Update app.py

app.py

@@ -122,6 +122,9 @@ def classify(new_column = True):
   else:
     return Image.open(r"inelegivel.png")
 
+  dataMCTI['opo_pre_tkn'] = sentencesMCTIList_xp8
+  dataMCTI['opo_pre'] = sentencesMCTIList_xp8_sentences
+
 def gen_output(data):
   return "output.xlsx"
     
@@ -134,13 +137,15 @@ reconstructed_model_CNN = keras.models.load_model("best weights CNN.h5",
                                                                    "recall_m":recall_m})
 
 def app(operacao, resultado, dados):
-
-  boxes = {'Color': ['Green','Green','Green','Blue','Blue','Red','Red','Red'],
-           'Shape': ['Rectangle','Rectangle','Square','Rectangle','Square','Square','Square','Rectangle'],
-           'Price': [10,15,5,5,10,15,15,5]
-          }
-  df = pd.DataFrame(boxes, columns= ['Color','Shape','Price'])
-  df.to_excel("output.xlsx")
+  data = pd.read_excel(dados)
+
+  # boxes = {'Color': ['Green','Green','Green','Blue','Blue','Red','Red','Red'],
+  #          'Shape': ['Rectangle','Rectangle','Square','Rectangle','Square','Square','Square','Rectangle'],
+  #          'Price': [10,15,5,5,10,15,15,5]
+  #         }
+  # df = pd.DataFrame(boxes, columns= ['Color','Shape','Price'])
+  data.to_excel("output.xlsx")
+  return "output.xlsx"
 
   if operacao === "Pré-processamento + Classificação" :
     pre_process()