from transformers import AutoModel, AutoTokenizer, LlamaTokenizer, LlamaForCausalLM
import gradio as gr
import torch

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

tokenizer = LlamaTokenizer.from_pretrained("lmsys/vicuna-7b-v1.3", trust_remote_code=True)
model = LlamaForCausalLM.from_pretrained("lmsys/vicuna-7b-v1.3", trust_remote_code=True).to(DEVICE)
model = model.eval()

def predict(input, history=None):
    if history is None:
        history = []
    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
    history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
    # convert the tokens to text, and then split the responses into the right format
    response = tokenizer.decode(history[0]).split("<|endoftext|>")
    response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
    return response, history


with gr.Blocks() as demo:
    gr.Markdown('''## Confidential HuggingFace Runner
    ''')
    state = gr.State([])
    chatbot = gr.Chatbot([], elem_id="chatbot").style(height=400)
    with gr.Row():
        with gr.Column(scale=4):
            txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter").style(container=False)
        with gr.Column(scale=1):
            button = gr.Button("Generate")
    txt.submit(predict, [txt, state], [chatbot, state])
    button.click(predict, [txt, state], [chatbot, state])
demo.queue().launch(share=True, server_name="0.0.0.0")