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import pandas as pd
import numpy as np
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import requests
from bs4 import BeautifulSoup
import torch
import gc
import time
from transformers import AutoTokenizer, AutoModelForCausalLM
class SHLRecommender:
_cache = {}
_cache_size = 20
def __init__(self, data_path='utils/data.csv'):
try:
self.df = pd.read_csv(data_path)
except FileNotFoundError:
raise FileNotFoundError(f"Data file not found at {data_path}. Please check the path.")
self.df.columns = [col.strip() for col in self.df.columns]
try:
import os
cache_dir = os.path.join(os.getcwd(), 'model_cache')
os.makedirs(cache_dir, exist_ok=True)
print(f"Using cache directory: {cache_dir}")
self.embedding_model = SentenceTransformer('all-MiniLM-L6-v2', cache_folder=cache_dir)
print("Successfully loaded all-MiniLM-L6-v2 model")
except Exception as e:
print(f"Error loading primary model: {str(e)}")
try:
# Try a different model as fallback
print("Trying fallback model: paraphrase-MiniLM-L3-v2")
self.embedding_model = SentenceTransformer('paraphrase-MiniLM-L3-v2', cache_folder=cache_dir)
print("Successfully loaded fallback model")
except Exception as e2:
print(f"Error loading fallback model: {str(e2)}")
# Create a simple embedding model as last resort
from sentence_transformers import models, SentenceTransformer
print("Creating basic embedding model from scratch")
word_embedding_model = models.Transformer('bert-base-uncased', cache_dir=cache_dir)
pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension())
self.embedding_model = SentenceTransformer(modules=[word_embedding_model, pooling_model])
print("Created basic embedding model")
model_id = "Qwen/Qwen2.5-0.5B-Instruct"
self.tokenizer = AutoTokenizer.from_pretrained(
model_id,
trust_remote_code=True,
use_fast=True,
model_max_length=512,
)
try:
print(f"Loading Qwen model: {model_id}")
self.model = AutoModelForCausalLM.from_pretrained(
model_id,
trust_remote_code=True,
torch_dtype=torch.float32,
device_map="auto",
low_cpu_mem_usage=True,
cache_dir=cache_dir,
local_files_only=False,
revision="main"
)
print("Successfully loaded Qwen model")
except ValueError as e:
print(f"Error with device_map: {str(e)}")
try:
print("Trying without device_map")
self.model = AutoModelForCausalLM.from_pretrained(
model_id,
trust_remote_code=True,
torch_dtype=torch.float32,
low_cpu_mem_usage=True,
cache_dir=cache_dir
)
print("Successfully loaded Qwen model without device_map")
except Exception as e2:
print(f"Error loading Qwen model: {str(e2)}")
try:
print("Trying fallback to smaller model: distilgpt2")
self.model = AutoModelForCausalLM.from_pretrained(
"distilgpt2",
cache_dir=cache_dir
)
self.tokenizer = AutoTokenizer.from_pretrained(
"distilgpt2",
cache_dir=cache_dir
)
print("Successfully loaded fallback model")
except Exception as e3:
print(f"All model loading attempts failed: {str(e3)}")
raise ValueError("Could not load any language model. Please check your environment and permissions.")
self.create_embeddings()
def create_embeddings(self):
texts = []
for _, row in self.df.iterrows():
text = f"{row['Test Name']} {row['Test Type']}"
texts.append(text)
self.product_embeddings = self.embedding_model.encode(texts)
def extract_text_from_url(self, url):
try:
response = requests.get(url)
response.raise_for_status()
soup = BeautifulSoup(response.content, 'html.parser')
for script in soup(["script", "style"]):
script.extract()
text = soup.get_text()
lines = (line.strip() for line in text.splitlines())
chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
text = '\n'.join(chunk for chunk in chunks if chunk)
return text
except Exception as e:
return f"Error extracting text from URL: {str(e)}"
def optimize_memory(self):
if torch.cuda.is_available():
torch.cuda.empty_cache()
self._cache.clear()
gc.collect()
return {"status": "Memory optimized"}
def generate_test_description(self, test_name, test_type):
try:
cache_key = f"{test_name}_{test_type}"
if cache_key in self._cache:
return self._cache[cache_key]
prompt = f"Write a short, factual description of '{test_name}', a {test_type} assessment, in 1-2 sentences."
inputs = self.tokenizer(prompt, return_tensors="pt", truncation=True, max_length=128, padding=True)
with torch.no_grad():
outputs = self.model.generate(
inputs.input_ids,
attention_mask=inputs.attention_mask,
max_new_tokens=40,
temperature=0.2,
top_p=0.95,
do_sample=False,
no_repeat_ngram_size=3
)
full_response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
generated_text = full_response.replace(prompt, "").strip()
if len(generated_text) < 20 or "write" in generated_text.lower() or "description" in generated_text.lower():
if test_type.lower() in ["cognitive ability", "cognitive", "reasoning"]:
description = f"The {test_name} measures cognitive abilities and problem-solving skills."
elif "numerical" in test_name.lower() or "numerical" in test_type.lower():
description = f"The {test_name} assesses numerical reasoning and data analysis abilities."
elif "verbal" in test_name.lower() or "verbal" in test_type.lower():
description = f"The {test_name} evaluates verbal reasoning and language comprehension skills."
elif "personality" in test_type.lower() or "behavioral" in test_type.lower():
description = f"The {test_name} assesses behavioral tendencies and personality traits in workplace contexts."
elif "technical" in test_type.lower() or any(tech in test_name.lower() for tech in ["java", "python", ".net", "sql", "coding"]):
description = f"The {test_name} evaluates technical knowledge and programming skills."
else:
description = f"The {test_name} assesses candidate suitability through standardized methods."
else:
description = generated_text
if len(self._cache) >= self._cache_size:
self._cache.pop(next(iter(self._cache)))
self._cache[cache_key] = description
return description
except Exception:
if test_type.lower() in ["cognitive ability", "cognitive", "reasoning"]:
return f"The {test_name} measures cognitive abilities through structured problem-solving tasks."
elif test_type.lower() in ["personality", "behavioral"]:
return f"The {test_name} assesses behavioral tendencies and personality traits."
elif "technical" in test_type.lower():
return f"The {test_name} evaluates technical knowledge and skills."
else:
return f"The {test_name} assesses {test_type.lower()} capabilities."
def check_health(self):
try:
test_prompt = "This is a test prompt to check model health."
start_time = time.time()
inputs = self.tokenizer(
test_prompt,
return_tensors="pt",
truncation=True,
max_length=32,
padding=True
)
tokenization_time = time.time() - start_time
start_time = time.time()
with torch.no_grad():
_ = self.model.generate(
inputs.input_ids,
attention_mask=inputs.attention_mask,
max_new_tokens=20,
do_sample=True
)
inference_time = time.time() - start_time
start_time = time.time()
self.embedding_model.encode(["Test embedding"])
embedding_time = time.time() - start_time
return {
"status": "healthy",
"tokenization_time_ms": round(tokenization_time * 1000, 2),
"inference_time_ms": round(inference_time * 1000, 2),
"embedding_time_ms": round(embedding_time * 1000, 2),
"cache_size": len(self._cache)
}
except Exception as e:
return {"status": "unhealthy", "error": str(e)}
def get_recommendations(self, query, is_url=False, max_recommendations=10):
self._cache.clear()
if is_url:
text = self.extract_text_from_url(query)
else:
text = query
max_text_length = 2000
if len(text) > max_text_length:
text = text[:max_text_length] + "..."
query_embedding = self.embedding_model.encode(text[:1000])
similarity_scores = cosine_similarity(
[query_embedding],
self.product_embeddings
)[0]
top_indices = np.argsort(similarity_scores)[::-1][:max_recommendations]
recommendations = []
for idx in top_indices:
recommendations.append({
'Test Name': self.df.iloc[idx]['Test Name'],
'Test Type': self.df.iloc[idx]['Test Type'],
'Remote Testing': self.df.iloc[idx]['Remote Testing (Yes/No)'],
'Adaptive/IRT': self.df.iloc[idx]['Adaptive/IRT (Yes/No)'],
'Duration': self.df.iloc[idx]['Duration'],
'Link': self.df.iloc[idx]['Link']
})
return recommendations |