Update main.py

main.py

@@ -44,16 +44,16 @@ def health():
 
 @app.route('/compare', methods=['POST'])
 def compare():
-    jobs_skills = request.json.get('job_skills')
-    employee_skills = request.json.get('employee_skills')
+    employee_skills = request.json.get('jobs_skills')
+    jobs_skills = request.json.get('employee_skills')
 
     # Validation
     if not isinstance(jobs_skills, list) or not all(isinstance(skill, str) for skill in jobs_skills):
-        raise ValueError("job_skills must be a list of strings")
+        raise ValueError("jobs_skills must be a list of strings")
 
     # Encoding skills into embeddings
-    employee_embeddings = model.encode(employee_skills)
     job_embeddings = model.encode(jobs_skills)
+    employee_embeddings = model.encode(employee_skills)
 
     # Computing cosine similarity between employee skills and each job
     similarity_scores = []
@@ -61,11 +61,11 @@ def compare():
 
     for i, job_e in enumerate(job_embeddings):
         job_e_tensor = torch.from_numpy(job_e).unsqueeze(0)
-        similarity_score = torch.nn.functional.cosine_similarity(employee_embeddings_tensor, job_e_tensor, dim=1)
-        similarity_scores.append({"job": jobs_skills[i], "similarity_score": similarity_score.squeeze().tolist()})
-
+        similarity_score = cosine_similarity(employee_embeddings_tensor, job_e_tensor, dim=1)
+        similarity_scores.append({"job": jobs_skills[i], "similarity_score": similarity_score.item()})
 
     return jsonify(similarity_scores)
 
+
 if __name__ == '__main__':
     app.run()