classifying the resume with job profile

app.py

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+from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModel
+import torch
+import pandas as pd
+import numpy as np
+import streamlit as st
+import json
+
+
+def intial_load():
+    tokenizer = AutoTokenizer.from_pretrained("LijinDurairaj/classify_resume_model")
+    model = AutoModelForSequenceClassification.from_pretrained("LijinDurairaj/classify_resume_model")
+    return tokenizer,model
+
+def load_labels():
+    with open('./label.json','r') as file:
+        data=json.load(file)
+        return data
+
+tokenizer,model=intial_load()
+labels=load_labels()
+
+st.title('classifying resume')
+
+resume = st.text_area(label='resume',placeholder='paste your resume here...')
+
+if st.button('Submit'):
+    resume_list=[]
+    resume_list.append(resume)
+
+    tokenized_data=tokenizer.batch_encode_plus(
+        resume_list,
+        max_length=512,
+        truncation=True,
+        return_tensors='pt'
+    )
+    with torch.no_grad():
+        prediction=model(**tokenized_data)
+    predicted_label=np.argmax(prediction[0].view(-1))
+    profile=[d for d,i in labels.items() if i==predicted_label.item()][0]
+    st.success(f'The uploaded resume is fit for the following role: {profile}')
+
+

label.json

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+{
+    "TEACHER": 0,
+   "INFORMATION-TECHNOLOGY": 1,
+   "APPAREL": 2,
+   "FINANCE": 3,
+   "ENGINEERING": 4,
+   "BANKING": 5,
+   "CONSULTANT": 6,
+   "DESIGNER": 7,
+   "DIGITAL-MEDIA": 8,
+   "ACCOUNTANT": 9,
+   "AVIATION": 10,
+   "CHEF": 11,
+   "AUTOMOBILE": 12,
+   "BPO": 13,
+   "ADVOCATE": 14,
+   "BUSINESS-DEVELOPMENT": 15,
+   "HEALTHCARE": 16,
+   "FITNESS": 17,
+   "AGRICULTURE": 18,
+   "CONSTRUCTION": 19,
+   "SALES": 20,
+   "PUBLIC-RELATIONS": 21,
+   "HR": 22,
+   "ARTS": 23}

requirements.txt

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+streamlit
+torch