fixed directories in app.py

app.py

@@ -128,7 +128,7 @@ def main():
     tokenizer=AutoTokenizer.from_pretrained("roberta-base")
     toxic_comments_dataset=toxicity_dataset("data/train.csv",tokenizer,attributes)
 
-    toxicity_data_module=Toxcity_Data_Module("data/train.csv","data/test.csv",attributes)
+    toxicity_data_module=Toxcity_Data_Module("AppDirectory/data/train.csv","AppDirectory/data/test.csv",attributes)
     toxicity_data_module.setup()
     dataloader=toxicity_data_module.train_dataloader()
 
@@ -143,7 +143,7 @@ def main():
         'n_epochs':1
     }
 
-    toxicity_data_module=Toxcity_Data_Module("data/train.csv","data/reduced_test.csv",attributes,batch_size=config['bs'])
+    toxicity_data_module=Toxcity_Data_Module("AppDirectory/data/train.csv","AppDirectory/data/reduced_test.csv",attributes,batch_size=config['bs'])
     toxicity_data_module.setup()
 
     
@@ -163,7 +163,7 @@ def main():
                 logits = predict_raw_comments(model,toxicity_data_module,trainer=trainer)
             torch_logits = torch.from_numpy(logits)
             probabilities = F.softmax(torch_logits, dim = -1).numpy()
-            inputs=pd.read_csv("data/reduced_test.csv")
+            inputs=pd.read_csv("AppDirectory/data/reduced_test.csv")
             data=[]
             #print(inputs["comment_text"][0]," ",probabilities)
             for i in range(len(probabilities)):
@@ -185,4 +185,4 @@ def main():
     
 
 if __name__ == '__main__' :
-    main()
+    main()