first commit

app.py

@@ -0,0 +1,10 @@
+import streamlit as st
+from annotated_text import annotated_text
+from utils import ner_extraction
+
+
+input_text = 'Bill Gates lives in USA'
+
+ner_extraction = ner_extraction(input_text)
+
+print(ner_extraction.entity_position())

requirements.txt

@@ -0,0 +1,4 @@
+streamlit
+st-annotated-text
+requests
+python-dotenv

utils.py

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+import requests
+from dotenv import load_dotenv
+import os
+
+# load the .env file
+load_dotenv()
+
+API_KEY = os.getenv("API")
+
+API_URL = "https://api-inference.huggingface.co/models/Sadashiv/BERT-ner"
+headers = {"Authorization": f"Bearer {API_KEY}"}
+
+tag_color_combination = {'O': '#FF5733',
+                        'PER': '#35B7FF',
+                        'ORG': '#00FF00',
+                        'LOC': '#FFA500',
+                        'MISC': '#BA55D3'}
+
+
+class ner_extraction:
+  def __init__(self, input_text):
+    self.input_text = input_text
+
+  def query(self):
+    response = requests.post(API_URL, headers=headers, json=self.input_text)
+    return response.json()
+
+  def entity_position_locator(self):
+    output = self.query()
+    entity_position = {}
+
+    for i in range(len(output)):
+      entity_position[i]={}
+      entity_position[i]["start"]=output[i]['start']
+      entity_position[i]["end"]=output[i]['end']
+
+    return entity_position 
+  
+  def entity_update(self):
+    entity_list = []
+    output = self.query()
+
+    for i in range(len(output)):
+      entity_list.append(
+          (
+              output[i]['word'],
+              output[i]['entity_group'],
+              tag_color_combination.get(output[i]['entity_group'])
+          )
+      )
+
+    return entity_list
+
+  def text_list(self):
+
+    input_text = self.input_text
+    entity_position = self.entity_position_locator()
+
+    split_text = [] 
+
+    for i in entity_position:
+      split_text.append(input_text[entity_position[i]['start']:entity_position[i]['end']])
+      
+      if entity_position[i]['end']!=len(input_text):
+        
+        if i+1<len(entity_position):
+            split_text.append(input_text[entity_position[i]['end']:entity_position[i+1]['start']])
+        
+        else:
+          split_text.append(input_text[entity_position[i]['end']:])
+
+    return split_text
+
+
+  def entity_position(self):
+    split_text = self.text_list()
+    entity_list = self.entity_update()
+    for i in range(len(split_text)):
+      for j in range(len(entity_list)):
+        if type(split_text[i])!= tuple:
+          if split_text[i].lower()==entity_list[j][0]:
+            split_text[i]=entity_list[j]
+
+    return tuple(split_text)