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


title = "🦅Falcon 🗨️ChatBot"
description = "Falcon-RW-1B is a 1B parameters causal decoder-only model built by TII and trained on 350B tokens of RefinedWeb."
examples = [["How are you?"]]


tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-rw-1b")
model = AutoModelForCausalLM.from_pretrained(
    "tiiuae/falcon-rw-1b",
    trust_remote_code=True,
    torch_dtype=torch.float16
)


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()

    #Construct the input message string for the model by concatenating the current system message and conversation history
    messages = "".join(["".join(["\n<human>:"+item[0], "\n<bot>:"+item[1]])  #curr_system_message + 
                for item in history_transformer_format])
    
    #Tokenize the messages string
    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=1024,
        do_sample=True,
        top_p=0.95,
        top_k=1000,
        temperature=1.0,
        num_beams=1,
        stopping_criteria=StoppingCriteriaList([stop])
        )
    #t = Thread(target=model.generate, kwargs=generate_kwargs)
    #t.start()
    model.generate(**generate_kwargs)

    #Initialize an empty string to store the generated text
    partial_message  = ""
    for new_token in streamer:
        if new_token != '<':
            partial_message += new_token
            yield partial_message 
            

gr.ChatInterface(predict,
    title=title,
    description=description,
    examples=examples,
    cache_examples=True,
    retry_btn=None,
    undo_btn="Delete Previous",
    clear_btn="Clear",
    chatbot=gr.Chatbot(height=300),
    textbox=gr.Textbox(placeholder="Chat with me").queue().launch()