Update app.py

app.py

@@ -6,9 +6,6 @@ import pandas as pd
 from tqdm import tqdm
 
 from transformers import AutoTokenizer, AutoConfig, AutoModel, AutoModelForSequenceClassification
-from transformers import TrainingArguments, Trainer
-
-from datasets import load_dataset
 
 
 description_sentence = "<h3>Demo EmotioNL</h3>\nThis demo allows you to analyse the emotion in a sentence."
@@ -85,7 +82,7 @@ def inference_dataset(file_object):
   return output
 """
 
-def inference_dataset(file_object):
+def inference_dataset(file_object, option_list):
     tokenizer = AutoTokenizer.from_pretrained(inference_modelpath)
     model = AutoModelForSequenceClassification.from_pretrained(inference_modelpath)
     data_path = open(file_object, 'r')
@@ -108,7 +105,17 @@ def inference_dataset(file_object):
     for line in predictions_content:
         f.write(str(line[0]) + '\t' + str(line[1]) + '\t' + str(line[2]) + '\n')
     f.close()
-    return output
+    output1 = output
+    output2 = output3 = output4 = output5 = "This option was not selected."
+    if "emotion frequencies" in option_list:
+        output2 = "This option was selected."
+    if "emotion distribution over time" in option_list:
+        output3 = "This option was selected."
+    if "peaks" in option_list:
+        output4 = "This option was selected."
+    if "topics" in option_list:
+        output5 = "This option was selected."
+    return [output1, output2, output3, output4, output5]
 
 def what_happened(text, file_object, option_list):
     if file_object:
@@ -180,7 +187,7 @@ iface_sentence = gr.Interface(
             outputs="text")
 
 iface2 = gr.Interface(
-            fn=what_happened2,
+            fn=inference_dataset,
             description = description2,
             inputs=[
                 gr.File(