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# imports
import json
import time
import gradio as gr
from transformers import AutoTokenizer, AutoModel
import openai
# pytorch library
import torch
import torch.nn.functional as f
from fuzzywuzzy import process
from roles_list import roles
from openai import OpenAI
# Load the model from the specified directory
embed_store = {}
model = 'sentence-transformers/all-MiniLM-L12-v2'
sbert_model = AutoModel.from_pretrained(model)
sbert_tokenizer = AutoTokenizer.from_pretrained(model)
client = OpenAI(
# defaults to os.environ.get("OPENAI_API_KEY")
api_key="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",
)
for role in roles:
encoding = sbert_tokenizer(role, # the texts to be tokenized
max_length=10,
padding="max_length",
return_tensors='pt' # return the tensors (not lists)
)
with torch.no_grad():
# get the model embeddings
embed = sbert_model(**encoding)
embed = embed.pooler_output
embed_store[role] = f.normalize(embed, p=2, dim=1)
print("Model is ready for inference")
def get_role_from_sbert(title):
start_time = time.time()
encoding = sbert_tokenizer(title,
max_length=10,
padding="max_length",
return_tensors='pt'
)
# Run the model prediction on the input data
with torch.no_grad():
# get the model embeddings
embed = sbert_model(**encoding)
embed = embed.pooler_output
store_cos = {}
for role in embed_store:
cos_sim = torch.nn.functional.cosine_similarity(f.normalize(embed, p=2, dim=1), embed_store[role])
store_cos[role] = round(cos_sim.item(), 3)
# Get the top 3 items with the highest cosine similarity
top_3_keys_values = sorted(store_cos.items(), key=lambda item: item[1], reverse=True)
job_scores_str = '\n'.join([f"{job}: {score}" for job, score in top_3_keys_values])
end_time = time.time()
execution_time = end_time - start_time
# Convert to dictionary if needed or keep as list of tuples
return job_scores_str + f" \nExecution time: {str(execution_time)}"
def fuzzy_match(title):
"""
Find the best matches for a query from a list of choices using fuzzy matching.
Parameters:
- query: The search string.
- choices: A list of strings to search through.
- limit: The maximum number of matches to return.
Returns:
A list of tuples with the match and its score. Higher score means closer match.
"""
matches = process.extract(title, roles, limit=3)
return matches
def fuzzy_match_sbert(title):
matches = fuzzy_match(title)
sbert_results = get_role_from_sbert(title)
new_list = [matches, sbert_results]
return new_list
demo = gr.Interface(fn=get_role_from_sbert,
inputs=gr.Textbox(label="Job Title"),
outputs=gr.Textbox(label="Role"),
title="HackerRank Role Classifier")
gr.close_all()
demo.launch(server_name='0.0.0.0', server_port=8081, share=True) |