Remove rating

app.py

@@ -15,10 +15,10 @@ model = AutoModelForTokenClassification.from_pretrained(current_model)
 
 plt.switch_backend("Agg")
 
-examples = {}
+examples = []
 with open("examples.json", "r") as f:
     content = json.load(f)
-    examples = {x["text"]: x["label"] for x in content}
+    examples = [f"{x['label']}: {x['text']}" for x in content]
 
 pipe = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
 
@@ -47,26 +47,24 @@ def run_ner(text):
             for x in raw
         ],
     }
-    label = examples.get(text, None)
     grouped = Counter((x["entity_group"] for x in raw))
     rows = [[k, v] for k, v in grouped.items()]
     figure = plot_to_figure(grouped)
-    return label, ner_content, rows, figure
+    return ner_content, rows, figure
 
 
 with gr.Blocks() as demo:
     note = gr.Textbox(label="Note text")
     submit = gr.Button("Submit")
     # with gr.Accordion("Examples", open=False):
-    example_dropdown = gr.Dropdown(label="Examples", choices=list(examples.keys()))
+    example_dropdown = gr.Dropdown(label="Examples", choices=examples)
     example_dropdown.change(
         lambda x: gr.Textbox.update(value=x), inputs=example_dropdown, outputs=note
     )
-    rating = gr.Label(label="Given rating")
     highlight = gr.HighlightedText(label="NER", combine_adjacent=True)
     table = gr.Dataframe(headers=["Entity", "Count"])
     plot = gr.Plot(label="Bar")
-    submit.click(run_ner, [note], [rating, highlight, table, plot])
-    note.submit(run_ner, [note], [rating, highlight, table, plot])
+    submit.click(run_ner, [note], [highlight, table, plot])
+    note.submit(run_ner, [note], [highlight, table, plot])
 
 demo.launch()