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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import numpy as np
import torch
TOP_N = 5
DEFAULT_MODEL = "amazon-sagemaker-community/xlm-roberta-en-ru-emoji-v2"
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 get_top_emojis(text, tokenizer, model, top_n=TOP_N):
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 '\t'.join(map(str, emojis))
cur_model_name = DEFAULT_MODEL
print("cur_model", cur_model_name)
tokenizer = AutoTokenizer.from_pretrained(cur_model_name)
model = AutoModelForSequenceClassification.from_pretrained(cur_model_name)
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 = [
"Today is going to be awesome!",
"Pity those who don't feel anything at all.",
"I envy people that know love.",
"Nature is so beautiful"]
def main():
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"
]
model_name = st.selectbox("Choose a model", models_to_choose)
if model_name != cur_model_name:
print("reloading model")
cur_model_name = model_name
tokenizer = AutoTokenizer.from_pretrained(cur_model_name)
model = AutoModelForSequenceClassification.from_pretrained(cur_model_name)
# Define function to run when submit is clicked
def submit(message):
if len(message) > 0:
st.header(get_top_emojis(message, tokenizer=tokenizer, model=model))
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() |