Update app_interface.py

app_interface.py

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+import gradio as gr
+from Summary import Summary
+from NamedEntity import NER
+
+text2 = ("UK law presumes the author of a copyright work to be the first owner. The law "
+         "also recognises that copyright works may be the product of joint authors and "
+         "co-authors. This means that as soon as the work is created, copyright in the work "
+         "belongs to the person or persons who created it. This is true even if the author "
+         "was hired to make the copyright work under a contract for services, such as a "
+         "wedding photographer. In the absence of an assignment of copyright via contract, "
+         "the wedding photographer retains copyright in the photographs, and the happy couple "
+         "merely gains physical prints of the pictures, and a right to privacy preventing the "
+         "issue, communication or exhibition of copies of the pictures to the public. There "
+         "are two exceptions to the presumption of first ownership: 1. The owner of copyright "
+         "in a work created by an employee in the course of his employment will be the "
+         "employer, unless there is an agreement to the contrary. This applies to literary, "
+         "dramatic, musical or artistic works, and films. It is not sufficient for the work "
+         "to have been created during working hours by an employee for the employer to own the "
+         "work, it must have been created as part of the job that employee was hired to do. "
+         "However, the employer may be able to make some claim to the work if the employee should "
+         "have been working for the employer at the time when he created the work, or if the "
+         "nature or subject matter of the work is so closely related to the type of employment "
+         "that the line between employment and private time becomes blurred. For these reasons "
+         "it is important to address copyright in employment contracts where employees are likely "
+         "to be creating copyright works. 2. Her Majesty the Queen is the first owner of any "
+         "copyright in works created by officers or servants of the Crown. This includes any "
+         "copyright works created by civil servants, such as this copyright notice."
+         )
+
+text = ("Mr Roberts had taken his dog for a walk in Hyde Park at around 9pm. "
+        "He saw a group of people shouting at Stephen - a guy who would shortly "
+        "have his Rolex watch and iPhone stolen by the same group of people "
+        "that had surrounded him. A lady named Fiona Walker was crossing the High "
+        "Street that runs alongside the park. She heard Mr Roberts shout for help "
+        "and called the police to assist.\n\n Constable Robbins arrived after about "
+        "20 minutes by which time the group had dispersed. Mr Roberts was able to "
+        "give a description of the people who had stolen Stephen's Rolex watch and iPhone. "
+        "He said that one of the people was wearing a blue Adidas t-shirt and another "
+        "was wearing a red Arsenal football cap. "
+        "It turned out the gang members hailed from Paddington and Mayfair and used Uber to "
+        "move around the area.\n\n"
+        "The gang leader had to appear at "
+        "the Old Bailey on 1st January 2021. He was sentenced to 3 years in prison "
+        "for robbery and assault by Judge Jennifer Sanderson."
+        )
+
+entity_desc = ("This demo uses the [DSLIM BERT model](https://huggingface.co/dslim/bert-base-NER) "
+               "to identify named entities in a piece of text. It has been trained to recognise "
+               "four types of entities: location (LOC), organisations (ORG), person (PER) and "
+               "Miscellaneous (MISC). The model size is approximately 430Mb. \n\n"
+               "This model is free for commercial use. \n\n"
+               "A [larger model](https://huggingface.co/dslim/bert-large-NER) is also available (~1.3Gb)."
+               )
+
+summary_desc = ("This demo uses the "
+                "[legal-bert-base-uncased model](https://huggingface.co/nlpaueb/legal-bert-base-uncased) "
+                "intended to assist legal NLP research, computational law, and legal technology "
+                "applications. The model size is approximately 500Mb. \n\n "
+                "The model was trained using 12Gb of diverse English legal text across a number of fields. "
+                "This model is free for commercial use. \n\n"
+                )
+
+
+def process_entities(txt_data):
+    ner = NER(txt_data)
+    ner.entity_markdown()
+
+    entity_list = '\n'.join(ner.unique_entities)
+
+    heading = 'Entities highlighted in the original text'
+    output = f'## {heading} \n\n {ner.markdown}'
+
+    return entity_list, output
+
+
+def process_summary(txt_data):
+    summary = Summary(txt_data)
+    result = summary.result
+
+    return 'The Summary'
+
+
+with gr.Blocks() as demo:
+    with gr.Tab('Entities'):
+        gr.Markdown("# Named Entity Recognition")
+        with gr.Accordion("See Details", open=False):
+            gr.Markdown(entity_desc)
+        text_source = gr.Textbox(label="Text to analyse", value=text, lines=10)
+        text_entities = gr.Textbox(label="Unique entities", lines=3)
+        mk_output = gr.Markdown(label="Entities Highlighted", value='Highlighted entities appear here')
+        with gr.Row():
+            btn_sample_entity = gr.Button("Load Sample Text")
+            btn_clear_entity = gr.Button("Clear Data")
+            btn_entities = gr.Button("Get Entities", variant='primary')
+
+        # Event Handlers
+        btn_sample_entity.click(fn=lambda: text, outputs=[text_source])
+        btn_entities.click(fn=process_entities, inputs=[text_source], outputs=[text_entities, mk_output])
+        btn_clear_entity.click(fn=lambda: ('', '', ''), outputs=[text_source, text_entities, mk_output])
+
+demo.launch()