Update classificator.py

classificator.py

@@ -44,7 +44,6 @@ def get_coordinates(city):
         return None
 
 def predict(cv, job, weight):
-  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
@@ -55,13 +54,16 @@ def predict(cv, job, weight):
   coords_1 = get_coordinates(cv['location'])
   coords_2 = get_coordinates(job['location'])
   distance = 999999
-  diffYoe = max(diffYoe, 0)
-  if coords_1 and coords_2:
-      distance = geodesic(coords_1, coords_2).kilometers
-  else:
-      raise ValueError("Could not get coordinates for one or both cities.")
+  diffYoe = min( cv['yoe']/job['minYoE'], 0)
+  try:
+    if coords_1 and coords_2:
+        distance = geodesic(coords_1, coords_2).kilometers
+    else:
+        raise ValueError("Could not get coordinates for one or both cities.")
+  except ValueError:
+    distance = 100000
 
-  score = weight['exp'] * role_req_exp + weight['position'] * role_pos  + weight['major'] * major_similarity + weight['diffYoe']* diffYoe/job['minYoE'] + weight['skills'] * skill_similarity + weight['location'] * (1 / (1 + distance))
+  score = weight['exp'] * role_req_exp + weight['position'] * role_pos  + weight['major'] * major_similarity + weight['diffYoe']* diffYoe + weight['skills'] * skill_similarity + weight['location'] * (1 / (1 + distance))
   # data = [{
   #   'role_req-exp': role_req_exp,      
   #   'role_pos': role_pos,