Update app.py

app.py

@@ -2,23 +2,27 @@ import gradio as gr
 import os
 import pandas as pd
 
+path_to_generation_model = str(os.environ['path_to_generation_model'])
+
 path_to_L_model = str(os.environ['path_to_L_model'])
 path_to_S_model = str(os.environ['path_to_S_model'])
+
 read_token      = str(os.environ['read_token'])
 read_token_ii   = str(os.environ['read_token_ii'])
 
 languages = pd.read_csv("model_lang.csv", names=["Lang_acr"])
 
+
 def check_lang(lang_acronym):
     if lang_acronym in languages["Lang_acr"].to_list():
         return "True"
     else: 
         return "False"
     
-title = "DSA: version II"
+title = "DSA: version III"
 
 description_main = """
-A set of pre-trained LLMs tuned to perform sentiment analysis. You can choose between a Large-Multilingual or Base-English-only model. 
+A set of pre-trained LLMs tuned to perform sentiment analysis. You can choose between a Multilingual or English-only. 
 
 Use the current interface to check if a language is included in the multilingual model, using language acronyms (e.g. it for Italian).
 Click on one of the upper buttons to select and start querying one of the two models.
@@ -32,6 +36,10 @@ description_S = """
 A BERT-base-cased model pre-trained and tuned on English data.
 """
 
+description_G = """
+A t5 model tuned to performer text-generation, and predict emotion as well as the character experiencing those emotions.
+"""
+
 example_main = ["en", "it", "pl"]
 
 examples = [
@@ -40,6 +48,12 @@ examples = [
     ["Śledził mnie niebieski potwór, ale się nie bałem. Byłem spokojny i zrelaksowany."],
 ]
 
+examples_g = [
+    ["I'm in an auditorium. Susie S is concerned at her part in this disability awareness spoof we are preparing. I ask, 'Why not do it? Lots of AB's represent us in a patronizing way. Why shouldn't we represent ourselves in a good, funny way?' I watch the video we all made. It is funny. I try to sit on a folding chair. Some guy in front talks to me. Merle is in the audience somewhere. [BL]"],
+
+]
+
+
 interface_words = gr.Interface(
             fn=check_lang,
             inputs="text", 
@@ -61,11 +75,19 @@ interface_model_S = gr.Interface.load(
             name=path_to_S_model,
             description=description_S,
             examples=examples[0],
-            title="DSA Small English Only",
+            title="DSA Base English-Only",
+            api_key=read_token_ii,
+)
+
+interface_model_G = gr.Interface.load(
+            name=path_to_generation_model,
+            description=description_G,
+            examples=examples_g,
+            title="DSA Generation",
             api_key=read_token_ii,
 )
 
 gr.TabbedInterface(
-    [interface_words, interface_model_L, interface_model_S], 
-    ["Intro", "Large Multilingual", "Base En"]
+    [interface_words, interface_model_L, interface_model_S, interface_model_G], 
+    ["Intro", "Large Multilingual", "Base En", "En Generation"]
 ).launch()