Create app.py

app.py

@@ -0,0 +1,42 @@
+# -*- coding: utf-8 -*-
+"""gradio_deploy.ipynb
+Automatically generated by Colaboratory.
+"""
+import os
+import gradio
+from PIL import Image
+from timeit import default_timer as timer
+from tensorflow import keras
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+import numpy as np
+
+loaded_model = AutoModelForSequenceClassification.from_pretrained("runaksh/ResumeClassification_distilBERT")
+loaded_tokenizer = AutoTokenizer.from_pretrained("runaksh/ResumeClassification_distilBERT")
+
+# Function for class prediction
+def predict(sample, validate=True):
+  classifier = pipeline("text-classification", model=loaded_model, tokenizer=loaded_tokenizer)
+  pred = classifier(sample)[0]['label']
+  return pred
+
+title = "Categorizing the Resumes"
+description = "Enter the Resume you want to categorize"
+
+# Gradio elements
+
+# Input from user
+in_prompt = gradio.inputs.Textbox(lines=2, label='Enter the Resume')
+
+# Output response
+out_response = gradio.outputs.Textbox(label='Category')
+
+# Gradio interface to generate UI link
+iface = gradio.Interface(fn=predict,
+                         inputs = in_prompt,
+                         outputs = out_response,
+                         title=title,
+                         description=description
+                         )
+
+iface.launch(debug = True)