first commit

app.py

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+import gradio as gr
+import nltk
+nltk.download('omw-1.4')
+from qanom.nominalization_detector import NominalizationDetector
+
+detector = NominalizationDetector()
+
+title = "Nominalization Detection Demo"
+description = f"""This is a demo of QANom's nominalization detection algorithm, 
+            comprised of candidate nominalization extraction followed by a contextualized binary classification model."""
+links = """<p style='text-align: center'>
+<a href='https://github.com/kleinay/QANom' target='_blank'>QANom repo</a>  |  
+<a href='https://huggingface.co/kleinay/nominalization-candidate-classifier' target='_blank'>Model Repo at Huggingface Hub</a>  |  
+<a href='https://www.aclweb.org/anthology/2020.coling-main.274/' target='_blank'>QANom Paper</a>  |  
+</p>"""
+examples = [["The doctor was interested in Luke 's treatment .", True, True, 0.6], 
+            ["the construction of the officer 's building finished right after the beginning of the destruction of the previous construction .", True, True, 0.7]]
+
+def call(sentence: str, return_all_candidates: bool, threshold: float):
+    ret = detector([sentence], return_all_candidates, True, threshold)[0]
+    return ret, ret
+
+iface = gr.Interface(fn=call, 
+                     inputs=[gr.inputs.Textbox(label="Sentence", lines=3), 
+                             gr.inputs.Checkbox(default=True, label="Return all candidates?"),
+                             gr.inputs.Slider(minimum=0., maximum=1., step=0.01, default=0.5, label="Threshold")], 
+                     outputs=[gr.outputs.JSON(label="Model Output"), gr.outputs.JSON(label="Model Output - QASRL")],
+                     title=title,
+                     description=description,
+                     article=links,
+                     examples=examples )
+iface.launch()

requirements.txt

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+qanom>=0.0.11
+torch