Update job_recommendation_inference.py

job_recommendation_inference.py

@@ -4,7 +4,7 @@ from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
 import json
 
-def recommend_jobs_for_input_skills(input_hard_skills, input_soft_skills, input_major, companies, tfidf_vectorizer_skills, tfidf_vectorizer_majors, companies_skills_vec, companies_majors_vec):
+def recommend_jobs_for_input_skills(input_hard_skills, input_soft_skills, input_major, jobs, tfidf_vectorizer_skills, tfidf_vectorizer_majors, companies_skills_vec, companies_majors_vec):
     input_hard_skills_vec = tfidf_vectorizer_skills.transform([input_hard_skills])
     input_soft_skills_vec = tfidf_vectorizer_skills.transform([input_soft_skills])
     input_major_vec = tfidf_vectorizer_majors.transform([input_major])
@@ -22,9 +22,9 @@ def recommend_jobs_for_input_skills(input_hard_skills, input_soft_skills, input_
     combined_similarity = (skills_similarity + major_similarity) / 2
 
     sorted_company_indices = np.argsort(-combined_similarity[0])
-    recommended_companies = companies.iloc[sorted_company_indices]['Major'].values[:3]
+    recommended_companies = jobs.iloc[sorted_company_indices]['Major'].values[:3]
 
-    return recommended_companies
+    return recommended_companies.tolist()
 
 def handler(event, context):
     input_data = json.loads(event['body'])
@@ -61,5 +61,5 @@ def handler(event, context):
 
     return {
         'statusCode': 200,
-        'body': json.dumps(recommended_jobs.tolist())
-    }
+        'body': json.dumps(recommended_jobs)
+    }