Update app.py

app.py

@@ -38,11 +38,15 @@ A BERT-base-cased model pre-trained on Eglish-only text and tuned on annotated D
 """
 
 description_G = """
-A T5 model tuned to performer text generation, and predict emotion as well as the character experiencing those emotions.
+A T5 model tuned to perform text generation and predict emotion as well as the character experiencing those emotions.
+"""
+
+description_R = """
+A T5 model tuned to perform text generation and predicts the characters and the (activity) relation between them.
 """
 
 description_GNER = """
-A T5 model tuned to performer text generation, and predict which characters are present in the report. Note that, in the Hall and Van de Castle, the character lists never includes the dreamer. Hence, if you (willingly or not) enter a report that does not contain another character reference, the model will/should (correctly) produce an empty string. Moreover, it is likely that the produced list of CHAR could be longer than the one produced by the SA model, as not all CHAR might be associated with emotions. 
+A T5 model tuned to perform text generation, and predict which characters are present in the report. Note that, in the Hall and Van de Castle, the character lists never includes the dreamer. Hence, if you (willingly or not) enter a report that does not contain another character reference, the model will/should (correctly) produce an empty string. Moreover, it is likely that the produced list of CHAR could be longer than the one produced by the SA model, as not all CHAR might be associated with emotions. 
 """
 
 example_main = ["en", "it", "pl"]
@@ -94,7 +98,7 @@ interface_model_G = gr.Interface.load(
 
 interface_model_RE = gr.Interface.load(
             name="huggingface/DReAMy-lib/t5-base-DreamBank-Generation-Act-Char",
-            description=description_G,
+            description=description_R,
             examples=examples_re,
             title="RE Generation",
 )