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from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
import pickle
st = SentenceTransformer('all-mpnet-base-v2')
filename = 'svc.pkl'
with open(filename, 'rb') as file:
model = pickle.load(file)
# role_req-exp 0.341522
# role_pos 0.350747
# major_similarity 0.846268
# skill_similarity 0.774542
# score 0.986356
# cv = {
# "experiences": str(body.cv.experiences),
# "positions": str(positions),
# "userMajors": str(userMajors),
# "skills": str(body.cv.skills),
# "yoe": yoe
# }
# job = {
# "jobDesc": body.job.jobDesc,
# "role": body.job.role,
# "majors": str(body.job.majors),
# "skills": str(body.job.skills),
# "minYoE": body.job.minYoE
# }
def predict(cv, job):
diffYoe = cv.yoe - job.minimumYoe
results = {}
role_req_exp = cosine_similarity(st.encode(cv['experiences']), st.encode(job['role']+' '+job['jobDesc']))
role_pos = cosine_similarity(st.encode(cv['positions']), st.encode(job['role']))
major_similarity = cosine_similarity(st.encode(cv['userMajors']), st.encode(job['majors']))
skill_similarity = cosine_similarity(st.encode(cv['skills']), st.encode(job['skills']))
score_yoe = 0.5 if diffYoe == -1 else (1 if diffYoe > 0 else 0)
score = 0.35 * role_req_exp + 0.1 * role_pos + 0.15 * major_similarity + 0.3* score_yoe + 0.1 * skill_similarity
X = np.array([role_req_exp, role_pos, major_similarity, skill_similarity, score]).reshape(1, -1)
res = model.predict(X)
results['score'] = model.predict(X)[:, 1]
results['is_accepted'] = np.argmax(res)
return results |