Create app.py

app.py

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+import re
+import gradio as gr
+from dataclasses import dataclass
+from prettytable import PrettyTable
+
+from pytorch_ie.annotations import LabeledSpan, BinaryRelation
+from pytorch_ie.auto import AutoPipeline
+from pytorch_ie.core import AnnotationList, annotation_field
+from pytorch_ie.documents import TextDocument
+
+from typing import List
+
+
+@dataclass
+class ExampleDocument(TextDocument):
+    entities: AnnotationList[LabeledSpan] = annotation_field(target="text")
+    relations: AnnotationList[BinaryRelation] = annotation_field(target="entities")
+
+
+ner_model_name_or_path = "pie/example-ner-spanclf-conll03"
+re_model_name_or_path = "DFKI-SLT/relation_classification_tacred_revisited"
+
+ner_pipeline = AutoPipeline.from_pretrained(ner_model_name_or_path, device=-1, num_workers=0)
+re_pipeline = AutoPipeline.from_pretrained(re_model_name_or_path, device=-1, num_workers=0)
+
+
+def predict(text):
+    document = ExampleDocument(text)
+
+    ner_pipeline(document)
+
+    while len(document.entities.predictions) > 0:
+        document.entities.append(document.entities.predictions.pop(0))
+
+    re_pipeline(document)
+
+    t = PrettyTable()
+    t.field_names = ["head", "tail", "relation"]
+    t.align = "l"
+    for relation in document.relations.predictions:
+        t.add_row([str(relation.head), str(relation.tail), relation.label])
+
+    html = t.get_html_string(format=True)
+    html = (
+        "<div style='max-width:100%; max-height:360px; overflow:auto'>"
+        + html
+        + "</div>"
+    )
+    
+    return html
+
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Textbox(
+        lines=5,
+        default="There is still some uncertainty that Musk - also chief executive of electric car maker Tesla and rocket company SpaceX - will pull off his planned buyout.",
+    ),
+    outputs="html",
+)
+iface.launch()