app.py

import os
from threading import Thread
from typing import Iterator

import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer

MAX_MAX_NEW_TOKENS = 4096
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "8000"))

DESCRIPTION = """\
# ConvAI 9b v2 Chat
"""

# Load model with appropriate device configuration
def load_model():
    model_id = "CreitinGameplays/dumbbot"
    device = "cuda" if torch.cuda.is_available() else "cpu"
    
    # If using CPU, load in 32-bit to avoid potential issues with 16-bit operations
    if device == "cpu":
        model = AutoModelForCausalLM.from_pretrained(
            model_id,
            torch_dtype=torch.float32,
            low_cpu_mem_usage=True
        )
    else:
        model = AutoModelForCausalLM.from_pretrained(
            model_id,
            torch_dtype=torch.float16,
            device_map="auto"
        )
    tokenizer = AutoTokenizer.from_pretrained(model_id, padding_side='left')
    tokenizer.use_default_system_prompt = False
    
    return model, tokenizer, device

model, tokenizer, device = load_model()

system_prompt_text = "You are Ricardinho."

def generate(
    message: str,
    chat_history: list[tuple[str, str]],
    system_prompt: str = system_prompt_text,
    max_new_tokens: int = 1024,
    temperature: float = 0.6,
    top_p: float = 1.0,
    top_k: int = 0,
    repetition_penalty: float = 1.2,
) -> Iterator[str]:
    conversation = []
    if system_prompt:
        conversation.append({"role": "system", "content": system_prompt})
    for user, assistant in chat_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")
    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
    input_ids = input_ids.to(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,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        num_beams=1,
        repetition_penalty=repetition_penalty,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

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

chat_interface = gr.ChatInterface(
    fn=generate,
    additional_inputs=[
        gr.Textbox(label="System prompt", lines=6, value=system_prompt_text),
        gr.Slider(
            label="Max new tokens",
            minimum=1,
            maximum=MAX_MAX_NEW_TOKENS,
            step=1,
            value=DEFAULT_MAX_NEW_TOKENS,
        ),
        gr.Slider(
            label="Temperature",
            minimum=0.1,
            maximum=4.0,
            step=0.1,
            value=0.6,
        ),
        gr.Slider(
            label="Top-p (nucleus sampling)",
            minimum=0.05,
            maximum=1.0,
            step=0.05,
            value=1.0,
        ),
        gr.Slider(
            label="Top-k",
            minimum=0,
            maximum=1000,
            step=1,
            value=0,
        ),
        gr.Slider(
            label="Repetition penalty",
            minimum=1.0,
            maximum=2.0,
            step=0.05,
            value=1.2,
        ),
    ],
    stop_btn=None,
    examples=[
        ["Hello there! How are you doing?"],
        ["Can you explain briefly to me what is the Python programming language?"],
        ["Explain the plot of Cinderella in a sentence."],
        ["How many hours does it take a man to eat a Helicopter?"],
        ["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
    ],
)

with gr.Blocks() as demo:
    gr.Markdown(DESCRIPTION)
    chat_interface.render()
    
if __name__ == "__main__":
    demo.queue(max_size=10).launch(show_error=True)