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| 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_svm_model_2_classes_gpt_chatgpt_detection_tfidf_bg_0.886_F1_score.pkl', 'rb') as f: | |
| st.session_state.tfidf_vectorizer_disinformation = pickle.load(f) | |
| with open('models/tfidf_vectorizer_untrue_inform_detection_tfidf_bg_0.96_F1_score.pkl', 'rb') as f: | |
| st.session_state.tfidf_vectorizer_untrue_inf = pickle.load(f) | |
| with open('models/svm_model_2_classes_gpt_chatgpt_detection_tfidf_bg_0.886_F1_score.pkl', 'rb') as f: | |
| st.session_state.gpt_detector = pickle.load(f) | |
| with open('models/SVM_model_untrue_inform_detection_tfidf_bg_0.96_F1_score.pkl', 'rb') as f: | |
| st.session_state.untrue_detector = pickle.load(f) | |
| st.session_state.bert = pipeline(task="text-classification", | |
| model=BertForSequenceClassification.from_pretrained("TRACES/private-bert", use_auth_token=os.environ['ACCESS_TOKEN'], num_labels=2), | |
| tokenizer=AutoTokenizer.from_pretrained("TRACES/private-bert", use_auth_token=os.environ['ACCESS_TOKEN'])) | |
| 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([ | |
| 'gpt_detector_result' not in st.session_state, | |
| 'untrue_detector_result' not in st.session_state, | |
| 'bert_result' not in st.session_state | |
| ]): | |
| st.session_state.gpt_detector_result = '' | |
| st.session_state.gpt_detector_probability = [1, 0] | |
| st.session_state.untrue_detector_result = '' | |
| st.session_state.untrue_detector_probability = 1 | |
| st.session_state.bert_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]): | |
| user_tfidf_disinformation = st.session_state.tfidf_vectorizer_disinformation.transform([user_input]) | |
| st.session_state.gpt_detector_result = st.session_state.gpt_detector.predict(user_tfidf_disinformation)[0] | |
| st.session_state.gpt_detector_probability = st.session_state.gpt_detector.predict_proba(user_tfidf_disinformation)[0] | |
| 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_result = st.session_state.bert(user_input) | |
| if st.session_state.gpt_detector_result == 1: | |
| st.warning(content['gpt_getect_yes'][st.session_state.lang] + | |
| str(round(st.session_state.gpt_detector_probability[1] * 100, 2)) + | |
| content['gpt_yes_proba'][st.session_state.lang], icon="⚠️") | |
| else: | |
| st.success(content['gpt_getect_no'][st.session_state.lang] + | |
| str(round(st.session_state.gpt_detector_probability[0] * 100, 2)) + | |
| content['gpt_no_proba'][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_result[0]['label'] == 'LABEL_1': | |
| st.warning(content['bert_yes_1'][st.session_state.lang] + | |
| str(round(st.session_state.bert_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_result[0]['score'] * 100, 2)) + | |
| content['bert_no_2'][st.session_state.lang], icon="✅") | |
| 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() | |