runtime weights download from ScamShield-model + zero-gpu

app.py

@@ -3,10 +3,23 @@ sys.path.insert(0, os.path.join(os.path.dirname(__file__), "smishing_detector"))
 
 import json
 import gradio as gr
+import spaces
 from predictor import SmishingPredictor
 from explainability.shap_explainer import SmishingExplainer
 
 CHECKPOINT = os.path.join(os.path.dirname(__file__), "smishing_detector", "best_model.pt")
+MODEL_REPO = os.getenv("SCAMSHIELD_MODEL_REPO", "ExistedYear/ScamShield-model")
+
+if not os.path.exists(CHECKPOINT):
+    print(f"Checkpoint not found locally, downloading from {MODEL_REPO}...")
+    from huggingface_hub import hf_hub_download
+
+    os.makedirs(os.path.dirname(CHECKPOINT), exist_ok=True)
+    downloaded = hf_hub_download(repo_id=MODEL_REPO, filename="best_model.pt")
+    import shutil
+
+    shutil.copyfile(downloaded, CHECKPOINT)
+    print(f"Weights downloaded to {CHECKPOINT}")
 
 print("Loading ScamShield model...")
 predictor = SmishingPredictor(CHECKPOINT)
@@ -14,6 +27,7 @@ print("Loading SHAP explainer...")
 explainer = SmishingExplainer(predictor)
 print("Model loaded. Ready.")
 
+@spaces.GPU
 def analyze(message):
     if not message or not message.strip():
         return "⚠️ Please enter a message.", None, None
@@ -133,6 +147,7 @@ with gr.Blocks(title="ScamShield — Smishing Detector") as demo:
                 "**Features:** 9 URL signals + 8 text signals | **F1:** 0.98")
 
 
+@spaces.GPU
 def api_predict(message: str) -> str:
     return json.dumps(predictor.predict(message), ensure_ascii=False)
 

requirements.txt

@@ -9,3 +9,4 @@ python-dotenv>=1.0.0
 gradio>=5.0.0
 pandas>=2.0.0
 shap>=0.43.0
+huggingface_hub>=0.20.0