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

# Lazy loading the model to meet huggingface stateless GPU requirements 

# Defining a custom stopping criteria class for the model's text generation.
class StopOnTokens(StoppingCriteria):
    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        stop_ids = [50256, 50295]  # IDs of tokens where the generation should stop.
        for stop_id in stop_ids:
            if input_ids[0][-1] == stop_id:  # Checking if the last generated token is a stop token.
                return True
        return False


# Function to generate model predictions.
@spaces.GPU
def predict(message, history):
    torch.set_default_device("cuda")

    # Loading the tokenizer and model from Hugging Face's model hub.
    tokenizer = AutoTokenizer.from_pretrained(
        "macadeliccc/SOLAR-math-2x10.7b",
        trust_remote_code=True
    )
    model = AutoModelForCausalLM.from_pretrained(
        "macadeliccc/SOLAR-math-2x10.7b",
        torch_dtype="auto",
        load_in_4bit=True,
        trust_remote_code=True
    )
    history_transformer_format = history + [[message, ""]]
    stop = StopOnTokens()

    # Formatting the input for the model.
    system_prompt = "<|im_start|>system\nYou are Solar, a helpful AI assistant.<|im_end|>"
    messages = system_prompt + "".join(["".join(["\n<|im_start|>user\n" + item[0], "<|im_end|>\n<|im_start|>assistant\n" + item[1]]) for item in history_transformer_format])
    input_ids = tokenizer([messages], return_tensors="pt").to('cuda')
    streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        input_ids,
        streamer=streamer,
        max_new_tokens=256,
        do_sample=True,
        top_p=0.95,
        top_k=50,
        temperature=0.7,
        num_beams=1,
        stopping_criteria=StoppingCriteriaList([stop])
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()  # Starting the generation in a separate thread.
    partial_message = ""
    for new_token in streamer:
        partial_message += new_token
        if '<|im_end|>' in partial_message:  # Breaking the loop if the stop token is generated.
            break
        yield partial_message


# Setting up the Gradio chat interface.
gr.ChatInterface(predict,
                 description="""
                 <center><img src="https://huggingface.co/macadeliccc/SOLAR-math-2x10.7b-v0.2/resolve/main/solar.png" width="33%"></center>\n\n
                 Chat with [macadeliccc/SOLAR-math-2x10.7b-v0.2](https://huggingface.co/macadeliccc/SOLAR-math-2x10.7b-v0.2), the first Mixture of Experts made by merging two fine-tuned [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0) models.
                 This model (19.2B param) scores top 5 on several evaluations. Output is considered experimental.\n\n
                 ❤️ If you like this work, please follow me on [Hugging Face](https://huggingface.co/macadeliccc) and [LinkedIn](https://www.linkedin.com/in/tim-dolan-python-dev/).
                 """,
                 examples=[
                     'Can you solve the equation 2x + 3 = 11 for x?',
                     'How does Fermats last theorem impact number theory?',
                     'What is a vector in the scope of computer science rather than physics?',
                     'Use a list comprehension to create a list of squares for numbers from 1 to 10.',
                     'Recommend some popular science fiction books.',
                     'Can you write a short story about a time-traveling detective?'
                 ],
                 theme=gr.themes.Soft(primary_hue="purple"),
                 ).launch()