Adding a version 1 of demo for testing

app.py

@@ -0,0 +1,126 @@
+"""Demo for NER4OPT."""
+import re
+import warnings
+
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+import spacy
+from spacy import displacy
+from spacy.training import iob_to_biluo, biluo_tags_to_offsets
+from spacy.tokenizer import Tokenizer
+
+import streamlit as st
+
+from simpletransformers.ner import NERModel, NERArgs
+
+HTML_WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem">{}</div>"""
+
+
+@st.cache_resource
+def load_models():
+    """Load custom built NER4OPT model."""
+    custom_labels = [
+        'O',
+        'B-CONST_DIR',
+        'I-CONST_DIR',
+        'B-LIMIT',
+        'I-LIMIT',
+        'B-VAR',
+        'I-VAR',
+        'B-OBJ_DIR',
+        'B-OBJ_NAME',
+        'I-OBJ_NAME',
+        'B-PARAM',
+        'I-PARAM',
+    ]
+    # # create model
+    model_args = NERArgs()
+    model_args.use_early_stopping = True
+    model_args.early_stopping_delta = 0.01
+    model_args.early_stopping_metric = "eval_loss"
+    model_args.early_stopping_metric_minimize = False
+    model_args.early_stopping_patience = 5
+    model_args.evaluate_during_training_steps = 2000
+    model_args.overwrite_output_dir = True
+    model_args.reprocess_input_data = True
+    model_args.num_train_epochs = 11
+    model_args.adafactor_beta1 = 0.9
+    model_args.weight_decay = 0.01
+    model_args.max_seq_length = 512
+    model_args.learning_rate = 4e-5
+    model_args.train_batch_size = 1
+    model_args.eval_batch_size = 1
+    model_args.manual_seed = 123456789
+    model_args.output_dir = "trained_transformer_model"
+    model_args.use_cuda = True
+    model_args.use_multiprocessing = False
+    model = NERModel("roberta",
+                     "skadio/ner4opt-roberta",
+                     labels=custom_labels,
+                     use_cuda=True,
+                     args=model_args)
+    spacy_model = spacy.load("en_core_web_sm")
+    spacy_model.tokenizer = Tokenizer(spacy_model.vocab,
+                                      token_match=re.compile(r'\S+').match)
+    spacy_blank_model = spacy.blank('en')
+    spacy_blank_model.tokenizer = Tokenizer(
+        spacy_blank_model.vocab, token_match=re.compile(r'\S+').match)
+    return model, spacy_model, spacy_blank_model
+
+
+def main():
+
+    st.sidebar.title("""
+        NER4OPT Demo: \nFull code will be available at https://github.com/skadio/Ner4Opt
+        """)
+
+    text = st.text_area(
+        "Text",
+        "Cautious Asset Investment has a total of $ 150,000 to manage and decides to invest it in money market fund , which yields a 2 % return as well as in foreign bonds , which gives and average rate of return of 10.2 % . Internal policies require PAI to diversify the asset allocation so that the minimum investment in money market fund is 40 % of the total investment . Due to the risk of default of foreign countries , no more than 40 % of the total investment should be allocated to foreign bonds . How much should the Cautious Asset Investment allocate in each asset so as to maximize its average return ?"
+    )
+    if text == "":
+        st.write("Please write a valid sentence.")
+    model, spacy_model, spacy_blank_model = load_models()
+
+    # Augmented Text
+    spacy_doc = spacy_model(text)
+    if len(list(spacy_doc.sents)) >= 2:
+        last_two_sentences = ' '.join(
+            [item.text for item in list(spacy_doc.sents)[-2::]])
+    else:
+        last_two_sentences = ' '.join(
+            [item.text for item in list(spacy_doc.sents)[-1::]])
+    to_skip_count = len(last_two_sentences.split())
+    augmented_sent = last_two_sentences + " " + text
+
+    if st.button("Get Named Entities"):
+        predictions, raw_outputs = model.predict([augmented_sent],
+                                                 split_on_space=True)
+        transformer_predictions = [
+            list(val.values())[0] for val in predictions[0]
+        ]
+        transformer_predictions = transformer_predictions[to_skip_count::]
+        biluo_tags = iob_to_biluo(transformer_predictions)
+        doc = spacy_blank_model.make_doc(text)
+        entities = biluo_tags_to_offsets(doc, biluo_tags)
+        entities_formatted = []
+        for tag in entities:
+            entities_formatted.append({
+                "start": tag[0],
+                "end": tag[1],
+                "label": tag[2],
+                "score": 1.0
+            })
+        ner_for_display = [{
+            "text": doc.text,
+            "ents": entities_formatted,
+            "title": None
+        }]
+        st.title("Named Entity Results")
+        html_ner = displacy.render(ner_for_display, style="ent", manual=True)
+        html_ner = html_ner.replace("\n", " ")
+        st.write(HTML_WRAPPER.format(html_ner), unsafe_allow_html=True)
+
+
+if __name__ == '__main__':
+    main()

requirements.txt

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+streamlit
+simpletransformers==0.63.7
+spacy==3.2.0