Update app.py

app.py

@@ -11,9 +11,8 @@ from transformers import pipeline
 
 MODEL_ID = "fakespot-ai/roberta-base-ai-text-detection-v1"
 
-# If you’re on CPU-only Space and want to be explicit, uncomment device=-1
-# clf = pipeline("text-classification", model=MODEL_ID, device=-1)
-clf = pipeline("text-classification", model=MODEL_ID)
+# Explicitly use CPU on Spaces
+clf = pipeline("text-classification", model=MODEL_ID, device=-1)
 
 def clean_text(s: str) -> str:
     s = s.strip()
@@ -44,11 +43,8 @@ def detect_ai(text: str) -> Tuple[str, float, str]:
         return "—", 0.0, "Please paste some text to analyze."
 
     chunks = [clean_text(c) for c in chunk_text(text, max_words=300)]
-
-    # Batch for speed and lower overhead
     preds = clf(chunks)
 
-    # Aggregate AI likelihood: if a chunk label is 'AI', use score; if 'Human', use (1-score)
     ai_probs = []
     for p in preds:
         label = str(p.get("label", "")).upper()
@@ -125,14 +121,13 @@ with gr.Blocks(title="AI Text Detector") as demo:
 
     def _run(t: str):
         label, score, expl = detect_ai(t)
-        # gr.Label expects a dict of {class_name: confidence} for pretty display
         return {label_out: {label: 1.0}, score_out: score, explain: expl}
 
     gr.Button("Analyze").click(_run, inputs=inp, outputs=[label_out, score_out, explain])
 
 if __name__ == "__main__":
     # For Spaces, PORT is provided by the environment
-    demo.queue(concurrency_count=1).launch(
+    demo.queue().launch(
         server_name="0.0.0.0",
         server_port=int(os.getenv("PORT", 7860))
     )