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import time |
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import streamlit as st |
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from annotated_text import annotated_text |
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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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colors = {'DET': '#b9d9a6', 'NFP': '#eddc92', 'ADJFP': '#95e9d7', 'AUX': '#e797db', 'VPPMS': '#9ff48b', 'ADV': '#ed92b4', 'PREP': '#decfa1', 'PDEMMS': '#ada7d7', 'NMS': '#85fad8', 'COSUB': '#8ba4f4', 'PINDMS': '#e7a498', 'PPOBJMS': '#e5c79a', 'VERB': '#eb94b6', 'DETFS': '#e698ae', 'NFS': '#d9d1a6', 'YPFOR': '#96e89f', 'VPPFS': '#e698c6', 'PUNCT': '#ddbfa2', 'DETMS': '#f788cd', 'PROPN': '#f19c8d', 'ADJMS': '#8ed5f0', 'PPER3FS': '#c4d8a6', 'ADJFS': '#e39bdc', 'COCO': '#8df1e2', 'NMP': '#d7f787', 'PREL': '#f986f0', 'PPER1S': '#878df8', 'ADJMP': '#83fe80', 'VPPMP': '#a6d8c9', 'DINTMS': '#d9a6cc', 'PPER3MS': '#a1deda', 'PPER3MP': '#8fefe1', 'PREF': '#e3c79b', 'ADJ': '#fb81fe', 'DINTFS': '#d5fe81', 'CHIF': '#8084ff', 'XFAMIL': '#dd80fe', 'PRELFS': '#9ce3e3', 'SYM': '#9fbddf', 'NOUN': '#dea1b5', 'MOTINC': '#93b8ec', 'PINDFS': '#f787a5', 'PPOBJMP': '#dca3d2', 'NUM': '#b2e897', 'PREFP': '#e39cd0', 'PDEMFS': '#d8a7cb', 'VPPFP': '#83d9fb', 'PPER3FP': '#a1ddaa', 'PPOBJFS': '#e9ca95', 'PINDMP': '#e897e3', 'PRON': '#e29dcc', 'PPOBJFP': '#86f9dc', 'PART': '#aa96e8', 'PDEMMP': '#b2d7a8', 'PRELMS': '#e39bde', 'PDEMFP': '#b1e599', 'PRELFP': '#bbe39b', 'INTJ': '#bde996', 'PREFS': '#b39be4', 'PINDFP': '#e2e897', 'PRELMP': '#a5c0da', 'PINTFS': '#ceff80', 'PPER2S': '#d5a2dd', 'VPPRE': '#e78af4', '<START>': '#e6a899', '<STOP>': '#9adde5'} |
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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 [{ "texte": t, "étiquette": l } for t, l in zip(texts, labels)] |
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def getAnnotatedFromPOS(texts, labels): |
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return [(t,l,colors[l]) for t, l in zip(texts, labels)] |
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def main(): |
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st.title("🥖 Étiqueteur morphosyntaxique étendu pour le français") |
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checkpoint = st.selectbox("Choix du modèle", 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="Texte", |
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value=default_text, |
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) |
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start = None |
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if st.button("🧠 Calculer"): |
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start = time.time() |
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with st.spinner("Calcul des étiquettes morphosyntaxiques en cours... 🔍"): |
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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("Étiquettes:") |
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anns = getAnnotatedFromPOS(texts, labels) |
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annotated_text(*anns) |
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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("Une erreur s'est produite!" + str(e)) |
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st.stop() |
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st.write("---") |
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st.markdown( |
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"Construit par [Yanis Labrak](https://www.linkedin.com/in/yanis-labrak-8a7412145/) & [Richard Dufour](https://cv.archives-ouvertes.fr/richard-dufour) avec [FlairNLP](https://github.com/flairNLP/flair) 🚀" |
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) |
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st.markdown( |
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"_Ce travail a été soutenu financièrement par [Zenidoc](https://zenidoc.fr/)_" |
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) |
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if start is not None: |
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st.text(f"La prédiction a prise {time.time() - start:.2f}s") |
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if __name__ == "__main__": |
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main() |
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