Upload app.py

app.py

@@ -4,26 +4,18 @@ 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
@@ -65,30 +57,6 @@ def get_role_from_sbert(title):
     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"),