modifying Slider values

app.py

@@ -10,11 +10,11 @@ REPO_ID = "c1tr0n75/VoxelPathFinder"
 # 1) Make sure torch is imported, then define device BEFORE using it anywhere
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-# 2) Download model code and weights from your model repo
+# 2) Download model code and weights from the model repo
 PY_PATH = hf_hub_download(repo_id=REPO_ID, filename="pathfinding_nn.py")
 CKPT_PATH = hf_hub_download(repo_id=REPO_ID, filename="training_outputs/final_model.pth")
 
-# 3) Dynamically import your model definitions
+# 3) Dynamically import the model definitions
 spec = importlib.util.spec_from_file_location("pathfinding_nn", PY_PATH)
 mod = importlib.util.module_from_spec(spec)
 spec.loader.exec_module(mod)
@@ -76,7 +76,7 @@ def infer_random(obstacle_prob=0.2, seed=None):
 with gr.Blocks(title="Voxel Path Finder") as demo:
     gr.Markdown("## 3D Voxel Path Finder — Inference")
     with gr.Tab("Random environment"):
-        obstacle = gr.Slider(0.0, 0.9, value=0.2, step=0.05, label="Obstacle probability")
+        obstacle = gr.Slider(0.0, 1.0, value=0.2, step=0.05, label="Obstacle probability")
         seed = gr.Number(value=None, label="Seed (optional)")
         btn = gr.Button("Run inference")
         out = gr.JSON(label="Result")