Spaces:
Runtime error
Runtime error
File size: 2,841 Bytes
d1e307b 5c4e25b d1e307b 5c4e25b d1e307b 5c4e25b d1e307b 5c4e25b d1e307b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
import streamlit as st
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import numpy as np
import torch
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 = [
"This space is lit!!",
"Today is going to be awesome!",
"I love Machine Learning",
"Cool cool cool no doubt no doubt no doubt"]
example = st.selectbox("Choose a pre-defined example", example_prompts)
# Take the message which needs to be processed
message = st.text_area('Or type a sentence to see if our AL Algorithm can detect your emotion', example)
# st.title(message)
st.text('')
models_to_choose = ["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)
@st.cache(allow_output_mutation=True, suppress_st_warning=True, show_spinner=False)
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:
st.header(get_top_emojis(message))
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() |