Update app.py

app.py

@@ -26,6 +26,7 @@ MODES = {
     ),
 }
 
+print("Loading model...")
 tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(
     MODEL_ID,
@@ -34,6 +35,7 @@ model = AutoModelForCausalLM.from_pretrained(
     trust_remote_code=True
 )
 pipe = TextGenerationPipeline(model=model, tokenizer=tokenizer, device=-1)
+print("Model loaded.")
 
 # ----------- CHAT FUNCTION -----------
 def chat(user_message: str):
@@ -41,7 +43,7 @@ def chat(user_message: str):
     if not user_message:
         return "[Error: Empty prompt]"
 
-    # Mode parsing
+    # Detect mode
     if "::" in user_message:
         mode_key, msg = user_message.split("::", 1)
         mode_key = mode_key.strip().upper()
@@ -72,21 +74,24 @@ iface = gr.Interface(
     inputs=gr.Textbox(lines=2, placeholder="Type your message…"),
     outputs=gr.Textbox(),
     title="BLACKLIGHT by v0id",
-    description="Brutalist • Minimal • Precise — Multiple modes supported. Use TRUTH::, SURGE::, NULL::",
+    description="Brutalist • Minimal • Precise — Modes: TRUTH::, SURGE::, NULL:: (or leave blank for TRUTH)",
 )
 
-# ----------- FASTAPI + SHIM ENDPOINT -----------
+# ----------- FASTAPI APP -----------
 app = FastAPI()
 
 @app.post("/run/predict")
 async def predict(request: Request):
     try:
         body = await request.json()
-        user_message = body.get("data", [""])[0]
+        if isinstance(body.get("data"), list) and len(body["data"]) > 0:
+            user_message = body["data"][0]
+        else:
+            user_message = ""
         reply = chat(user_message)
         return JSONResponse({"data": [reply]})
     except Exception as e:
         return JSONResponse({"error": str(e)}, status_code=500)
 
-# Mount Gradio at root so Spaces boots
+# Mount Gradio at `/` so Hugging Face Spaces auto-starts
 app = gr.mount_gradio_app(app, iface, path="/")