import time
from threading import Thread
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import gradio as gr
import torch

model_id = "DeepMount00/Llama-3-8b-Ita"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto").eval()  # to("cuda:0")

DESCRIPTION = '''
<div>
<h1 style="text-align: center;">Meta Llama3 8B Ita</h1>
<p>This Space demonstrates the instruction-tuned model <a href="https://huggingface.co/DeepMount00/Llama-3-8b-Ita"><b>Meta Llama3 8b Chat</b></a>.</p>
</div>
'''
PLACEHOLDER = """
<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
   <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/64f1bf6a8b550e875926a590/9IXg0qMUF0OV2cWPT8cZn.jpeg" style="width: 80%; max-width: 550px; height: auto; opacity: 0.50;  "> 
   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">DeepMount00 llama3</h1>
   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Chiedimi qualsiasi cosa...</p>
</div>
"""


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

@spaces.GPU(duration=120)
def chat_llama3_8b(message: str, history: list, temperature: float, max_new_tokens: int) -> str:
    # Creare la struttura della conversazione
    conversation = []
    for user, assistant in history:
        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
    conversation.append({"role": "user", "content": message})

    # Preparare gli input per il modello
    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(model.device)

    # Parametri per la generazione del testo
    do_sample = True if temperature > 0 else False  # Usa il campionamento a meno che la temperatura non sia 0
    real_temperature = max(temperature, 0.001)  # Evita temperatura 0 che disabilita il campionamento

    # Generare una risposta dal modello
    generated_ids = model.generate(
        input_ids=input_ids,
        max_new_tokens=max_new_tokens,
        do_sample=do_sample,
        temperature=real_temperature,
        eos_token_id=tokenizer.eos_token_id
    )

    # Decodificare i token generati
    decoded = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
    prompt_end_index = decoded[0].find(message) + len(message)
    final_response = decoded[0][prompt_end_index:] if prompt_end_index != -1 else decoded[0]

    return final_response.strip("assistant")


# 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.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
    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=0.001,
                      label="Temperature",
                      render=False),
            gr.Slider(minimum=128,
                      maximum=4096,
                      step=1,
                      value=512,
                      label="Max new tokens",
                      render=False),
        ],
        examples=[
            ['Quanto è alta la torre di Pisa?'],
            ["Se un mattone pesa 1kg più mezzo mattone, quanto pesa il mattone? rispondi impostando l'equazione"],
            ['Quanto fa 9.000 * 9.000?'],
            ['Scrivi una funzione python che calcola i primi n numeri di fibonacci'],
            ['Inventa tre indovinelli tutti diversi con le relative risposte in formato json']
        ],
        cache_examples=False,
    )


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