Add application file

app.py

@@ -38,7 +38,7 @@ def classify_output(item):
         'LABEL_2': 'Self',
         'LABEL_3': 'Motivation',
         'LABEL_4': 'Attention',
-        'LABEL_5': 'Overt_Behavior',
+        'LABEL_5': 'Overt Behavior',
         'LABEL_6': 'Context'
     }
 
@@ -136,12 +136,14 @@ demo = gr.Interface(fn=classify_output, inputs=txtbx, outputs=txtbxopt,
                     title = 'EEMM Item Classification Machine V 0.5',
                     description = """
                     
-                    This machine classifies items It classifies items in 7 EEMM dimensions following (ref).
+                    ¡Hello! This use two machine learning models to classify items and phrases into the EEMM grid. It will output a classification for dimensions as Cognition, Self, Affect, Motivation, Attention, Overt Behavior or Context. It Will also output levels as Social, Psychological or Physical. 
 
-                      **Please note that the machine goes idle after a period of inactivity. If this occurs, waking it up may take around 20 seconds. Be patient ;)**""",
+                    This is a proof of concept, so the model will not be perfect right away and may make some mistakes. **You can help us improve the models by flagging wrong answers**. Using the buttons below the output you can tell us the output lacks a category, or if levels and/or dimensions are wrong. When flagging, your item/phrase will be saved with your answer. We only save flagged items.
+
+                    **Please note that the machine goes idle after a period of inactivity.** If this occurs, waking it up may take around 20 seconds. Be patient ;)""",
                     article = article_text,
                     allow_flagging = 'manual',
-                    flagging_options = ['Wrong category','Lacks a category'],
+                    flagging_options = ['Wrong dimension/level','Lacks dimension/level', 'Wrong and lacking :('],
                     flagging_callback= hf_writer
 )
 demo.launch()