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import numpy as np
from fit_predictor import load_fit_model, predict_fit_score
def recommend_jobs(jobs_df, user_skills, user_role):
# Load the model
model = load_fit_model()
# Prepare the recommendations
recommended_jobs = []
for index, row in jobs_df.iterrows():
title_match_score = 1 if user_role.lower() in row['job_title'].lower() else 0
matched_skills = set(user_skills).intersection(set(row['skills'].split(',')))
missing_skills = set(row['skills'].split(',')) - matched_skills
fit_score = predict_fit_score(model, len(matched_skills), len(missing_skills), title_match_score)
if fit_score > 0: # Assuming fit_score should be greater than 0 to recommend
recommended_jobs.append({
'job_title': row['job_title'],
'description': row['description_text'][:150] + '...', # Minimized description
'salary': row['salary_formatted'],
'fit_score': fit_score,
'matched_skills': ', '.join(matched_skills),
'missing_skills': ', '.join(missing_skills)
})
# Sort jobs by fit score in descending order
recommended_jobs = sorted(recommended_jobs, key=lambda x: x['fit_score'], reverse=True)
return recommended_jobs
def predict_fit_score(model, matched_count, missing_count, title_match):
features = np.array([[matched_count, missing_count, title_match]])
return model.predict(features)[0]
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