import streamlit as st
import pandas as pd
from pathlib import Path
import requests
import base64
from requests.auth import HTTPBasicAuth
import torch

st.set_page_config(layout="wide")
st.title("【随无涯】")

@st.cache(allow_output_mutation=True)
def load_model():
    from transformers import (
        EncoderDecoderModel,
        AutoTokenizer
    )
    PRETRAINED = "raynardj/wenyanwen-ancient-translate-to-modern"
    tokenizer = AutoTokenizer.from_pretrained(PRETRAINED)
    model = EncoderDecoderModel.from_pretrained(PRETRAINED)
    return tokenizer, model


tokenizer, model = load_model()


def inference(text):
    tk_kwargs = dict(
        truncation=True,
        max_length=168,
        padding="max_length",
        return_tensors='pt')

    inputs = tokenizer([text, ], **tk_kwargs)
    with torch.no_grad():
        return tokenizer.batch_decode(
            model.generate(
                inputs.input_ids,
                attention_mask=inputs.attention_mask,
                num_beams=3,
                max_length=256,
                bos_token_id=101,
                eos_token_id=tokenizer.sep_token_id,
                pad_token_id=tokenizer.pad_token_id,
            ), skip_special_tokens=True)[0].replace(" ","")


@st.cache
def get_file_df():
    file_df = pd.read_csv("meta.csv")
    return file_df


file_df = get_file_df()

col1, col2 = st.columns([.3, 1])

col1.markdown("""
* 朕亲自下厨的[🤗 翻译模型](https://github.com/raynardj/wenyanwen-ancient-translate-to-modern), [⭐️ 训练笔记](https://github.com/raynardj/yuan)
* 📚 书籍来自 [殆知阁](http://www.daizhige.org/)，只为了便于展示翻译，喜欢请访问网站，书籍[github文件链接](https://github.com/garychowcmu/daizhigev20)
""")

USER_ID = st.secrets["USER_ID"]
SECRET = st.secrets["SECRET"]


@st.cache
def get_maps():
    file_obj_hash_map = dict(file_df[["filepath", "obj_hash"]].values)
    file_size_map = dict(file_df[["filepath", "fsize"]].values)
    return file_obj_hash_map, file_size_map


file_obj_hash_map, file_size_map = get_maps()


def show_file_size(size: int):
    if size < 1024:
        return f"{size} B"
    elif size < 1024*1024:
        return f"{size//1024} KB"
    else:
        return f"{size/1024//1024} MB"


def fetch_file(path):
    # reading from local path first
    if (Path("data")/path).exists():
        with open(Path("data")/path, "r") as f:
            return f.read()

    # read from github api
    obj_hash = file_obj_hash_map[path]
    auth = HTTPBasicAuth(USER_ID, SECRET)
    url = f"https://api.github.com/repos/garychowcmu/daizhigev20/git/blobs/{obj_hash}"
    r = requests.get(url, auth=auth)
    if r.status_code == 200:
        data = r.json()
        content = base64.b64decode(data['content']).decode('utf-8')
        return content
    else:
        r.raise_for_status()


def fetch_from_df(sub_paths: str = ""):
    sub_df = file_df.copy()
    for idx, step in enumerate(sub_paths):
        sub_df.query(f"col_{idx} == '{step}'", inplace=True)
        if len(sub_df) == 0:
            return None
    return list(sub_df[f"col_{len(sub_paths)}"].unique())


# root_data = fetch_from_github()
if 'pathway' in st.session_state:
    pass
else:
    st.session_state.pathway = []

path_text = col1.text("/".join(st.session_state.pathway))

def reset_path():
    print("before rooting")
    print("/".join(st.session_state.pathway))
    st.session_state.pathway = []
    path_text.text(st.session_state.pathway)

if col1.button("回到根目录"):
    reset_path()

def display_tree(sub_list):
    dropdown = col1.selectbox("【选书】", options=sub_list)
    if col1.button(f'【确定{len(st.session_state.pathway)+1}】'):
        st.session_state.pathway.append(dropdown)
        if dropdown.endswith('.txt'):
            filepath = "/".join(st.session_state.pathway)
            file_size = file_size_map[filepath]
            col2.write(
                f"loading file:{filepath},({show_file_size(file_size)})")

            # if file size is too large, we will not load it
            if file_size > 3*1024*1024:
                urlpath = filepath.replace(".txt",".html")
                dzg = f"http://www.daizhige.org/{urlpath}"
                st.markdown(f"文件太大，[前往殆知阁页面]({dzg}), 或挑挑其他的书吧")
                reset_path()
                return None
            path_text.text(filepath)
            text = fetch_file(filepath)
            # set y scroll markdown
            col2.markdown(f"""```{text}```""", )
            reset_path()

        else:
            sub_list = fetch_from_df(
                st.session_state.pathway)
            path_text.text("/".join(st.session_state.pathway))
            display_tree(sub_list)


display_tree(fetch_from_df(st.session_state.pathway))

cc = st.text_area("【输入文本】", height=150)

if st.button("【翻译】"):
    if cc:
        if len(cc)>168:
            st.write(f"句子太长，最多168个字符")
        else:
            st.markdown(f"""```{inference(cc)}```""")
    else:
        st.write("请输入文本")