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import json |
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import time |
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import streamlit as st |
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from flair.data import Sentence |
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from flair.models import SequenceTagger |
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checkpoints = [ |
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"qanastek/pos-french", |
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] |
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@st.cache(suppress_st_warning=True, allow_output_mutation=True) |
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def get_model(model_name): |
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return SequenceTagger.load(model_name) |
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def getPos(s: Sentence): |
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texts = [] |
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labels = [] |
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for t in s.tokens: |
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for label in t.annotation_layers.keys(): |
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texts.append(t.text) |
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labels.append(t.get_labels(label)[0].value) |
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return texts, labels |
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def getDictFromPOS(texts, labels): |
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return [{ "text": t, "label": l } for t, l in zip(texts, labels)] |
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def main(): |
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st.title("🥖 French-Part-Of-Speech-Tagging") |
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checkpoint = st.selectbox("Choose model", checkpoints) |
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model = get_model(checkpoint) |
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default_text = "George Washington est allé à Washington" |
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input_text = st.text_area( |
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label="Original text", |
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value=default_text, |
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) |
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start = None |
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if st.button("🧠 Compute"): |
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start = time.time() |
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with st.spinner("Search for Part-Of-Speech Tags 🔍"): |
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s = Sentence(input_text) |
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model.predict(s) |
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try: |
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texts, labels = getPos(s) |
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st.header("Labels:") |
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st.write(" ".join(labels)) |
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st.header("JSON:") |
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st.json(getDictFromPOS(texts, labels)) |
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except Exception as e: |
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st.error("Some error occured!" + str(e)) |
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st.stop() |
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st.write("---") |
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st.markdown( |
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"Built by [Yanis Labrak](https://www.linkedin.com/in/yanis-labrak-8a7412145/) 🚀" |
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) |
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st.markdown( |
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"_Source code made with [FlairNLP](https://github.com/flairNLP/flair)_" |
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) |
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if start is not None: |
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st.text(f"prediction took {time.time() - start:.2f}s") |
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if __name__ == "__main__": |
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main() |
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