app.py

app.py

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+import gradio as gr
+from spacy import displacy
+import spacy
+
+med7 = spacy.load("en_core_med7_lg")
+
+def get_med7_ent(text):
+
+    # create distinct colours for labels
+    col_dict = {}
+    seven_colours = ['#e6194B', '#3cb44b', '#ffe119', '#ffd8b1', '#f58231', '#f032e6', '#42d4f4']
+    for label, colour in zip(med7.pipe_labels['ner'], seven_colours):
+        col_dict[label] = colour
+
+    options = {'ents': med7.pipe_labels['ner'], 'colors':col_dict}
+
+#     text = 'A patient was prescribed Magnesium hydroxide 400mg/5ml suspension PO of total 30ml bid for the next 5 days.'
+    doc = med7(text)
+
+#     spacy.displacy.render(doc, style='ent', jupyter=True, options=options)
+
+#     [(ent.text, ent.label_) for ent in doc.ents]
+    
+    html = displacy.render(doc, style="ent", jupyter=True, options=options)
+    return html
+
+exp=["A patient was prescribed Magnesium hydroxide 400mg/5ml suspension PO of total 30ml bid for the next 5 days."]
+
+desc="Med7 — an information extraction model for clinical natural language processing"
+
+inp=gr.inputs.Textbox(lines=5, placeholder=None, default="", label="text to extract Med7 Entities")
+out=gr.outputs.HTML(label=None)
+
+iface = gr.Interface(fn=get_med7_ent, inputs=inp, outputs=out,examples=exp,article=desc,title="Med7",theme="huggingface",layout='horizontal')
+iface.launch()