File size: 2,372 Bytes
64f92ba 049266b 2c5db9f 049266b 2c5db9f 049266b 2c5db9f 049266b 64f92ba 049266b 2c5db9f 049266b e6c30c5 049266b 601925f 049266b 601925f 049266b 64f92ba dbcd12b 64f92ba 2c5db9f 6cda240 64f92ba dbcd12b 8dffc3b 64f92ba 049266b 64f92ba 049266b |
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 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
import json
import time
import streamlit as st
from annotated_text import annotated_text
import matplotlib
from flair.data import Sentence
from flair.models import SequenceTagger
checkpoints = [
"qanastek/pos-french",
]
colors = list(matplotlib.colors.cnames.values())
@st.cache(suppress_st_warning=True, allow_output_mutation=True)
def get_model(model_name):
return SequenceTagger.load(model_name) # Load the model
def getPos(s: Sentence):
texts = []
labels = []
for t in s.tokens:
for label in t.annotation_layers.keys():
texts.append(t.text)
labels.append(t.get_labels(label)[0].value)
return texts, labels
def getDictFromPOS(texts, labels):
return [{ "text": t, "label": l } for t, l in zip(texts, labels)]
def getAnnotatedFromPOS(texts, labels):
return [(t,l,"#8ef") for t, l in zip(texts, labels)]
def main():
st.title("🥖 French Part-Of-Speech Tagging")
checkpoint = st.selectbox("Choose model", checkpoints)
model = get_model(checkpoint)
default_text = "George Washington est allé à Washington"
input_text = st.text_area(
label="Original text",
value=default_text,
)
start = None
if st.button("🧠 Compute"):
start = time.time()
with st.spinner("Search for Part-Of-Speech Tags 🔍"):
# Build Sentence
s = Sentence(input_text)
# predict tags
model.predict(s)
try:
texts, labels = getPos(s)
st.header("Labels:")
st.write(" ".join(labels))
st.header("Labels:")
anns = getAnnotatedFromPOS(texts, labels)
annotated_text(anns)
st.header("JSON:")
st.json(getDictFromPOS(texts, labels))
except Exception as e:
st.error("Some error occured!" + str(e))
st.stop()
st.write("---")
st.markdown(
"Built by [Yanis Labrak](https://www.linkedin.com/in/yanis-labrak-8a7412145/) 🚀"
)
st.markdown(
"_Source code made with [FlairNLP](https://github.com/flairNLP/flair)_"
)
if start is not None:
st.text(f"prediction took {time.time() - start:.2f}s")
if __name__ == "__main__":
main()
|