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| from transformers import FSMTForConditionalGeneration, FSMTTokenizer | |
| from transformers import AutoModelForSequenceClassification | |
| from transformers import AutoTokenizer | |
| from langdetect import detect | |
| from newspaper import Article | |
| from PIL import Image | |
| import streamlit as st | |
| import requests | |
| import torch | |
| st.markdown("## Prediction of Fakeness by Given URL") | |
| background = Image.open('logo.jpg') | |
| st.image(background) | |
| st.markdown(f"### Article URL") | |
| text = st.text_area("Insert some url here", | |
| value="https://en.globes.co.il/en/article-yandex-looks-to-expand-activities-in-israel-1001406519") | |
| def get_models_and_tokenizers(): | |
| model_name = 'distilbert-base-uncased-finetuned-sst-2-english' | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2) | |
| model.eval() | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model.load_state_dict(torch.load('./model.pth', map_location='cpu')) | |
| model_name_translator = "facebook/wmt19-ru-en" | |
| tokenizer_translator = FSMTTokenizer.from_pretrained(model_name_translator) | |
| model_translator = FSMTForConditionalGeneration.from_pretrained(model_name_translator) | |
| model_translator.eval() | |
| return model, tokenizer, model_translator, tokenizer_translator | |
| model, tokenizer, model_translator, tokenizer_translator = get_models_and_tokenizers() | |
| article = Article(text) | |
| article.download() | |
| article.parse() | |
| concated_text = article.title + '. ' + article.text | |
| lang = detect(concated_text) | |
| st.markdown(f"### Language detection") | |
| if lang == 'ru': | |
| with st.spinner('Waiting for translation'): | |
| st.markdown(f"The language of this article is {lang.upper()} so we translated it!") | |
| input_ids = tokenizer_translator.encode(concated_text, | |
| return_tensors="pt", max_length=512, truncation=True) | |
| outputs = model_translator.generate(input_ids) | |
| decoded = tokenizer_translator.decode(outputs[0], skip_special_tokens=True) | |
| st.markdown("### Translated Text") | |
| st.markdown(f"{decoded[:777]}") | |
| concated_text = decoded | |
| else: | |
| st.markdown(f"The language of this article for sure: {lang.upper()}!") | |
| st.markdown("### Extracted Text") | |
| st.markdown(f"{concated_text[:777]}") | |
| tokens_info = tokenizer(concated_text, truncation=True, return_tensors="pt") | |
| with torch.no_grad(): | |
| raw_predictions = model(**tokens_info) | |
| softmaxed = int(torch.nn.functional.softmax(raw_predictions.logits[0], dim=0)[1] * 100) | |
| st.markdown("### Fakeness Prediction") | |
| st.progress(softmaxed) | |
| st.markdown(f"This is fake by **{softmaxed}%**!") | |
| if (softmaxed > 70): | |
| st.error('We would not trust this text!') | |
| elif (softmaxed > 40): | |
| st.warning('We are not sure about this text!') | |
| else: | |
| st.success('We would trust this text!') |