# Install Gradio for creating an interface
import gradio as gr
import torch
from transformers import AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
from peft import AutoPeftModelForCausalLM
from threading import Thread

# Load the fine-tuned model and tokenizer
new_model = "adhisetiawan/phi2_DPO"
model = AutoPeftModelForCausalLM.from_pretrained(new_model,
                                                 low_cpu_mem_usage=True,
                                                 torch_dtype=torch.float16,
                                                 load_in_4bit=True,)
tokenizer = AutoTokenizer.from_pretrained(new_model)
model = model.to('cuda:0')

# Define stopping criteria
class StopOnTokens(StoppingCriteria):
    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        stop_ids = [29, 0]  # Token IDs to stop the generation
        for stop_id in stop_ids:
            if input_ids[0][-1] == stop_id:
                return True
        return False

# Define the prediction function
def predict(message, history):
    # Transform history into the required format
    history_transformer_format = history + [[message, ""]]
    stop = StopOnTokens()

    # Format messages for the model
    messages = "".join(["".join(["\n<human>:"+item[0], "\n<bot>:"+item[1]]) for item in history_transformer_format])
    model_inputs = tokenizer([messages], return_tensors="pt").to("cuda")

    # Set up the streamer and generate responses
    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()

    # Yield partial messages as they are generated
    partial_message = ""
    for new_token in streamer:
        if new_token != '<':
            partial_message += new_token
            yield partial_message

# Launch Gradio Chat Interface
gr.ChatInterface(predict).queue().launch(debug=True)