App File Added

app.py

@@ -0,0 +1,14 @@
+import gradio as gr
+from ner import ner
+
+demo = gr.Interface(fn=ner,
+                    inputs=[gr.Textbox(label="Input text", lines=3)],
+                    outputs=[gr.HighlightedText(label='Entities')],
+                    title='Named Entity Recognition with `dslim/bert-base-ner` ',
+                    description='Find entities using the dslim/bert-base-ner model under the hood',
+                    allow_flagging='never',
+                    examples=["My name is Logeswaran, the creator of this HuggingFace Space. I live in India.","Hello There! I am Andrew Ng, founder of DeepLearning.AI"]
+)
+
+
+demo.launch()

ner.py

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+import requests
+import json
+import os
+
+def get_completion(inputs, parameters=None): 
+    headers = {
+      "Authorization": f"Bearer {model_api}",
+      "Content-Type": "application/json"
+    }
+    data = { "inputs": inputs }
+    if parameters is not None:
+        data.update({"parameters": parameters})
+    response = requests.request("POST",
+                                model_url, headers=headers,
+                                data=json.dumps(data)
+                               )
+    return json.loads(response.content.decode("utf-8"))
+
+def merge_tokens(tokens):
+    merged_tokens=[]
+    for token in tokens:
+        if merged_tokens and merged_tokens[-1]['entity_group'] == token['entity_group']:
+            last_token = merged_tokens[-1]
+            last_token['word'] += token['word'].replace('##', '')
+            last_token['end'] = token['end']
+            last_token['score'] = (last_token['score'] + token['score']) / 2
+        else:
+            merged_tokens.append(token)
+
+    return merged_tokens
+
+def ner(input):
+    output = get_completion(input)
+    output = merge_tokens(output)
+    return {"text": input, "entities": output}
+
+content = json.load(open('hf_api.json'))
+ner_api = content['named_entity_recognition']
+model_url = ner_api['API_URL']
+model_api = ner_api['API_Key']