import os
from threading import Thread
from typing import Iterator
import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from modelGuards.suicideModel import predictSuicide
from openai import OpenAI

MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
DEFAULT_SYSTEM_PROMPT = """\
You are a helpful and joyous mental therapy assistant. Always answer as helpfully and cheerfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content.Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
"""

DESCRIPTION = """
# LLama-2-Mental-Therapy-Chatbot
"""
LICENSE = "open-source"

from llamaModel.model import get_input_token_length, get_LLAMA_response_stream
def generate(
    message: str,
    chat_history: list[tuple[str, str]],
    system_prompt: str,
    max_new_tokens: int = 1024,
    temperature: float = 0.6,
    top_p: float = 0.9,
    top_k: int = 50
) -> Iterator[str]:
    if os.getenv("PREDICT_SUICIDE")=="True" and predict_suicide(message)=='suicide':
        yield("I am sorry that you are feeling this way. You need a specialist help. Please consult a nearby doctor.")
    else:
        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})
        if(get_input_token_length(conversation) > MAX_INPUT_TOKEN_LENGTH):
            raise gr.InterfaceError(f"The accumulated input is too long ({get_input_token_length(conversation)} > {MAX_INPUT_TOKEN_LENGTH}). Clear your chat history and try again.")
        generator = get_LLAMA_response_stream(conversation, max_new_tokens, temperature, top_p, top_k)
        for response in generator:
            yield response


chat_interface = gr.ChatInterface(
    fn=generate,
    additional_inputs=[
        gr.Textbox(label='System Prompt',
                                   value=DEFAULT_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=1,
        ),
        gr.Slider(
            label="Top-p (nucleus sampling)",
            minimum=0.05,
            maximum=1.0,
            step=0.05,
            value=0.95,
        ),
        gr.Slider(
            label="Top-k",
            minimum=1,
            maximum=1000,
            step=1,
            value=50,
        ),
    ],
    stop_btn="Stop",
)

with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    chat_interface.render()
if __name__ == "__main__":
    demo.queue(max_size=20).launch()