Create app.py

app.py

@@ -0,0 +1,87 @@
+import gradio as gr
+import torch, threading, time, spaces
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
+
+# ---------------------
+# Model Config
+# ---------------------
+MODEL_ID = "WeiboAI/VibeThinker-1.5B"
+SYSTEM_PROMPT = "You are a concise solver. Respond briefly with the correct answer."
+
+print(f"⏳ Loading {MODEL_ID} …")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    trust_remote_code=True,
+    low_cpu_mem_usage=True,
+    dtype=torch.bfloat16,
+    device_map="auto"
+)
+print("✅ Model ready.")
+
+# ---------------------
+# Chat Function
+# ---------------------
+@spaces.GPU(duration=60)
+def chat_fn(message, history):
+    history = history or []
+    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
+    for user_msg, bot_msg in history:
+        if user_msg: messages.append({"role": "user", "content": user_msg})
+        if bot_msg: messages.append({"role": "assistant", "content": bot_msg})
+    messages.append({"role": "user", "content": message})
+
+    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
+
+    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+    gen_kwargs = dict(
+        **inputs,
+        streamer=streamer,
+        max_new_tokens=200,
+        temperature=0.3,
+        top_p=0.9,
+        do_sample=False,
+        pad_token_id=tokenizer.eos_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+        repetition_penalty=1.15
+    )
+
+    thread = threading.Thread(target=model.generate, kwargs=gen_kwargs)
+    thread.start()
+
+    partial_text = ""
+    for new_text in streamer:
+        partial_text += new_text
+        yield partial_text
+
+# ---------------------
+# UI
+# ---------------------
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("## 💡 VibeThinker-1.5B · Edge/ZeroGPU (Streaming Stable)")
+    chatbot = gr.Chatbot(label="Chatbot", height=500)
+    msg_box = gr.Textbox(label="Textbox", placeholder="Type here…")
+    send_btn = gr.Button("Send", variant="primary")
+
+    def user_message(message, history):
+        history = history or []
+        return "", history + [[message, None]]
+
+    def bot_response(history):
+        user_message = history[-1][0]
+        response = ""
+        for partial in chat_fn(user_message, history[:-1]):
+            response = partial
+            history[-1][1] = response
+            yield history
+
+    msg_box.submit(user_message, [msg_box, chatbot], [msg_box, chatbot], queue=False).then(
+        bot_response, chatbot, chatbot
+    )
+    send_btn.click(user_message, [msg_box, chatbot], [msg_box, chatbot], queue=False).then(
+        bot_response, chatbot, chatbot
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=16).launch()