Create app.py

app.py

@@ -0,0 +1,39 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForTokenClassification,pipeline 
+
+tokenizer = AutoTokenizer.from_pretrained("dbmdz/electra-large-discriminator-finetuned-conll03-english")
+
+model = AutoModelForTokenClassification.from_pretrained("dbmdz/electra-large-discriminator-finetuned-conll03-english")
+
+
+
+ner_pipeline = pipeline("ner",model=model, 
+                tokenizer=tokenizer)
+
+examples = [
+    "where did Wandobire's laptop come from, was it africa or uganda?",
+]
+
+examples_2 = [
+    "The Intern was oriented on ICT setup and Infrastructure of Soroti University, drafted workplan and started off the Internship. Simon was encouraged to take the Internship seriously as there was a lot to learn.",
+]
+
+examples_3 = [
+    "Partially done, expected a better result based on Steven's experienced. More effort needed ...",
+]
+
+
+
+
+def ner_electra(text):
+    output = ner_pipeline(text)
+    return {"text": text, "entities": output}
+
+
+gr.Interface(ner_electra,
+             gr.Textbox(placeholder="Enter sentence here..."), 
+             gr.HighlightedText(),
+             examples=[[examples],[examples_2],[examples_3],],
+    title="Comparative Natural Entity Recognition Model by Brian Joram Wandobire",
+    description="takes in a comment as an input and outputs the Entities",
+             ).launch()