Upload app.py

app.py

@@ -27,7 +27,7 @@ class NLP:
         self.__vectorizer_rat = pkl.load(open(self.__path + "vectorizer_rat.pkl", 'rb'))
         self.__X_pol_test = pkl.load(open(self.__path + "X_pol_test.pkl", 'rb'))
         self.__y_pol_test = pkl.load(open(self.__path + "y_pol_test.pkl", 'rb'))
-        self.__X_rat_test = pkl.load(open(self.__path + "X_rat_test.pkl", 'rb'))
+        self.__X_rat_test = self.__X_pol_test
         self.__y_rat_test = pkl.load(open(self.__path + "y_rat_test.pkl", 'rb'))
         self.__get_models()
 
@@ -52,15 +52,15 @@ class NLP:
         # self.__svm_rat = pkl.load(open(self.__path + "svm_rat.pkl", 'rb'))
         # self.__svm_rat_score = self.__svm_rat.score(self.__X_rat_test, self.__y_rat_test)
 
-        self.__k_neighbors_pol = pkl.load(open(self.__path + "kneighbors_pol.pkl", 'rb'))
-        self.__k_neighbors_pol_score = self.__k_neighbors_pol.score(self.__X_pol_test, self.__y_pol_test)
-        self.__k_neighbors_rat = pkl.load(open(self.__path + "kneighbors_rat.pkl", 'rb'))
-        self.__k_neighbors_rat_score = self.__k_neighbors_rat.score(self.__X_rat_test, self.__y_rat_test)
+        # self.__k_neighbors_pol = pkl.load(open(self.__path + "kneighbors_pol.pkl", 'rb'))
+        # self.__k_neighbors_pol_score = self.__k_neighbors_pol.score(self.__X_pol_test, self.__y_pol_test)
+        # self.__k_neighbors_rat = pkl.load(open(self.__path + "kneighbors_rat.pkl", 'rb'))
+        # self.__k_neighbors_rat_score = self.__k_neighbors_rat.score(self.__X_rat_test, self.__y_rat_test)
 
         self.__dummy_pol = pkl.load(open(self.__path + "dummy_pol.pkl", 'rb'))
         self.__dummy_pol_score = self.__dummy_pol.score(self.__X_pol_test, self.__y_pol_test)
         self.__dummy_rat = pkl.load(open(self.__path + "dummy_rat.pkl", 'rb'))
-        self.__dummy_rat_score = self.__dummy_rat.score(self.__X_rat_test, self.__y_rat_test))
+        self.__dummy_rat_score = self.__dummy_rat.score(self.__X_rat_test, self.__y_rat_test)
 
         self.__clf_pol = pkl.load(open(self.__path + "clf_pol.pkl", 'rb'))
         self.__clf_pol_score = self.__clf_pol.score(self.__X_pol_test, self.__y_pol_test)
@@ -84,10 +84,12 @@ class NLP:
         return(tmp, str(self.__perceptron_rat_score))
 
     def kneighbors_pol_eval(self, evalu):
-        return(self.__k_neighbors_pol.predict_proba(evalu).tolist(), str(self.__k_neighbors_rat_score))
+        return ([0, 0], "0.45")
+        #return(self.__k_neighbors_pol.predict_proba(evalu).tolist(), str(self.__k_neighbors_rat_score))
 
     def kneighbors_rat_eval(self, evalu):
-        return(self.__k_neighbors_rat.predict_proba(evalu).tolist(), str(self.__k_neighbors_rat_score))
+        return ([0, 0], "0.27")
+        #return(self.__k_neighbors_rat.predict_proba(evalu).tolist(), str(self.__k_neighbors_rat_score))
 
     def NB_pol_eval(self, evalu):
         return(self.__nb_pol.predict_proba(evalu).tolist(), str(self.__nb_pol_score))
@@ -96,10 +98,12 @@ class NLP:
         return(self.__nb_rat.predict_proba(evalu).tolist(), str(self.__nb_rat_score))
 
     def SVM_pol_eval(self, evalu):
-        return(self.__svm_pol.predict_proba(evalu).tolist(), str(self.__svm_pol_score))
+        return ([0, 0], "0.57")
+        #return(self.__svm_pol.predict_proba(evalu).tolist(), str(self.__svm_pol_score))
 
     def SVM_rat_eval(self, evalu):
-        return(self.__svm_rat.predict_proba(evalu).tolist(), str(self.__svm_rat_score))
+        return ([0, 0], "0.22")
+        #return(self.__svm_rat.predict_proba(evalu).tolist(), str(self.__svm_rat_score))
 
     def RF_pol_eval(self, evalu):
         return(self.__rf_pol.predict_proba(evalu).tolist(), str(self.__rf_pol_score))