Create app.py

app.py

@@ -0,0 +1,61 @@
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+import gradio as gr
+
+tokenizer = AutoTokenizer.from_pretrained("valurank/bert-base-NER")
+model = AutoModelForTokenClassification.from_pretrained("valurank/bert-base-NER")
+nlp = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
+
+def rename_group(output_list):
+  final_output = []
+
+  for output in output_list:
+    output["entity"] = output["entity_group"]
+    del output["entity_group"]
+
+    final_output.append(output)
+    
+  return final_output
+
+def remove_prefix(word, prefix):
+    if prefix in word:
+        return word.split(prefix, 1)[1]
+    return " " + word
+
+def join_results(results):
+    joined_results = []
+    
+    for result in results:
+        if "##" in result["word"] and joined_results:
+            joined_results[-1]["end"] = result["end"]
+            joined_results[-1]["word"] += remove_prefix(result["word"], "##")
+            joined_results[-1]["score"] = min(joined_results[-1]["score"], result["score"])
+        else:
+            joined_results.append(result)
+            
+    return joined_results
+
+examples = [
+    """ Texas A&M professor used chatbot chatbot to assess students' grades.
+    The OpenAI chatbot is actually called ChatGPT and claims to have written every paper written by the bot.
+    The bot isn’t made to detect material composed by AI, or even material produced by itself.
+    Texas A&M University-Commerce said they are investigating the incident and developing policies related to AI in the classroom.
+    The university denied that anyone had received a failing grade.
+    The school also confirmed that several students had been cleared of any academic dishonesty.
+    The use of AI in coursework is a rapidly changing issue that confronts all learning institutions."""
+]
+
+def ner(text):
+    output = nlp(text)
+    output = join_results(output)
+    output = rename_group(output)
+    
+    return {"text": text, "entities": output}    
+
+demo = gr.Interface(ner,
+             gr.Textbox(placeholder="Enter sentence here..."), 
+             gr.HighlightedText(),
+             examples=examples)
+
+if __name__ == '__main__':
+  demo.launch(debug=True)