import gradio as gr
import random
import time

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("redoop/opt-1.3b")
model = AutoModelForCausalLM.from_pretrained("redoop/opt-1.3b")

model.eval()

#import intel_extension_for_pytorch as ipex
#model = ipex.optimize(model)


def generate(text):
    #text = '<s>{}</s></s>'.format(text)
    input_ids = tokenizer(text, return_tensors="pt").input_ids
    #input_ids = input_ids.to(device)
    outputs = model.generate(input_ids, max_new_tokens=200, do_sample=True, top_p=0.7, temperature=0.35,
                             repetition_penalty=1.2, eos_token_id=tokenizer.eos_token_id)
    rets = tokenizer.batch_decode(outputs)
    output = rets[0].strip().replace(text, "").replace('<|endoftext|>', "")
    return output

with gr.Blocks() as demo:
    chatbot = gr.Chatbot()
    msg = gr.Textbox()
    clear = gr.Button("Clear")

    def respond(message, chat_history):
        #bot_message = random.choice(["How are you?", "I love you", "I'm very hungry"])
        bot_message = generate(message)
        chat_history.append((message, bot_message))
        time.sleep(1)
        return "", chat_history

    msg.submit(respond, [msg, chatbot], [msg, chatbot])
    clear.click(lambda: None, None, chatbot, queue=False)

demo.queue(api_open=False)
if __name__ == "__main__":
    demo.launch()