app.py

#duplicated from https://huggingface.co/spaces/chheplo/DeepSeek-R1-Distill-Llama-8B
import gradio as gr
import os
import spaces
from transformers import GemmaTokenizer, AutoModelForCausalLM
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread

# Set an environment variable
HF_TOKEN = os.environ.get("HF_TOKEN", None)


DESCRIPTION = '''
<div>
<h1 style="text-align: center;">deepseek-ai/DeepSeek-R1-Distill-Llama-8B</h1>
</div>
'''

LICENSE = """
<p/>

---
"""

PLACEHOLDER = """
<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">DeepSeek-R1-Distill-Llama-8B</h1>
   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Ask me anything...</p>
</div>
"""


css = """
h1 {
  text-align: center;
  display: block;
}

#duplicate-button {
  margin: auto;
  color: white;
  background: #1565c0;
  border-radius: 100vh;
}
"""
model_id = "AXCXEPT/phi-4-deepseek-R1K-RL-EZO"
model_id = "AXCXEPT/phi-4-open-R1-Distill-EZOv1"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)  # to("cuda:0") 
terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

@spaces.GPU(duration=120)
def chat_llama3_8b(message: str, 
              history: list, 
              temperature: float, 
              max_new_tokens: int
             ) -> str:
    """
    Generate a streaming response using the llama3-8b model.
    Args:
        message (str): The input message.
        history (list): The conversation history used by ChatInterface.
        temperature (float): The temperature for generating the response.
        max_new_tokens (int): The maximum number of new tokens to generate.
    Returns:
        str: The generated response.
    """
    conversation = []
    for user, assistant in history:
        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
    conversation.append({"role": "user", "content": message})

    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(model.device)
    
    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)

    generate_kwargs = dict(
        input_ids= input_ids,
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        temperature=temperature,
        eos_token_id=terminators,
    )
    # This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash.             
    if temperature == 0:
        generate_kwargs['do_sample'] = False
        
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    outputs = []
    for text in streamer:
        outputs.append(text)
        #print(outputs)
        yield "".join(outputs)
        

# Gradio block
chatbot=gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Gradio ChatInterface')

with gr.Blocks(fill_height=True, css=css) as demo:
    
    gr.Markdown(DESCRIPTION)
    gr.ChatInterface(
        fn=chat_llama3_8b,
        chatbot=chatbot,
        fill_height=True,
        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
        additional_inputs=[
            gr.Slider(minimum=0,
                      maximum=1, 
                      step=0.1,
                      value=0.5, 
                      label="Temperature", 
                      render=False),
            gr.Slider(minimum=128, 
                      maximum=4096,
                      step=1,
                      value=1024, 
                      label="Max new tokens", 
                      render=False ),
            ],
        examples=[
            ['How to setup a human base on Mars? Give short answer.'],
            ['Explain theory of relativity to me like I’m 8 years old.'],
            ['What is 9,000 * 9,000?'],
            ['Write a pun-filled happy birthday message to my friend Alex.'],
            ['Justify why a penguin might make a good king of the jungle.']
            ],
        cache_examples=False,
                     )
    
    gr.Markdown(LICENSE)
    
if __name__ == "__main__":
    demo.launch()



    
import spaces
import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from transformers import TextIteratorStreamer
from threading import Thread

import gradio as gr

text_generator = None
is_hugging_face = True
model_id = "AXCXEPT/phi-4-deepseek-R1K-RL-EZO"
model_id = "AXCXEPT/phi-4-open-R1-Distill-EZOv1"

huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
huggingface_token = None
device = "auto" # torch.device("cuda" if torch.cuda.is_available() else "cpu")
device = "cuda"
dtype = torch.bfloat16
dtype = torch.float16

if not huggingface_token:
    pass
    print("no HUGGINGFACE_TOKEN if you need set secret ")
    #raise ValueError("HUGGINGFACE_TOKEN environment variable is not set")
        

    
    
    



tokenizer = AutoTokenizer.from_pretrained(model_id, token=huggingface_token)

print(model_id,device,dtype)
histories = []
#model = None



if not is_hugging_face:
    model = AutoModelForCausalLM.from_pretrained(
        model_id, token=huggingface_token ,torch_dtype=dtype,device_map=device
    )
    text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer,torch_dtype=dtype,device_map=device,stream=True ) #pipeline has not to(device)
    
    if next(model.parameters()).is_cuda:
        print("The model is on a GPU")
    else:
        print("The model is on a CPU")

    #print(f"text_generator.device='{text_generator.device}")
    if str(text_generator.device).strip() == 'cuda':
        print("The pipeline is using a GPU")
    else:
        print("The pipeline is using a CPU")

print("initialized")


def generate_text(messages):
    if is_hugging_face:#need everytime initialize for ZeroGPU
        model = AutoModelForCausalLM.from_pretrained(
                model_id, token=huggingface_token ,torch_dtype=dtype,device_map=device
            )
        model.to(device)
    question = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    question = tokenizer(question, return_tensors="pt").to(device)

    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True)
    generation_kwargs = dict(question, streamer=streamer, max_new_tokens=200)
    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    
    generated_output = ""
    thread.start()
    for new_text in streamer:
        generated_output += new_text
        yield generated_output            
generate_text.zerogpu = True
    
       

@spaces.GPU(duration=60)
def call_generate_text(message, history):
   # history.append({"role": "user", "content": message})
    #print(message)
    #print(history)
   
    messages = history+[{"role":"user","content":message}]
    try:
        
        for text in generate_text(messages):
            yield text
    except RuntimeError  as e:
        print(f"An unexpected error occurred: {e}")
        yield ""

demo = gr.ChatInterface(call_generate_text,type="messages")

#if __name__ == "__main__":
demo.queue()
demo.launch()