changed deps

README.md

@@ -37,8 +37,8 @@ Weights:
 - If the repos are public on HF LFS, no additional setup is required on Spaces; downloads are automatic and reused on subsequent runs.
 
 ## ZeroGPU setup (Hugging Face Spaces)
-- In the Space settings, choose the **ZeroGPU** hardware tier and Python **3.10** (supported runtimes for ZeroGPU).
-- The `run_prediction` handler is decorated with `@spaces.GPU(duration=120)`, so a GPU is provisioned only while a prediction runs.
+- In the Space settings, choose the **ZeroGPU** hardware tier and Python **3.10** or **3.12** (both supported on ZeroGPU).
+- The GPU workload is isolated in `_gpu_predict`, decorated with `@spaces.GPU(duration=120)`, so a GPU is provisioned only for the inference step.
 - Queueing is enabled with a single worker (`demo.queue(concurrency_count=1)`) to match the one-at-a-time ZeroGPU execution model.
 - To avoid spending GPU time on downloads, click **Download / refresh models** once after each deployment to prefetch weights on CPU.
-- If a prediction might exceed 120 seconds, bump the `duration` parameter in `app.py` to keep the GPU session alive long enough.
+- If inference ever needs more time, adjust the `duration` parameter in `app.py`, but keep it as low as practical to respect ZeroGPU queue fairness.

app.py

@@ -82,13 +82,9 @@ def parse_smiles(smiles_text: str) -> List[str]:
     return smiles
 
 
-@spaces.GPU(duration=240)
-def run_prediction(smiles_text: str, assay_text: str) -> Tuple[pd.DataFrame, object, str, str]:
-    assay = assay_text.strip()
-    if not assay:
-        raise gr.Error("Please provide a bioassay description.")
-
-    smiles = parse_smiles(smiles_text)
+@spaces.GPU(duration=120)
+def _gpu_predict(smiles: List[str], assay: str) -> Tuple[pd.DataFrame, object, str, str]:
+    """GPU-only path: expects prevalidated inputs and ready models."""
     model = get_model()
 
     try:
@@ -132,6 +128,19 @@ def run_prediction(smiles_text: str, assay_text: str) -> Tuple[pd.DataFrame, obj
     return df, fig, csv_file.name, xlsx_file.name
 
 
+def run_prediction(smiles_text: str, assay_text: str) -> Tuple[pd.DataFrame, object, str, str]:
+    """CPU wrapper: validates inputs and prepares models before GPU allocation."""
+    assay = assay_text.strip()
+    if not assay:
+        raise gr.Error("Please provide a bioassay description.")
+
+    smiles = parse_smiles(smiles_text)
+    # Download/refresh weights on CPU to keep ZeroGPU sessions short
+    ensure_models()
+
+    return _gpu_predict(smiles, assay)
+
+
 def build_demo() -> gr.Blocks:
     example_smiles = "CC1=CC=C(C=C1)C2=CC(=NC3=NC=NC(=C23)N)C4=CC=C(C=C4)F\nCC(=O)C1=CC=C(C=C1)NC(=O)C2=CC3=C(C=C2)N=C(C(=N3)C4=CC=CO4)C5=CC=CO5\nCC1=C(C=C(C=C1)Cl)NC2=C/C(=N\S(=O)(=O)C3=CC=CS3)/C4=CC=CC=C4C2=O\nCC(C)C1=NC2=CC=CC=C2C(=N1)SCC(=O)N3CCCC3 "
     example_assay = "TR-FRET counterscreen for FAK inhibitors: dose-response biochemical high throughput screening assay to identify inhibitors of Proline-rich tyrosine kinase 2 (Pyk2)"

requirements.txt

@@ -1,4 +1,7 @@
-twinbooster==0.3.1
+--ignore-requires-python  # twinbooster may still advertise py3.8; keep latest anyway
+
+twinbooster
+gradio==4.44.1
 huggingface_hub>=0.22.0
 pandas==2.0.3
 plotly>=5.22.0