from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoConfig
import re
import gradio as gr
from torch.nn import functional as F
import seaborn
import matplotlib
import platform
from transformers.file_utils import ModelOutput

if platform.system() == "Darwin":

    print("MacOS")
    matplotlib.use('Agg')

import matplotlib.pyplot as plt
import io
from PIL import Image
import matplotlib.font_manager as fm

# global var

MODEL_NAME = 'yseop/FNP_T5_D2T_complete'
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
config = AutoConfig.from_pretrained(MODEL_NAME)

MODEL_BUF = {
    "name": MODEL_NAME,
    "tokenizer": tokenizer,
    "model": model,
    "config": config
}

font_dir = ['./']

for font in fm.findSystemFonts(font_dir):
    print(font)
    fm.fontManager.addfont(font)

plt.rcParams["font.family"] = 'NanumGothicCoding'

def change_model_name(name):

    MODEL_BUF["name"] = name
    MODEL_BUF["tokenizer"] = AutoTokenizer.from_pretrained(name)
    MODEL_BUF["model"] = AutoModelForSeq2SeqLM.from_pretrained(name)
    MODEL_BUF["config"] = AutoConfig.from_pretrained(name)


def generate(model_name, text):

    if model_name != MODEL_NAME:
        change_model_name(model_name)
        
    tokenizer = MODEL_BUF["tokenizer"]
    model = MODEL_BUF["model"]
    config = MODEL_BUF["config"]
    
    model.eval()
    input_ids = tokenizer.encode("AFA:{}".format(text), return_tensors="pt")
    outputs = model.generate(input_ids, max_length=200, num_beams=2, repetition_penalty=2.5, top_k=50, top_p=0.98, length_penalty=1.0, early_stopping=True)
    output = tokenizer.decode(outputs[0])
    #return ".".join(output.split(".")[:-1]) + "."
    sent = ".".join(output.split(".")[:-1]) + "."
    return re.match(r'<pad> ([^<>]*)', sent).group(1)


output_text = gr.outputs.Textbox()


if __name__ == '__main__':

    text = ['Group profit | valIs | € 115.7 million && € 115.7 million | dTime | in 2019']
    example = [['Net income | valIs | $48 million && $48 million | diGeo | in France && Net income | jPose | the interest rate && the interest rate | valIs | 0.6%'],
    ['The retirement age | incBy | 7 years && 7 years | cTime | 2018 && The retirement age | jpose | life expectancy && life expectancy | incBy | 10 years'],
    ['sales | incBy | € 115.7 million && € 115.7 million | dTime | in 2019 && € 115.7 million | diGeo | Europe']]

    model_name_list = [

        'yseop/FNP_T5_D2T_complete',
        'yseop/FNP_T5_D2T_simple'

    ]


app = gr.Interface(

    fn=generate,

    inputs=[gr.inputs.Dropdown(model_name_list, label="Model Name"), 'text'], outputs=output_text, 

    examples = [[MODEL_BUF["name"], text]],

    title="FTG",

    description="Financial Text Generation"

    )

app.launch(inline=False)