Update main.py

main.py

@@ -50,7 +50,6 @@ def health():
 # we will make here post request to compare between lists of skills one has employee just one text and the other has the of jobs has many texts
 # the llm will say the most similar job to the cv
 @app.route('/compare', methods=['POST'])
-@app.route('/compare', methods=['POST'])
 def compare():
     employee_skills = request.json.get('employee_skills')
     jobs_skills = request.json.get('jobs_skills')
@@ -70,6 +69,26 @@ def compare():
         similarity_scores.append({"job": jobs_skills[i], "similarity_score": similarity_score.item()})
 
     return jsonify(similarity_scores)
+@app.route('/compare', methods=['POST'])
+def compare_jop():
+    employee_skills = request.json.get('post')
+    jobs_skills = request.json.get('employee_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")
+
+    job_embeddings = model.encode(employee_skills)
+    employee_embeddings = model.encode(emplpostoyee_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.item()})
+
+    return jsonify(similarity_scores)
 
 
 if __name__ == '__main__':