import os 
os.system("pip install huggingface_hub")

from huggingface_hub import space_info

from predict import *
from transformers import BloomTokenizerFast, BloomForCausalLM

#import os
import gradio as gr

model_path = "svjack/bloom-daliy-dialogue-english"
tokenizer = BloomTokenizerFast.from_pretrained(model_path)
model = BloomForCausalLM.from_pretrained(model_path)

obj = Obj(model, tokenizer)

example_sample = [
    ["This dog is fierce,", 128],
    ["Do you like this film?", 64],
]

def demo_func(prefix, max_length):
    max_length = max(int(max_length), 32)
    l = obj.predict(prefix, max_length=max_length)[0].split("\n-----\n")
    l_ = []
    for ele in l:
        if ele not in l_:
            l_.append(ele)
    l = l_
    assert type(l) == type([])
    return {
        "Dialogue Context": l
    }

markdown_exp_size = "##"
lora_repo = "svjack/chatglm3-few-shot"
lora_repo_link = "svjack/chatglm3-few-shot/?input_list_index=11"
emoji_info = space_info(lora_repo).__dict__["cardData"]["emoji"]
space_cnt = 1
task_name = "[---English Dialogue Generator---]"
description = f"{markdown_exp_size} {task_name} few shot prompt in ChatGLM3 Few Shot space repo (click submit to activate) : [{lora_repo_link}](https://huggingface.co/spaces/{lora_repo_link}) {emoji_info}"


demo = gr.Interface(
        fn=demo_func,
        inputs=[gr.Text(label = "Prefix"),
                gr.Number(label = "Max Length", value = 128)
        ],
        outputs="json",
        title=f"Bloom English Daliy Dialogue Generator 🦅🌸 demonstration",
        examples=example_sample if example_sample else None,
        #description = 'This _example_ was **drive** from <br/><b><h4>[https://github.com/svjack/Daliy-Dialogue](https://github.com/svjack/Daliy-Dialogue)</h4></b>\n',
        description = description,
        cache_examples = False
    )

demo.launch(server_name=None, server_port=None)