app.py

import spaces
import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
from threading import Thread
import traceback

model_path = 'infly/OpenCoder-8B-Instruct'

# Loading the tokenizer and model from Hugging Face's model hub.
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.bfloat16)

# using CUDA for an optimal experience
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)

# Defining a custom stopping criteria class for the model's text generation.
class StopOnTokens(StoppingCriteria):
    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        stop_ids = [96539]  # IDs of tokens where the generation should stop.
        for stop_id in stop_ids:
            if input_ids[0][-1] == stop_id:  # Checking if the last generated token is a stop token.
                return True
        return False


system_role= 'system'
user_role = 'user'
assistant_role = "assistant"

sft_start_token =  "<|im_start|>"
sft_end_token = "<|im_end|>"
ct_end_token = "<|endoftext|>"

# system_prompt= 'You are a CodeLLM developed by INF.'


# Function to generate model predictions.

@spaces.GPU()
def predict(message, history):

    try:
        stop = StopOnTokens()
    
        model_messages = []
        # print(f'history: {history}')

        for i, item in enumerate(history):
            model_messages.append({"role": user_role, "content": item[0]})
            model_messages.append({"role": assistant_role, "content": item[1]})
    
        model_messages.append({"role": user_role, "content": message})
        
        print(f'model_messages: {model_messages}')
    
        # print(f'model_final_inputs: {tokenizer.apply_chat_template(model_messages, add_generation_prompt=True, tokenize=False)}', flush=True)
        model_inputs = tokenizer.apply_chat_template(model_messages, add_generation_prompt=True, return_tensors="pt").to(device)
        # model_inputs = tokenizer([messages], return_tensors="pt").to(device)
        
        streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
        generate_kwargs = dict(
            input_ids=model_inputs,
            streamer=streamer,
            max_new_tokens=1024,
            do_sample=False,
            stopping_criteria=StoppingCriteriaList([stop])
        )
        
        t = Thread(target=model.generate, kwargs=generate_kwargs)
        t.start()  # Starting the generation in a separate thread.
        partial_message = ""
        for new_token in streamer:
            partial_message += new_token
            if sft_end_token in partial_message:  # Breaking the loop if the stop token is generated.
                break
            yield partial_message

    except Exception as e:
        print(traceback.format_exc())


css = """
full-height {
    height: 100%;
}
"""

prompt_examples = [
    'Write a quick sort algorithm in python.',
    'Write a colorful greedy snake game using pygame.',
    'How to use numpy?'
]

placeholder = """
<div style="opacity: 0.5;">
    <img src="https://raw.githubusercontent.com/sail-sg/sailor-llm/main/misc/banner.jpg" style="width:30%;">
    <br>Sailor models are designed to understand and generate text across diverse linguistic landscapes of these SEA regions:
    <br>🇮🇩Indonesian, 🇹🇭Thai, 🇻🇳Vietnamese, 🇲🇾Malay, and 🇱🇦Lao.
</div>
"""


chatbot = gr.Chatbot(label='OpenCoder', placeholder=placeholder) 
with gr.Blocks(theme=gr.themes.Soft(), fill_height=True) as demo:
    
    gr.ChatInterface(predict, chatbot=chatbot, fill_height=True, examples=prompt_examples, css=css)

    demo.launch()  # Launching the web interface.