uses a tokenizer updated to better encode spatial information through `<locYYYY>` tokens.
-
 Select a model and click **Generate** to visualize its prediction on a random test image.
 """)
 
@@ -110,4 +114,4 @@ Select a model and click **Generate** to visualize its prediction on a random te
     generate_btn.click(fn=detect_random_image, inputs=model_selector, outputs=output_image)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()