Upload app.py with huggingface_hub

app.py

@@ -0,0 +1,42 @@
+from transformers import AutoTokenizer, AutoModel
+import pandas as pd
+import numpy as np
+import random 
+import torch
+import os 
+import gradio as gr
+
+# Model
+auth_token = os.environ.get("TOKEN_FROM_SECRET")
+checkpoint = 'srota/job-bert-mini'
+model = AutoModel.from_pretrained(checkpoint, token=auth_token)
+tokenizer = AutoTokenizer.from_pretrained(checkpoint, token=auth_token)
+
+# Data
+titles = pd.read_csv('inventory.csv', usecols=['title'])['title'].tolist()
+descriptions = pd.read_csv('inventory.csv', usecols=['description'])['description'].tolist()
+with open('inventory.npy', 'rb') as f:     
+    embeddings = np.load(f)
+
+# Inference
+def inference(query, top_k=5):
+    with torch.no_grad():  
+        inputs = tokenizer([query], padding=True, truncation=True, max_length=512, return_tensors='pt')        
+    query_embedding = model(**inputs)['last_hidden_state'][:,0,:].detach().numpy()
+    cosines = np.dot(query_embedding, embeddings.T)[0]
+    indexes = np.argsort(cosines)[-top_k:]
+    return '\n\n'.join(['*' + t for i, t in enumerate(titles) if i in indexes])
+
+# Gradio
+examples = [['Data Scientist'], ['Warehouse Worker'], ['Gardener'], ['Part-Time Cleaner'], ['Math Teacher'], ['Registered Nurse'], ['Line Cook'],['Night Porter'],['Dietitian'],['Planned Surveyor'],['Driving Instructor'],['Senior It Engineer'],['Stores Person'],['Dental Hygienist'],['Event Manager'],['Welder'],['Underwriter'],['Frontend Developer'],['Paralegal'],['Copywriter'],['Community Nurse'],['Courier'],['Personal Trainer'],['Night Porter'],['Pharmacist'],['Carpenter']]
+
+demo = gr.Interface(
+    fn=inference,
+    title='Job Search', 
+    description='Simulate a semantic search for retrieving job titles that match the user query (the match is performed between the user query and 15K job descriptions)',
+    inputs=gr.Textbox(lines=1, placeholder='', label="User keyword"),
+    outputs=gr.Textbox(lines=10, label="Relevant jobs"),
+    examples=random.sample(examples, 10)
+)
+
+demo.launch()