Upload app.py

app.py

@@ -1,8 +1,21 @@
 import pandas as pd
 import pickle as pkl
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+from sklearn.dummy import DummyClassifier
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.linear_model import Perceptron
 from numpy import reshape
 import numpy as np
-import sklearn
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import classification_report
+from sklearn.naive_bayes import GaussianNB
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.linear_model import Perceptron
+from sklearn.dummy import DummyClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.neural_network import MLPClassifier
+from sklearn import svm
 import gradio as gr
 
 class NLP:
@@ -71,11 +84,11 @@ class NLP:
         return(tmp, str(self.__perceptron_rat_score))
 
     def kneighbors_pol_eval(self, evalu):
-        return ([0, 0], "0.45")
+        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 ([0, 0], "0.27")
+        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):
@@ -85,11 +98,11 @@ class NLP:
         return(self.__nb_rat.predict_proba(evalu).tolist(), str(self.__nb_rat_score))
 
     def SVM_pol_eval(self, evalu):
-        return ([0, 0], "0.57")
+        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 ([0, 0], "0.22")
+        return ([[0, 0]], "0.22")
         #return(self.__svm_rat.predict_proba(evalu).tolist(), str(self.__svm_rat_score))
 
     def RF_pol_eval(self, evalu):
@@ -130,8 +143,10 @@ class NLP:
             percent, score = self.__exec[model][1](review)
             res = pd.DataFrame({'Rated 1/5': percent[0][0], 'Rated 2/5': percent[0][1], 'Rated 4/5': percent[0][2], 'Rated 5/5': percent[0][3]}, index=["Prediction"])
 
-        return (res, f"Model: {model}\nDataset: {Dataset}\nAccuracy: {str(float(score)*100)}")
-
+        if (percent[0][0] == 0 and percent[1][0] == 0):
+            return (res, f"Model: {model}\nDataset: {Dataset}\nAccuracy: {str(float(score)*100)}\nDue to the size of the model, it has not been implemented on huggingface.")
+        else:
+            return (res, f"Model: {model}\nDataset: {Dataset}\nAccuracy: {str(float(score)*100)}")
 
 if __name__ == "__main__":
     class Execution: