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# recommendation_engine.py
import requests
from bs4 import BeautifulSoup
import pandas as pd
from sentence_transformers import SentenceTransformer, util
import torch
import numpy as np
from langchain.callbacks.tracers import ConsoleCallbackHandler
from langsmith import traceable
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1",trust_remote_code=True)
catalog = pd.read_csv("data.csv")
embeddings = torch.load("embeddings.pth")
handler = ConsoleCallbackHandler()
def scrape_url(url):
try:
page = requests.get(url)
soup = BeautifulSoup(page.text, "html.parser")
return soup.get_text(separator=' ')
except Exception as e:
return ""
def clean_query_text(text):
replacements = {
"Java Script": "JavaScript",
"java script": "JavaScript",
"Java script": "JavaScript"
}
for wrong, correct in replacements.items():
text = text.replace(wrong, correct)
return text
def prepare_input(query, duration, jd_text=""):
cleaned_query = clean_query_text(query)
input_text = f"{cleaned_query}. Candidate should complete assessment in {duration} minutes. {jd_text}"
return input_text.strip()
def get_recommendations(query_text, top_k=10,max_duration = None):
query_embedding = model.encode(query_text)
scores = util.cos_sim(query_embedding, embeddings)[0].numpy()
ranked_indices = np.argsort(-scores)
results = []
for idx in ranked_indices:
item = catalog.iloc[idx]
print(f"Matched: {item['name']} with duration {item['assessment_length']}")
result = {
"name": item["name"],
"url": item["url"],
"remote_testing": item["remote"],
"adaptive": item["adaptive"],
"duration": item['assessment_length'],
"test_type": item["test_types"],
}
results.append(result)
if len(results) >= top_k:
break
return results
@traceable(name="SHL Recommendation Trace")
def traced_get_recommendations(query_text, top_k=10, max_duration=None):
return get_recommendations(query_text, top_k, max_duration)
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