initial commit

app.py

@@ -0,0 +1,44 @@
+import gradio as gr
+import numpy as np
+from transformers import pipeline
+
+title = "Token Classification"
+description = """
+Label the entities of a sentence as: 
+1. person(PER), 
+2. organization(ORG), 
+3. location(LOC) 
+4. miscellaneous(MISC).
+<img src="https://huggingface.co/spaces/course-demos/Rick_and_Morty_QA/resolve/main/rick.png" width=200px>
+"""
+
+article = "Check out [my github repository](https://github.com/Neural-Net-Rahul/P2-Token-Classification-using-Fine-tuned-Hugging-face-transformer) and my [fine tuned model](https://huggingface.co/neural-net-rahul/bert-finetuned-ner)"
+
+textbox = gr.Textbox(label="Type your sentence here :", placeholder="My name is Bill Gates.", lines=3)
+
+model = pipeline('token-classification',model='neural-net-rahul/bert-finetuned-ner')
+
+def predict(text):
+  result = []
+  for dicti in model(text):
+    entity,word = dicti['entity'],dicti['word']
+    if entity == "B-PER" or entity=='I-PER':
+      entity = "Person"
+    elif entity == "B-LOC" or entity=='I-LOC':
+      entity = "Location"
+    elif entity == "B-ORG" or entity=='I-ORG':
+      entity = "Organization"
+    elif entity == "B-MISC" or entity=='I-MISC':
+      entity = "Miscellaneous"
+    result.append({entity,word})
+  return result
+
+gr.Interface(
+    fn=predict,
+    inputs=textbox,
+    outputs=[gr.Text()],
+    title=title,
+    description=description,
+    article=article,
+    examples=[["Mark founded Facebook, shaping global social media connectivity."], ["Delhi is the most beautiful state after Kerala"]],
+).launch()