#from transformers import AutoModel, AutoTokenizer
import gradio as gr

from transformers import AutoModelForSeq2SeqLM

model = AutoModelForSeq2SeqLM.from_pretrained("silver/chatglm-6b-int4-qe-slim")

#tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
#model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
#tokenizer = AutoTokenizer.from_pretrained("silver/chatglm-6b-slim", trust_remote_code=True)
#model = AutoModel.from_pretrained("silver/chatglm-6b-slim", trust_remote_code=True).half().cuda()


model = model.eval()

def predict(input, history=None):
    if history is None:
        history = []
    response, history = model.chat(tokenizer, input, history)
    return history, history


with gr.Blocks() as demo:
    
    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()