import os
import gradio as gr
import spaces
import torch
from threading import Thread
from transformers import AutoModelForCausalLM, AutoTokenizer

# Constants for model behavior
MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))

# Models selection
MODELS = {
    "Fast-Model": "Artples/L-MChat-Small",
    "Quality-Model": "Artples/L-MChat-7b"
}

# Description for the application
DESCRIPTION = """\
# L-MChat
This Space demonstrates [L-MChat](https://huggingface.co/collections/Artples/l-mchat-663265a8351231c428318a8f) by L-AI.
"""

# Check for GPU availability
if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU! This demo does not work on CPU.</p>"

# Load models and tokenizers
models = {name: AutoModelForCausalLM.from_pretrained(model_id, device_map="auto") for name, model_id in MODELS.items()}
tokenizers = {name: AutoTokenizer.from_pretrained(model_id) for name, model_id in MODELS.items()}

@spaces.GPU(enable_queue=True, duration=90)
def generate(
    model_choice: str,
    message: str,
    chat_history: list[tuple[str, str]],
    system_prompt: str,
    max_new_tokens: int = 1024,
    temperature: float = 0.1,
    top_p: float = 0.9,
    top_k: int = 50,
    repetition_penalty: float = 1.2,
) -> str:
    model = models[model_choice]
    tokenizer = tokenizers[model_choice]

    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(conversation, return_tensors="pt", truncation=True, max_length=MAX_INPUT_TOKEN_LENGTH).input_ids
    input_ids = input_ids.to(model.device)

    output_ids = model.generate(
        input_ids,
        max_length=input_ids.shape[1] + max_new_tokens,
        temperature=temperature,
        top_p=top_p,
        top_k=top_k,
        repetition_penalty=repetition_penalty,
        num_return_sequences=1,
    )

    return tokenizer.decode(output_ids[0], skip_special_tokens=True)

# Gradio Interface
chat_interface = gr.Interface(
    fn=generate,
    inputs=[
        gr.Dropdown(label="Choose Model", choices=list(MODELS.keys()), default="Quality-Model"),
        gr.ChatBox(),
        gr.Textbox(label="System prompt", lines=6),
        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=0.9),
        gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50),
        gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2),
    ],
    outputs="text",
    theme='ehristoforu/RE_Theme',
    examples=[
        ["Quality-Model", "Hello there! How are you doing?", [], "Let's start the conversation.", 1024, 0.6, 0.9, 50, 1.2]
    ]
)

# Main execution block
with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    chat_interface.render()

if __name__ == "__main__":
    demo.queue(max_size=20).launch()