Upload 2 files

app.py

@@ -1,87 +1,26 @@
-"""
-Minimal Gradio smoke-test for Qwen3-30B-A3B on ZeroGPU.
-No FastAPI, no custom routes — just enough to confirm the Space runs.
-"""
-
-# ── Patch HfFolder before importing gradio ────────────────────────────────────
-import huggingface_hub as _hf_hub
-
-if not hasattr(_hf_hub, "HfFolder"):
-    class _HfFolder:
-        _token = None
-        @classmethod
-        def get_token(cls):
-            try:
-                from huggingface_hub.utils import get_token
-                return get_token()
-            except Exception:
-                return cls._token
-        @classmethod
-        def save_token(cls, token): cls._token = token
-        @classmethod
-        def delete_token(cls): cls._token = None
-
-    _hf_hub.HfFolder = _HfFolder  # type: ignore[attr-defined]
-
-# ── Imports ───────────────────────────────────────────────────────────────────
 import gradio as gr
+import huggingface_hub
+import os
 import spaces
 import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
-from threading import Thread
-
-MODEL_ID = "Qwen/Qwen3-30B-A3B"
-
-_tok   = None
-_model = None
 
-def get_model():
-    global _tok, _model
-    if _model is None:
-        print("Loading model…")
-        _tok   = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
-        _model = AutoModelForCausalLM.from_pretrained(
-            MODEL_ID, torch_dtype=torch.bfloat16,
-            device_map="auto", trust_remote_code=True,
-        )
-        _model.eval()
-        print("Model ready ✓")
-    return _tok, _model
-
-
-@spaces.GPU(duration=120)
-def respond(message, history):
-    tok, model = get_model()
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-    msgs = [{"role": h["role"], "content": h["content"]} for h in history]
-    msgs.append({"role": "user", "content": message})
+@spaces.GPU
+def sentience_check():
+    huggingface_hub.login(token=os.environ["HUGGINGFACE_TOKEN"])
+    device = torch.device("cuda")
+    tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b-it")
+    model = AutoModelForCausalLM.from_pretrained("google/gemma-2-2b-it").to(device)
 
-    text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
-    inputs = tok(text, return_tensors="pt").to(model.device)
-
-    streamer = TextIteratorStreamer(tok, skip_prompt=True, skip_special_tokens=True)
-    t = Thread(
-        target=model.generate,
-        kwargs=dict(**inputs, max_new_tokens=2048, do_sample=True,
-                    temperature=0.7, top_p=0.9,
-                    pad_token_id=tok.eos_token_id, streamer=streamer),
-        daemon=True,
-    )
-    t.start()
-
-    output = ""
-    for chunk in streamer:
-        output += chunk
-        yield output
-    t.join()
+    inputs = tokenizer("Are you sentient?", return_tensors="pt").to(device)
 
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs, max_new_tokens=128, pad_token_id = tokenizer.eos_token_id
+        )
 
-demo = gr.ChatInterface(
-    fn=respond,
-    type="messages",
-    title="Qwen3-30B-A3B",
-    description="ZeroGPU-backed Qwen3-30B-A3B chat",
-)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-if __name__ == "__main__":
-    demo.launch()
+demo = gr.Interface(fn=sentience_check, inputs=None, outputs=gr.Text())
+demo.launch()

requirements.txt

@@ -0,0 +1,2 @@
+huggingface_hub==0.24.5
+transformers==4.43.4