Update main.py

main.py

@@ -45,60 +45,6 @@ def get_skills():
 def health():
     return jsonify({'status': 'Worked'})
 
-# Endpoint to compare between employee skills and job skills
-@app.route('/compare', methods=['POST'])
-def compare():
-    employee_skills = request.json.get('employee_skills')
-    jobs_skills = request.json.get('jobs_skills')
-
-    if not isinstance(employee_skills, list) or not all(isinstance(skill, str) for skill in employee_skills):
-        raise ValueError("employee_skills must be a list of strings")
-
-    if not isinstance(jobs_skills, list) or not all(isinstance(skill, str) for skill in jobs_skills):
-        raise ValueError("jobs_skills must be a list of strings")
-
-    job_embeddings = model.encode(jobs_skills)
-    employee_embeddings = model.encode(employee_skills)
-
-    similarity_scores = []
-    employee_embeddings_tensor = torch.from_numpy(employee_embeddings).unsqueeze(0)
-
-    for i, job_e in enumerate(job_embeddings):
-        job_e_tensor = torch.from_numpy(job_e).unsqueeze(0)
-        similarity_score = cosine_similarity(employee_embeddings_tensor, job_e_tensor, dim=1)
-        similarity_scores.append({"job": jobs_skills[i], "similarity_score": similarity_score.tolist()[0]})
-
-    return jsonify(similarity_scores)
-
-# Endpoint to compare job posts with employee skills
-@app.route('/compare_jop', methods=['POST'])
-def compare_jop():
-    employee_skills = request.json.get('employee_skills')
-    jobs_skills = request.json.get('jobs_skills')
-
-    if not isinstance(employee_skills, list) or not all(isinstance(skill, str) for skill in employee_skills):
-        raise ValueError("employee_skills must be a list of strings")
-
-    if not isinstance(jobs_skills, list) or not all(isinstance(skill, str) for skill in jobs_skills):
-        raise ValueError("jobs_skills must be a list of strings")
-
-    job_embeddings = model.encode(jobs_skills)
-    employee_embeddings = model.encode(employee_skills)
-
-    similarity_scores = []
-    employee_embeddings_tensor = torch.from_numpy(employee_embeddings).unsqueeze(0)
-
-    for i, job_e in enumerate(job_embeddings):
-        job_e_tensor = torch.from_numpy(job_e).unsqueeze(0)
-        similarity_score = cosine_similarity(employee_embeddings_tensor, job_e_tensor, dim=1)
-        similarity_scores.append({"job": jobs_skills[i], "similarity_score": similarity_score.tolist()[0]})
-
-    return jsonify(similarity_scores)
-
-if __name__ == '__main__':
-    app.run()
-
-
 # Endpoint to compare between employee skills and job skills
 @app.route('/compare', methods=['POST'])
 def compare():
@@ -150,4 +96,4 @@ def compare_jop():
     return jsonify(similarity_scores)
 
 if __name__ == '__main__':
-    app.run()
+    app.run()