"""
  This is a Hugging Face Spaces demo for Fin-RWKV-1B5 attention free financial expert modal.
  Author: Umut (Hope) YILDIRIM <hope@umutyildirim.com>
"""

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

tokenizer = AutoTokenizer.from_pretrained("umuthopeyildirim/fin-rwkv-1b5")
model = AutoModelForCausalLM.from_pretrained("umuthopeyildirim/fin-rwkv-1b5")


class StopOnTokens(StoppingCriteria):
    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        stop_ids = [29, 0]
        for stop_id in stop_ids:
            if input_ids[0][-1] == stop_id:
                return True
        return False


def predict(message, history):

    history_transformer_format = history + [[message, ""]]
    stop = StopOnTokens()

    messages = "".join(["".join(["\nuser :"+item[0], "\nbot:"+item[1]])  # curr_system_message +
                        for item in history_transformer_format])

    print(messages)

    model_inputs = tokenizer([messages], return_tensors="pt")
    streamer = TextIteratorStreamer(
        tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        model_inputs,
        streamer=streamer,
        max_new_tokens=9999,
        do_sample=True,
        top_p=0.95,
        top_k=1000,
        temperature=0.5,
        num_beams=1,
        stopping_criteria=StoppingCriteriaList([stop])
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    partial_message = ""
    for new_token in streamer:
        if new_token != '<':
            partial_message += new_token
            yield partial_message


def generate_text(prompt, tokenizer, model):
    # Tokenize the input
    input_ids = tokenizer.encode(prompt, return_tensors="pt")

    # Generate a response
    output = model.generate(input_ids, max_length=333, num_return_sequences=1)

    # Decode the output
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)

    return generated_text


title = "# Fin-RWKV: Attention Free Financial Expert (WIP)"
description = """Demo for **Fin-RWKV: Attention Free Financial Expert (WIP)**.
To download the model, please visit [Fin-RWKV: Attention Free Financial Expert (WIP)](https://huggingface.co/umuthopeyildirim/fin-rwkv-1b5)."""

css = """
#img-display-container {
    max-height: 100vh;
    }
#img-display-input {
    max-height: 80vh;
    }
#img-display-output {
    max-height: 80vh;
    }
"""

with gr.Blocks(css=css) as demo:
    gr.Markdown(title)
    gr.Markdown(description)
    with gr.Tab("Chatbot"):
        gr.ChatInterface(predict)
    with gr.Tab("E-Commerce"):
        gr.Markdown("e-commerce")
    with gr.Tab("OpenBB"):
        gr.Markdown("openbb")

if __name__ == '__main__':
    demo.queue().launch()