import os
from threading import Thread
from typing import Iterator

import gradio as gr
#import spaces
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")

# Download model from Huggingface Hub
# Change this to meta-llama or the correct org name from Huggingface Hub
model_id = "ussipan/SipanGPT-0.1-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 0.1 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;">
       <a href="https://huggingface.co/datasets/ussipan/sipangpt" target="_blank" style="color: inherit; text-decoration: none;">Ver el dataset aquí</a>
   </p>
</div>"""

# <p style="font-size: 12px; margin: 5px 0 0; opacity: 0.9;">Entrenado con un dataset de 5.4k conversaciones.</p>

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)


with gr.Blocks(theme=gr.themes.Soft(), fill_height=True) as demo:
    with gr.Column(elem_id="container", scale=1):
        chatbot = gr.Chatbot(
            label="SipánGPT 0.1 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()