updated app.py

app.py

@@ -1,57 +1,4 @@
 import gradio as gr
-import torch
-from threading import Thread
-from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
 
-MODEL_ID = "TildeAI/TildeOpen-30b"
-
-# Tokenizer MUST be slow version per model card
-tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=False)
-
-# Load model on GPU with BF16
-model = AutoModelForCausalLM.from_pretrained(
-    MODEL_ID,
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
-)
-
-SYSTEM = (
-    "You are a helpful multilingual assistant. "
-    "The model is base (not instruction-tuned), so follow the user's request precisely."
-)
-
-def format_history(history, user_msg):
-    prompt = SYSTEM + "\n\n"
-    for u, a in history:
-        prompt += f"<|user|>\n{u}\n<|assistant|>\n{a}\n"
-    prompt += f"<|user|>\n{user_msg}\n<|assistant|>\n"
-    return prompt
-
-def chat_fn(message, history):
-    prompt = format_history(history, message)
-    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-
-    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
-    gen_kwargs = dict(
-        **inputs,
-        max_new_tokens=512,
-        do_sample=True,
-        temperature=0.7,
-        top_p=0.9,
-        repetition_penalty=1.1,
-        streamer=streamer,
-    )
-
-    thread = Thread(target=model.generate, kwargs=gen_kwargs)
-    thread.start()
-    partial = ""
-    for new_text in streamer:
-        partial += new_text
-        yield partial
-
-demo = gr.ChatInterface(
-    fn=chat_fn,
-    title="TildeOpen-30B (Transformers, BF16)",
-    description="Base model (not instruction-tuned). Multilingual; context length 8192.",
-)
-demo.queue().launch()
+def greet(name): return "Hello " + name + "!!"
+gr.Interface(fn=greet, inputs="text", outputs="text", title="Hello test").launch()