Delete app.py

app.py

@@ -1,101 +0,0 @@
-import os, time, threading
-import gradio as gr
-import torch, spaces
-from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
-
-MODEL_ID = "WeiboAI/VibeThinker-1.5B"
-SYSTEM_PROMPT = "You are a concise solver. Give one clear final answer."
-MAX_INPUT_TOKENS = 384
-MAX_NEW_TOKENS   = 128         # keep short so the slice finishes
-TEMPERATURE      = 0.4
-TOP_P            = 0.9
-NO_TOKEN_TIMEOUT = 8           # seconds with no new token -> stop the stream
-
-print(f"⏳ Loading {MODEL_ID} …", flush=True)
-tok = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained(
-    MODEL_ID,
-    trust_remote_code=True,
-    low_cpu_mem_usage=True,
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
-).eval()
-print("✅ Model ready.", flush=True)
-
-def _apply_template(messages):
-    return tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-
-def _clip_inputs(prompt_text, max_tokens):
-    ids = tok([prompt_text], return_tensors="pt")
-    if ids["input_ids"].shape[-1] > max_tokens:
-        ids = {k: v[:, -max_tokens:] for k, v in ids.items()}
-    return {k: v.to(model.device) for k, v in ids.items()}
-
-@spaces.GPU(duration=90)  # shorter than max to reduce pre-emption
-def respond(message, history):
-    history = history or []
-    msgs = [{"role": "system", "content": SYSTEM_PROMPT}, *history,
-            {"role": "user", "content": str(message)}]
-
-    prompt = _apply_template(msgs)
-    inputs = _clip_inputs(prompt, MAX_INPUT_TOKENS)
-    streamer = TextIteratorStreamer(tok, skip_prompt=True, skip_special_tokens=True)
-
-    gen_kwargs = dict(
-        **inputs,
-        streamer=streamer,
-        do_sample=True,
-        temperature=TEMPERATURE,
-        top_p=TOP_P,
-        repetition_penalty=1.18,
-        max_new_tokens=MAX_NEW_TOKENS,
-        pad_token_id=tok.eos_token_id,
-        use_cache=True,
-    )
-
-    # run generate() in a daemon thread so it never blocks future calls
-    th = threading.Thread(target=model.generate, kwargs=gen_kwargs, daemon=True)
-    th.start()
-
-    # start streaming
-    assistant = {"role": "assistant", "content": ""}
-    out = list(history) + [assistant]
-
-    last_token_time = time.time()
-    last_heartbeat  = 0
-
-    for chunk in streamer:
-        assistant["content"] += chunk
-        last_token_time = time.time()
-        # heartbeat every ~4s so frontend never stalls
-        if int(time.time() - last_heartbeat) >= 4:
-            yield out
-            last_heartbeat = time.time()
-
-    # if the streamer hangs (no more chunks), enforce timeout
-    while th.is_alive() and (time.time() - last_token_time) < NO_TOKEN_TIMEOUT:
-        # keep UI alive while waiting for last tokens
-        time.sleep(0.5)
-        yield out
-
-    # if still alive after timeout, we abort the turn gracefully
-    if th.is_alive():
-        assistant["content"] += "\n\n(Stopped: no tokens for {}s)".format(NO_TOKEN_TIMEOUT)
-
-    yield out  # final
-
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("## 💡 VibeThinker-1.5B — ZeroGPU slice (stable streaming)")
-    chat = gr.Chatbot(type="messages", height=520)
-    box  = gr.Textbox(placeholder="Ask a question…")
-    send = gr.Button("Send", variant="primary")
-
-    def pipeline(msg, hist):
-        for hist in respond(msg, hist):
-            yield "", hist
-
-    box.submit(pipeline, [box, chat], [box, chat])
-    send.click(pipeline, [box, chat], [box, chat])
-
-if __name__ == "__main__":
-    demo.queue(concurrency_count=1, max_size=16).launch()