update

app.py

@@ -14,8 +14,6 @@ app = FastAPI(title="SmolVLM Inference API", version="1.2.0")
 _runner: Optional[SmolVLMRunner] = None
 
 
-# ----------------------- Pydantic models -----------------------
-
 class URLRequest(BaseModel):
     prompt: str = Field(..., description="Text prompt to accompany the images.")
     image_urls: List[HttpUrl] = Field(..., description="List of image URLs.")
@@ -34,9 +32,6 @@ class DetectDescribeURLRequest(BaseModel):
     temperature: Optional[float] = None
     top_p: Optional[float] = None
 
-
-# ----------------------- Startup / health -----------------------
-
 @app.on_event("startup")
 async def _load_model_on_startup():
     global _runner
@@ -46,9 +41,6 @@ async def _load_model_on_startup():
 def health():
     return {"status": "ok", "model": _runner.model_id if _runner else None}
 
-
-# ----------------------- Core VLM endpoints -----------------------
-
 @app.post("/generate")
 async def generate_from_files(
     prompt: str = Form(...),
@@ -122,9 +114,6 @@ async def generate_from_urls(req: URLRequest):
     }
     return {"text": text, "metrics": metrics}
 
-
-# ----------------------- Detect & Describe endpoints -----------------------
-
 @app.post("/detect_describe")
 async def detect_describe(
     image: UploadFile = File(..., description="One image file (image/*)"),
@@ -181,8 +170,6 @@ async def detect_describe_url(req: DetectDescribeURLRequest):
     return out
 
 
-# ----------------------- Entrypoint -----------------------
-
 if __name__ == "__main__":
     # Run with: python app.py  (or: uvicorn app:app --host 0.0.0.0 --port 8000)
     uvicorn.run("app:app", host="0.0.0.0", port=8000, reload=False)

grounding_dino2.py

@@ -11,7 +11,6 @@ import torch
 from PIL import Image, ImageDraw, ImageFont
 from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
 
-# ---- Writable caches (HF Spaces / containers) ----
 CACHE_DIR = os.getenv("HF_CACHE_DIR", "/tmp/hf-cache")
 Path(CACHE_DIR).mkdir(parents=True, exist_ok=True)
 os.environ.setdefault("HOME", "/tmp")
@@ -181,8 +180,6 @@ def visualize_detections(
         draw.text((x0 + pad, max(0, y0 - 18)), text, fill=text_color, font=font)
     return vis
 
-
-# convenience singleton
 _runner_singleton: GroundingDINORunner | None = None
 
 def get_runner() -> GroundingDINORunner:

ui.py

@@ -29,7 +29,6 @@ def show_metrics(metrics: dict):
 
 tab_upload, tab_detect = st.tabs(["SmolVLM Detection", "Grounded Detection"])
 
-# -------------------- Tab 1: uploads -> /generate --------------------
 with tab_upload:
     st.subheader("Upload an image")
     files = st.file_uploader("Images", type=["png", "jpg", "jpeg", "webp"], accept_multiple_files=True)
@@ -84,7 +83,6 @@ with tab_upload:
                         except Exception:
                             st.write(e.response.text)
 
-# -------------------- Tab 2: Detect & Describe -> /detect_describe --------------------
 with tab_detect:
     st.subheader("SmolVLM Grounded Detection")
 

util.py

@@ -1,6 +1,6 @@
 
 
-# util.py (patched cache handling for HF Spaces)
+# util.py
 import os
 from pathlib import Path
 
@@ -15,7 +15,6 @@ os.environ.setdefault("HUGGINGFACE_HUB_CACHE", CACHE_DIR)
 os.environ.setdefault("XDG_CACHE_HOME", CACHE_DIR)
 os.environ.setdefault("TORCH_HOME", CACHE_DIR)
 
-# util.py (Spaces-safe + metrics)
 
 from time import perf_counter
 import threading
@@ -285,8 +284,6 @@ class SmolVLMRunner:
 
         return (text, metrics) if return_stats else text
 
-
-# Convenience singleton
 _runner_singleton = None
 def get_runner():
     global _runner_singleton