Create app.py

app.py

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+# code is adopted from https://huggingface.co/spaces/flair/model_demo/blob/main/app.py and modfied to fit the purpose of this project.
+import spacy.displacy
+import streamlit as st
+from flair.nn import Classifier
+from flair.splitter import SegtokSentenceSplitter
+from colorhash import ColorHash
+
+# st.title("Flair NER Demo")
+st.set_page_config(layout="centered")
+
+# Block 2: Users can input text
+st.subheader("Input your text here")
+input_text = st.text_area('Write or Paste Text Below',
+                          value='May visited the Eiffel Tower in Paris last May.\n\n'
+                                'There she ran across a sign in German that read: "Dirk liebt den Eiffelturm"',
+                          height=128,
+                          max_chars=None,
+                          label_visibility="collapsed")
+
+
+@st.cache_resource
+def get_model():
+    return Classifier.load('pos')
+
+
+def get_html(html: str):
+    WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem">{}</div>"""
+    html = html.replace("\n", " ")
+    return WRAPPER.format(html)
+
+
+def color_variant(hex_color, brightness_offset=1):
+    """ takes a color like #87c95f and produces a lighter or darker variant
+    taken from: https://chase-seibert.github.io/blog/2011/07/29/python-calculate-lighterdarker-rgb-colors.html
+    """
+    if len(hex_color) != 7:
+        raise Exception("Passed %s into color_variant(), needs to be in #87c95f format." % hex_color)
+    rgb_hex = [hex_color[x:x + 2] for x in [1, 3, 5]]
+    new_rgb_int = [int(hex_value, 16) + brightness_offset for hex_value in rgb_hex]
+    new_rgb_int = [min([255, max([0, i])]) for i in new_rgb_int]  # make sure new values are between 0 and 255
+    # hex() produces "0x88", we want just "88"
+    return "#" + "".join([hex(i)[2:] for i in new_rgb_int])
+
+
+# Block 3: Output is displayed
+button_clicked = st.button("**Click here** to tag the input text", key=None)
+
+if button_clicked:
+    splitter = SegtokSentenceSplitter()
+    # TODO: perhaps truncate input_text
+    sentences = splitter.split(input_text)
+
+    # get the model and predict
+    model = get_model()
+    model.predict(sentences)
+
+    spacy_display = {"ents": [], "text": input_text, "title": None}
+
+    predicted_labels = set()
+    for sentence in sentences:
+        for prediction in sentence.get_labels():
+            entity_fields = {
+                "start": prediction.data_point.start_position + sentence.start_position,
+                "end": prediction.data_point.end_position + sentence.start_position,
+                "label": prediction.value,
+            }
+
+            spacy_display["ents"].append(entity_fields)
+            predicted_labels.add(entity_fields["label"])
+
+    # create colors for each label
+    colors = {}
+    for label in predicted_labels:
+        colors[label] = color_variant(ColorHash(label).hex, brightness_offset=85)
+
+    # use displacy to render
+    html = spacy.displacy.render(spacy_display,
+                                 style="ent",
+                                 minify=True,
+                                 manual=True,
+                                 options={
+                                     "colors": colors,
+                                 },
+                                 )
+    style = "<style>mark.entity { display: inline-block }</style>"
+    st.subheader("Tagged text")
+    st.write(f"{style}{get_html(html)}", unsafe_allow_html=True)