app.py

import os
from threading import Thread
from typing import Iterator

import gradio as gr
from gradio.themes.base import Base
from gradio.themes.utils import colors, sizes, fonts
import time
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer

MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

model_id = "ussipan/SipanGPT-0.2-Llama-3.2-1B-GGUF"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)
model.eval()

# Main Gradio inference function
def generate(
    message: str,
    chat_history: list[tuple[str, str]],
    max_new_tokens: int = 1024,
    temperature: float = 0.6,
    top_p: float = 0.9,
    top_k: int = 50,
    repetition_penalty: float = 1.2,
) -> Iterator[str]:

    conversation = [{k: v for k, v in d.items() if k != 'metadata'} for d in chat_history]
    conversation.append({"role": "user", "content": message})

    input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt")
    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
        gr.Warning(f"Se recortó la entrada de la conversación porque era más larga que {MAX_INPUT_TOKEN_LENGTH} tokens.")
    input_ids = input_ids.to(model.device)

    streamer = TextIteratorStreamer(tokenizer, timeout=20.0, 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()

    conversation.append({"role": "assistant", "content": ""})
    outputs = []
    for text in streamer:
        outputs.append(text)
        bot_response = "".join(outputs)
        conversation[-1]['content'] = bot_response
        yield "", conversation


# Implementing Gradio 5 features and building a ChatInterface UI yourself
PLACEHOLDER = """<div style="padding: 20px; text-align: center; display: flex; flex-direction: column; align-items: center;">
   <img src="https://corladlalibertad.org.pe/wp-content/uploads/2024/01/USS.jpg" style="width: 80%; max-width: 550px; height: auto; opacity: 0.55; margin-bottom: 10px;">
   <h1 style="font-size: 28px; margin: 0;">SipánGPT 0.2 Llama 3.2</h1>
   <p style="font-size: 8px; margin: 5px 0 0; opacity: 0.65;">
       <a href="https://huggingface.co/spaces/ysharma/Llama3-2_with_Gradio-5" target="_blank" style="color: inherit; text-decoration: none;">Forked from @ysharma</a>
   </p>
   <p style="font-size: 12px; margin: 5px 0 0; opacity: 0.9;">Este modelo es experimental, puede generar alucinaciones o respuestas incorrectas.</p>
   <p style="font-size: 12px; margin: 5px 0 0; opacity: 0.9;">Entrenado con un dataset de 5.4k conversaciones.</p>
   <p style="font-size: 12px; margin: 5px 0 0; opacity: 0.9;">
       <a href="https://huggingface.co/datasets/ussipan/sipangpt" target="_blank" style="color: inherit; text-decoration: none;">Ver el dataset aquí</a>
   </p>
</div>"""

def handle_retry(history, retry_data: gr.RetryData):
    new_history = history[:retry_data.index]
    previous_prompt = history[retry_data.index]['content']
    yield from generate(previous_prompt, chat_history = new_history, max_new_tokens = 1024, temperature = 0.6, top_p = 0.9, top_k = 50, repetition_penalty = 1.2)

def handle_like(data: gr.LikeData):
    if data.liked:
        print("Votaste positivamente esta respuesta: ", data.value)
    else:
        print("Votaste negativamente esta respuesta: ", data.value)

def handle_undo(history, undo_data: gr.UndoData):
    chatbot = history[:undo_data.index]
    prompt = history[undo_data.index]['content']
    return chatbot, prompt

def chat_examples_fill(data: gr.SelectData):
    yield from generate(data.value['text'], chat_history = [], max_new_tokens = 1024, temperature = 0.6, top_p = 0.9, top_k = 50, repetition_penalty = 1.2)

class SipanGPTTheme(Base):
    def __init__(
        self,
        *,
        primary_hue: colors.Color | str = colors.Color(
            name="custom_green",
            c50="#f0fde4",
            c100="#e1fbc8",
            c200="#c3f789",
            c300="#a5f34a",
            c400="#7dfa00",  # primary color
            c500="#5ef000",
            c600="#4cc700",
            c700="#39a000",
            c800="#2b7900",
            c900="#1d5200",
            c950="#102e00",
        ),
        secondary_hue: colors.Color | str = colors.Color(
            name="custom_secondary_green",
            c50="#edfce0",
            c100="#dbf9c1",
            c200="#b7f583",
            c300="#93f145",
            c400="#5fed00",  # secondary color
            c500="#4ed400",
            c600="#3fad00",
            c700="#308700",
            c800="#236100",
            c900="#153b00",
            c950="#0a1f00",
        ),
        neutral_hue: colors.Color | str = colors.gray,
        spacing_size: sizes.Size | str = sizes.spacing_md,
        radius_size: sizes.Size | str = sizes.radius_md,
        text_size: sizes.Size | str = sizes.text_md,
        font: fonts.Font | str | list[fonts.Font | str] = [
            fonts.GoogleFont("Exo 2"),
            "ui-sans-serif",
            "system-ui",
            "sans-serif",
        ],
        font_mono: fonts.Font | str | list[fonts.Font | str] = [
            fonts.GoogleFont("Fraunces"),
            "ui-monospace",
            "monospace",
        ],
    ):
        super().__init__(
            primary_hue=primary_hue,
            secondary_hue=secondary_hue,
            neutral_hue=neutral_hue,
            spacing_size=spacing_size,
            radius_size=radius_size,
            text_size=text_size,
            font=font,
            font_mono=font_mono,
        )
        self.set(
            body_background_fill="#333333",
            body_background_fill_dark="#333333",
            body_text_color="#ffffff",
            body_text_color_dark="#ffffff",
            color_accent_soft="*secondary_200",
            button_primary_background_fill="*primary_400",
            button_primary_background_fill_hover="*primary_500",
            button_primary_text_color="#333333",
            button_primary_text_color_dark="#333333",
            block_title_text_color="*primary_400",
            block_title_text_color_dark="*primary_400",
            input_background_fill="#444444",
            input_background_fill_dark="#444444",
            input_border_color="#555555",
            input_border_color_dark="#555555",
            input_placeholder_color="#888888",
            input_placeholder_color_dark="#888888",
        )

# Uso del tema
theme = SipanGPTTheme()

with gr.Blocks(theme=theme, fill_height=True) as demo:
    with gr.Column(elem_id="container", scale=1):
        chatbot = gr.Chatbot(
            label="SipánGPT 0.2 Llama 3.2",
            show_label=False,
            type="messages",
            scale=1,
            suggestions = [
                {"text": "Háblame del reglamento de estudiantes de la universidad"},
                {"text": "Qué becas ofrece la universidad"},
                ],
            placeholder = PLACEHOLDER,
            )

    msg = gr.Textbox(submit_btn=True, show_label=False)
    with gr.Accordion('Additional inputs', open=False):
        max_new_tokens = gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS, )
        temperature = gr.Slider(label="Temperature",minimum=0.1, maximum=4.0, step=0.1, value=0.6,)
        top_p = gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9, )
        top_k = gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50, )
        repetition_penalty = gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2, )

    msg.submit(generate, [msg, chatbot, max_new_tokens, temperature, top_p, top_k, repetition_penalty], [msg, chatbot])
    chatbot.retry(handle_retry, chatbot, [msg, chatbot])
    chatbot.like(handle_like, None, None)
    chatbot.undo(handle_undo, chatbot, [chatbot, msg])
    chatbot.suggestion_select(chat_examples_fill, None, [msg, chatbot] )


demo.launch()