Update classificator.py

classificator.py

@@ -1,52 +1,52 @@
-from sentence_transformers import SentenceTransformer
-from sklearn.metrics.pairwise import cosine_similarity
-import numpy as np
-import pandas as pd
-import pickle 
-st = SentenceTransformer('all-mpnet-base-v2')
-filename = 'svc.pkl'
-
-with open(filename, 'rb') as file:
-  model = pickle.load(file)
-
-# role_req-exp        0.341522
-# role_pos            0.350747
-# major_similarity    0.846268
-# skill_similarity    0.774542
-# score               0.986356
-# cv = {
-#     "experiences": str(body.cv.experiences), 
-#     "positions": str(positions), 
-#     "userMajors": str(userMajors), 
-#     "skills": str(body.cv.skills), 
-#     "yoe": yoe
-# }
-# job = {
-#     "jobDesc": body.job.jobDesc, 
-#     "role": body.job.role, 
-#     "majors": str(body.job.majors), 
-#     "skills": str(body.job.skills), 
-#     "minYoE": body.job.minYoE
-# }
-
-def predict(cv, job):
-  diffYoe = cv['yoe'] - job['minYoE']
-  results = {}
-  role_req_exp = cosine_similarity(st.encode(cv['experiences']).reshape(1,-1), st.encode(job['role']+'\n'+job['jobDesc']).reshape(1,-1))[0][0]
-  role_pos = cosine_similarity(st.encode(cv['positions']).reshape(1,-1), st.encode(job['role']).reshape(1,-1))[0][0]
-  major_similarity = cosine_similarity(st.encode(cv['userMajors']).reshape(1,-1), st.encode(job['majors']).reshape(1,-1))[0][0]
-  skill_similarity = cosine_similarity(st.encode(cv['skills']).reshape(1,-1), st.encode(job['skills']).reshape(1,-1))[0][0]
-  score_yoe = 0.5 if diffYoe == -1 else (0 if diffYoe < 0 else 1)
-  score = 0.35 * role_req_exp + 0.1 * role_pos  + 0.15 * major_similarity + 0.3* score_yoe + 0.1 * skill_similarity 
-  data = [{
-    'role_req-exp': role_req_exp,      
-    'role_pos': role_pos,            
-    'major_similarity':  major_similarity,  
-    'skill_similarity':  skill_similarity,
-    'score': score           
-  }]
-  X = pd.DataFrame.from_dict(data)
-  res = model.predict(X)
-  results['score'] = model.predict_proba(X)[:, 1]
-  results['is_accepted'] = np.argmax(res) 
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+import pandas as pd
+import pickle 
+st = SentenceTransformer('all-mpnet-base-v2')
+filename = 'svc.pkl'
+
+with open(filename, 'rb') as file:
+  model = pickle.load(file)
+
+# role_req-exp        0.341522
+# role_pos            0.350747
+# major_similarity    0.846268
+# skill_similarity    0.774542
+# score               0.986356
+# cv = {
+#     "experiences": str(body.cv.experiences), 
+#     "positions": str(positions), 
+#     "userMajors": str(userMajors), 
+#     "skills": str(body.cv.skills), 
+#     "yoe": yoe
+# }
+# job = {
+#     "jobDesc": body.job.jobDesc, 
+#     "role": body.job.role, 
+#     "majors": str(body.job.majors), 
+#     "skills": str(body.job.skills), 
+#     "minYoE": body.job.minYoE
+# }
+
+def predict(cv, job):
+  diffYoe = cv['yoe'] - job['minYoE']
+  results = {}
+  role_req_exp = cosine_similarity(st.encode(cv['experiences']).reshape(1,-1), st.encode(job['role']+'\n'+job['jobDesc']).reshape(1,-1))[0][0] if cv['experiences'] != '[]' else 0
+  role_pos = cosine_similarity(st.encode(cv['positions']).reshape(1,-1), st.encode(job['role']).reshape(1,-1))[0][0] if cv['positions'] != '[]' else 0
+  major_similarity = cosine_similarity(st.encode(cv['userMajors']).reshape(1,-1), st.encode(job['majors']).reshape(1,-1))[0][0] if cv['useMajors'] != '[]' else 0
+  skill_similarity = cosine_similarity(st.encode(cv['skills']).reshape(1,-1), st.encode(job['skills']).reshape(1,-1))[0][0] if cv['skills'] != '[]' else 0
+  score_yoe = 0.5 if diffYoe == -1 else (0 if diffYoe < 0 else 1)
+  score = 0.35 * role_req_exp + 0.1 * role_pos  + 0.15 * major_similarity + 0.3* score_yoe + 0.1 * skill_similarity 
+  data = [{
+    'role_req-exp': role_req_exp,      
+    'role_pos': role_pos,            
+    'major_similarity':  major_similarity,  
+    'skill_similarity':  skill_similarity,
+    'score': score           
+  }]
+  X = pd.DataFrame.from_dict(data)
+  res = model.predict(X)
+  results['score'] = model.predict_proba(X)[:, 1]
+  results['is_accepted'] = np.argmax(res) 
   return results