main.py

import json
import os

import streamlit as st
import pickle

from transformers import AutoTokenizer, BertForSequenceClassification, pipeline
from sklearn.feature_extraction.text import TfidfVectorizer


def load_models():
    st.session_state.loaded = True

    # with open('models/tfidf_vectorizer_untrue_inform_detection_tfidf_bg_0.96_F1_score_3Y_N_Q1_082023.pkl', 'rb') as f:
    #     st.session_state.tfidf_vectorizer_untrue_inf = pickle.load(f)

    # with open('models/SVM_model_untrue_inform_detection_tfidf_bg_0.96_F1_score_3Y_N_Q1_082023.pkl', 'rb') as f:
    #     st.session_state.untrue_detector = pickle.load(f)

    st.session_state.bert_disinfo = pipeline(task="text-classification",
                                     model=BertForSequenceClassification.from_pretrained("usmiva/bert-desinform-bg", num_labels=2),
                                     tokenizer=AutoTokenizer.from_pretrained("usmiva/bert-desinform-bg"))
    st.session_state.bert_gpt = pipeline(task="text-classification",
                                     model=BertForSequenceClassification.from_pretrained("usmiva/bert-deepfake-bg", num_labels=2),
                                     tokenizer=AutoTokenizer.from_pretrained("usmiva/bert-deepfake-bg"))

    st.session_state.emotions = pipeline(task="text-classification",
                                     model=BertForSequenceClassification.from_pretrained("TRACES/emotions", use_auth_token=os.environ['ACCESS_TOKEN2'],  num_labels=11),
                                     tokenizer=AutoTokenizer.from_pretrained("usmiva/bert-web-bg"))



def load_content():
    with open('resource/page_content.json', encoding='utf8') as json_file:
        return json.load(json_file)


def switch_lang(lang):
    if 'lang' in st.session_state:
        if lang == 'bg':
            st.session_state.lang = 'bg'
        else:
            st.session_state.lang = 'en'


if 'lang' not in st.session_state:
    st.session_state.lang = 'bg'

if all([
    'bert_gpt_result' not in st.session_state,
    # 'untrue_detector_result' not in st.session_state,
    'bert_disinfo_result' not in st.session_state,
    'emotions_result' not in st.session_state
    ]):
    st.session_state.bert_gpt_result = [{'label': '', 'score': 1}]

    # st.session_state.untrue_detector_result = ''
    # st.session_state.untrue_detector_probability = 1
    
    st.session_state.bert_disinfo_result = [{'label': '', 'score': 1}]
        
    st.session_state.emotions_result = [{'label': '', 'score': 1}]    

content = load_content()
if 'loaded' not in st.session_state:
    load_models()

#######################################################################################################################

st.title(content['title'][st.session_state.lang])

col1, col2, col3 = st.columns([1, 1, 10])
with col1:
    st.button(
        label='EN',
        key='en',
        on_click=switch_lang,
        args=['en']
    )
with col2:
    st.button(
        label='BG',
        key='bg',
        on_click=switch_lang,
        args=['bg']
    )

if 'agree' not in st.session_state:
    st.session_state.agree = False

if st.session_state.agree:
    tab_tool, tab_terms = st.tabs([content['tab_tool'][st.session_state.lang], content['tab_terms'][st.session_state.lang]])

    with tab_tool:
        user_input = st.text_area(content['textbox_title'][st.session_state.lang],
                                  content['text_placeholder'][st.session_state.lang]).strip('\n')
    
        if st.button(content['analyze_button'][st.session_state.lang]):
            st.session_state.bert_gpt_result = st.session_state.bert_gpt(user_input)
            
            # user_tfidf_untrue_inf = st.session_state.tfidf_vectorizer_untrue_inf.transform([user_input])
            # st.session_state.untrue_detector_result = st.session_state.untrue_detector.predict(user_tfidf_untrue_inf)[0]
            # st.session_state.untrue_detector_probability = st.session_state.untrue_detector.predict_proba(user_tfidf_untrue_inf)[0]
            # st.session_state.untrue_detector_probability = max(st.session_state.untrue_detector_probability[0], st.session_state.untrue_detector_probability[1]) 

            st.session_state.bert_disinfo_result = st.session_state.bert_disinfo(user_input)
            
            st.session_state.emotions_result = st.session_state.emotions(user_input)

            

        if st.session_state.bert_gpt_result[0]['label'] == 'LABEL_1':
            st.warning(content['bert_gpt'][st.session_state.lang] +
                       str(round(st.session_state.bert_gpt_result[0]['score'] * 100, 2)) +
                       content['bert_gpt_prob'][st.session_state.lang], icon = "⚠️")
        else:
            st.success(content['bert_human'][st.session_state.lang] +
                       str(round(st.session_state.bert_gpt_result[0]['score'] * 100, 2)) +
                       content['bert_human_prob'][st.session_state.lang], icon="✅")
        
        # if st.session_state.untrue_detector_result == 0:
        #     st.warning(content['untrue_getect_yes'][st.session_state.lang] +
        #                str(round(st.session_state.untrue_detector_probability * 100, 2)) +
        #                content['untrue_yes_proba'][st.session_state.lang], icon="⚠️")
        # else:
        #     st.success(content['untrue_getect_no'][st.session_state.lang] +
        #                str(round(st.session_state.untrue_detector_probability * 100, 2)) +
        #                content['untrue_no_proba'][st.session_state.lang], icon="✅")
    
        if st.session_state.bert_disinfo_result[0]['label'] == 'LABEL_1':
            st.warning(content['bert_yes_1'][st.session_state.lang] +
                       str(round(st.session_state.bert_disinfo_result[0]['score'] * 100, 2)) +
                       content['bert_yes_2'][st.session_state.lang], icon = "⚠️")
        else:
            st.success(content['bert_no_1'][st.session_state.lang] +
                       str(round(st.session_state.bert_disinfo_result[0]['score'] * 100, 2)) +
                       content['bert_no_2'][st.session_state.lang], icon="✅")

        if st.session_state.emotions_result[0]['score'] < 0.97:
            st.warning(content['emotions_label_1'][st.session_state.lang] + 
                       str(st.session_state.emotions_result[0]['label']) + 
                       content['emotions_label_2'][st.session_state.lang] +
                       str(round(st.session_state.emotions_result[0]['score'] * 100, 2)) + 
                       content['emotions_label_3'][st.session_state.lang] +
                       content['emotions_label_4'][st.session_state.lang], icon = "⚠️")
        else:
            st.info(content['emotions_label_1'][st.session_state.lang] + 
                       str(st.session_state.emotions_result[0]['label']) + 
                       content['emotions_label_2'][st.session_state.lang] +
                       str(round(st.session_state.emotions_result[0]['score'] * 100, 2)) + 
                       content['emotions_label_3'][st.session_state.lang]+
                       content['emotions_label_5'][st.session_state.lang])

    
        st.info(content['disinformation_definition'][st.session_state.lang], icon="ℹ️")

    with tab_terms:
        st.write(content['disclaimer'][st.session_state.lang])

else:
    st.write(content['disclaimer_title'][st.session_state.lang])
    st.write(content['disclaimer'][st.session_state.lang])
    if st.button(content['disclaimer_agree_text'][st.session_state.lang]):
        st.session_state.agree = True
        st.experimental_rerun()