Update app.py

app.py

@@ -16,12 +16,12 @@ description_main = """
 This space allows you to test a set of LLMs tuned to perform different tasks over dream reports. 
 Three main tasks are available: 
 
+- Name Entity Recognition (NER), with an English-only model that generates the identified characters. 
+
 - Sentiment Analysis (SA), with two English-only models (one for classification, one for generation) and a large multilingual model for classification.
 
 - Relation Extraction (RE), with an English-only model that identifies relevant characters and existing relations between them following the Activity feature of the Hall and Van de Castle framework.
 
-- Name Entity Recognition (NER), with an English-only model that generates the identified characters. 
-
 All models have been tuned on the Hall and Van de Castle framework. More details are on the page for each model. For more on the training framework, see the [Bertolini et al., 2023](https://arxiv.org/pdf/2302.14828.pdf) preprint.
 
 Use the current interface to check if a language is included in the multilingual SA model, using language acronyms (e.g. it for Italian). the tabs above will direct you to each model to query.
@@ -77,6 +77,13 @@ interface_words = gr.Interface(
             examples=example_main,
 )
 
+interface_model_G = gr.Interface.load(
+            name="huggingface/DReAMy-lib/t5-base-DreamBank-Generation-Emot-Char",
+            description=description_G,
+            examples=examples_g,
+            title="SA Generation",
+)
+
 interface_model_L = gr.Interface.load(
             name="huggingface/DReAMy-lib/xlm-roberta-large-DreamBank-emotion-presence",
             description=description_L,
@@ -91,13 +98,6 @@ interface_model_S = gr.Interface.load(
             title="SA Base English-Only",
 )
 
-interface_model_G = gr.Interface.load(
-            name="huggingface/DReAMy-lib/t5-base-DreamBank-Generation-Emot-Char",
-            description=description_G,
-            examples=examples_g,
-            title="SA Generation",
-)
-
 interface_model_RE = gr.Interface.load(
             name="huggingface/DReAMy-lib/t5-base-DreamBank-Generation-Act-Char",
             description=description_R,
@@ -115,5 +115,5 @@ interface_model_NER = gr.Interface.load(
 
 gr.TabbedInterface(
     [interface_words, interface_model_L, interface_model_S, interface_model_G, interface_model_RE, interface_model_NER], 
-    ["Main", "SA Large Multilingual", "SA Base En", "SA En Generation", "RE Generation", "NER Generation"]
+    ["Main", "NER Generation", "SA Large Multilingual", "SA Base En", "SA En Generation", "RE Generation"]
 ).launch()