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import streamlit as st
from pyparsing import empty
from mymodel import CustomBertForSequenceClassification
from transformers import BertTokenizer, Trainer

POSITIVE = 0
NEGATIVE = 1
NEUTRAL = 2

idx_target = {POSITIVE:'positive', NEGATIVE:'negative', NEUTRAL:'neutral'}

g_selected_model_type = None
g_input_text = ' '

def get_model_type(select_model, add_layers):
    if (select_model == "BERT") & (add_layers == True):
        return "klue/bert-base", "bert_layered"
    elif (select_model == "BERT") & (add_layers == False):
        return "klue/bert-base", "bert"
    elif (select_model == "RoBERTa") & (add_layers == True):
        return "klue/roberta-base", "roberta_layered"
    elif (select_model == "RoBERTa") & (add_layers == False):
        return "klue/roberta-base", "roberta"

def show_predict_result(model_type, input_text):
    target = POSITIVE
    show_response_img(target)
    show_response_text(target)

def show_response_img(target = None):
    if target == None:
        st.con3.write("")
    elif target == POSITIVE:
        st.con3.write("웃는 표정")
    elif target == NEGATIVE:
        st.con3.write("우는 표정")
    elif target == NEUTRAL:
        st.con3.write("무덤덤한 표정")

def show_response_text(target = None):
    if target == None:
        st.con4.write("")
    elif target == POSITIVE:
        st.con4.write("긍정적인 반응")
    elif target == NEGATIVE:
        st.con4.write("부정정인 반응")
    elif target == NEUTRAL:
        st.con4.write("무덤덤한 반응")
    

def show_data():
    st.write("data")

def show_api_usage():
    with st.container(border=True):
        st.write("api 사용법")

def tab1_page():
    global g_selected_model_type
    global g_input_text
    st.con1,st.con2 = st.columns([0.3,0.7])
    st.con3,st.con4 = st.columns([0.3,0.7])
    st.con5,empty1 = st.columns([0.9999,0.0001])

    with st.container():
        with st.con1:
            with st.con1.container(border=True):
                selected_model = st.selectbox("예측 모델을 선택하세요.", ["BERT", "RoBERTa"])
                add_layers = st.checkbox('Layer 추가')
                g_selected_model_type = get_model_type(selected_model, add_layers)
                show_predict_result(g_selected_model_type, g_input_text)
        with st.con2:
            with st.con2.container(border=True):
                input_text = st.text_area("input_text")
                submit_button = st.button('확인')
                if submit_button :
                    g_input_text = input_text
                    show_predict_result(g_selected_model_type, g_input_text)
    with st.con3:
        with st.con3.container(border=True):
            show_response_img()
    with st.con4:
        with st.con4.container(border=True):
            show_response_text()
    with st.con5:
        with st.con5.container(border=True):
            show_data()
    with empty1:
        empty()


def tab2_page():
    show_api_usage()


#tokenizer = BertTokenizer()
#model = CustomBertForSequenceClassification()
#trainer = Trainer()
tokenizer = BertTokenizer.from_pretrained("bert_tokenizer_layered")
model = CustomBertForSequenceClassification.from_pretrained("bert_model_layered")
trainer = Trainer().load_model("bert_trainer_layered")



st.title("Semi Project - Sentiment analysis")
#st.subheader("일상 대화를 입력하면 해당 문장이 나타내는 느낌을 표시합니다.")


tab1, tab2 = st.tabs(['동작 확인', 'API'])

with tab1:
    tab1_page()
    
with tab2:
    tab2_page()

	import streamlit as st
	from pyparsing import empty
	from mymodel import CustomBertForSequenceClassification
	from transformers import BertTokenizer, Trainer

	POSITIVE = 0
	NEGATIVE = 1
	NEUTRAL = 2

	idx_target = {POSITIVE:'positive', NEGATIVE:'negative', NEUTRAL:'neutral'}

	g_selected_model_type = None
	g_input_text = ' '

	def get_model_type(select_model, add_layers):
	if (select_model == "BERT") & (add_layers == True):
	return "klue/bert-base", "bert_layered"
	elif (select_model == "BERT") & (add_layers == False):
	return "klue/bert-base", "bert"
	elif (select_model == "RoBERTa") & (add_layers == True):
	return "klue/roberta-base", "roberta_layered"
	elif (select_model == "RoBERTa") & (add_layers == False):
	return "klue/roberta-base", "roberta"

	def show_predict_result(model_type, input_text):
	target = POSITIVE
	show_response_img(target)
	show_response_text(target)

	def show_response_img(target = None):
	if target == None:
	st.con3.write("")
	elif target == POSITIVE:
	st.con3.write("웃는 표정")
	elif target == NEGATIVE:
	st.con3.write("우는 표정")
	elif target == NEUTRAL:
	st.con3.write("무덤덤한 표정")

	def show_response_text(target = None):
	if target == None:
	st.con4.write("")
	elif target == POSITIVE:
	st.con4.write("긍정적인 반응")
	elif target == NEGATIVE:
	st.con4.write("부정정인 반응")
	elif target == NEUTRAL:
	st.con4.write("무덤덤한 반응")


	def show_data():
	st.write("data")

	def show_api_usage():
	with st.container(border=True):
	st.write("api 사용법")

	def tab1_page():
	global g_selected_model_type
	global g_input_text
	st.con1,st.con2 = st.columns([0.3,0.7])
	st.con3,st.con4 = st.columns([0.3,0.7])
	st.con5,empty1 = st.columns([0.9999,0.0001])

	with st.container():
	with st.con1:
	with st.con1.container(border=True):
	selected_model = st.selectbox("예측 모델을 선택하세요.", ["BERT", "RoBERTa"])
	add_layers = st.checkbox('Layer 추가')
	g_selected_model_type = get_model_type(selected_model, add_layers)
	show_predict_result(g_selected_model_type, g_input_text)
	with st.con2:
	with st.con2.container(border=True):
	input_text = st.text_area("input_text")
	submit_button = st.button('확인')
	if submit_button :
	g_input_text = input_text
	show_predict_result(g_selected_model_type, g_input_text)
	with st.con3:
	with st.con3.container(border=True):
	show_response_img()
	with st.con4:
	with st.con4.container(border=True):
	show_response_text()
	with st.con5:
	with st.con5.container(border=True):
	show_data()
	with empty1:
	empty()


	def tab2_page():
	show_api_usage()


	#tokenizer = BertTokenizer()
	#model = CustomBertForSequenceClassification()
	#trainer = Trainer()
	tokenizer = BertTokenizer.from_pretrained("bert_tokenizer_layered")
	model = CustomBertForSequenceClassification.from_pretrained("bert_model_layered")
	trainer = Trainer().load_model("bert_trainer_layered")



	st.title("Semi Project - Sentiment analysis")
	#st.subheader("일상 대화를 입력하면 해당 문장이 나타내는 느낌을 표시합니다.")


	tab1, tab2 = st.tabs(['동작 확인', 'API'])

	with tab1:
	tab1_page()

	with tab2:
	tab2_page()