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| import streamlit as st | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| import numpy as np | |
| import torch | |
| TOP_N = 5 | |
| def main(): | |
| st.set_page_config( # Alternate names: setup_page, page, layout | |
| layout="centered", # Can be "centered" or "wide". In the future also "dashboard", etc. | |
| initial_sidebar_state="auto", # Can be "auto", "expanded", "collapsed" | |
| page_title="Emoji-motion!", # String or None. Strings get appended with "• Streamlit". | |
| page_icon=None, # String, anything supported by st.image, or None. | |
| ) | |
| st.title('Emoji-motion!') | |
| example_prompts = [ | |
| "it's pretty depressing when u hit pan on ur favourite highlighter", | |
| "After what just happened. In need to smoke.", | |
| "I've never been happier. I'm laying awake as I watch @user sleep. Thanks for making me happy again, babe.", | |
| "@user is the man", | |
| "Поприветствуем моего нового читателя @user", | |
| "сегодня у одной крутой бичи день рождения! @user поздравляю тебя с днем рождения! будь самой-самой счастливой,красота:* море любви тебе", | |
| "Никогда не явствовала себя ужаснее, чем сейчас:( я просто раздавленна", | |
| "Самое ужасное - это ожидание результатов", | |
| "печально что заряд одинаково фигово держится(", | |
| ] | |
| example = st.selectbox("Choose an example", example_prompts) | |
| # Take the message which needs to be processed | |
| message = st.text_area("...or paste some text to see the model's predictions", example) | |
| # st.title(message) | |
| st.text('') | |
| models_to_choose = [ | |
| "amazon-sagemaker-community/xlm-roberta-en-ru-emoji-v2", | |
| "AlekseyDorkin/xlm-roberta-en-ru-emoji" | |
| ] | |
| BASE_MODEL = st.selectbox("Choose a model", models_to_choose) | |
| TOP_N = 5 | |
| def preprocess(text): | |
| new_text = [] | |
| for t in text.split(" "): | |
| t = '@user' if t.startswith('@') and len(t) > 1 else t | |
| t = 'http' if t.startswith('http') else t | |
| new_text.append(t) | |
| return " ".join(new_text) | |
| def load_model(): | |
| tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) | |
| model = AutoModelForSequenceClassification.from_pretrained(BASE_MODEL) | |
| return model, tokenizer | |
| def get_top_emojis(text, top_n=TOP_N): | |
| model, tokenizer = load_model() | |
| preprocessed = preprocess(text) | |
| inputs = tokenizer(preprocessed, return_tensors="pt") | |
| preds = model(**inputs).logits | |
| scores = torch.nn.functional.softmax(preds, dim=-1).detach().numpy() | |
| ranking = np.argsort(scores) | |
| ranking = ranking.squeeze()[::-1][:top_n] | |
| emojis = [model.config.id2label[i] for i in ranking] | |
| return ', '.join(map(str, emojis)) | |
| # Define function to run when submit is clicked | |
| def submit(message): | |
| if len(message) > 0: | |
| emoji = get_top_emojis(message) | |
| html_str = f""" | |
| <style> | |
| p.a {{ | |
| font: 26px Courier; | |
| }} | |
| </style> | |
| <p class="a">{emoji}</p> | |
| """ | |
| st.markdown(html_str, unsafe_allow_html=True) | |
| # st.markdown(emoji) | |
| else: | |
| st.error("The text can't be empty") | |
| # Run algo when submit button is clicked | |
| if st.button('Submit'): | |
| submit(message) | |
| st.text('') | |
| st.markdown( | |
| '''<span style="color:blue; font-size:10px">App created by [@AlekseyDorkin](https://huggingface.co/AlekseyDorkin) | |
| and [@akshay7](https://huggingface.co/akshay7)</span>''', | |
| unsafe_allow_html=True, | |
| ) | |
| if __name__ == "__main__": | |
| main() |