Update app.py

app.py

@@ -57,6 +57,8 @@ examples = [
     "Śledził mnie niebieski potwór, ale się nie bałem. Byłem spokojny i zrelaksowany.",
 ]
 
+#############################
+
 interface_words = gr.Interface(
         fn=check_lang,
         inputs="text", 
@@ -66,13 +68,31 @@ interface_words = gr.Interface(
         examples=["en", "it", "pl"],
         cache_examples=True,
 )
-    
-# interface_model_L = gr.Interface(
-#             description=description_L,
-#             examples=examples,
-#             title="SA Large Multilingual",
-# )
-# interface_model_L.load("models/DReAMy-lib/xlm-roberta-large-DreamBank-emotion-presence")
+
+#############################
+
+pipe_L = pipeline(
+    "text-classification",
+    model="models/DReAMy-lib/xlm-roberta-large-DreamBank-emotion-presence",
+    max_length=300,
+    return_all_scores=True,
+    truncation="do_not_truncate",
+)
+def predictL(text):
+    t = pipe_SL(text)
+    t = {list(dct.values())[0] : list(dct.values())[1] for dct in t[0]}
+    return t
+interface_model_L =  gr.Interface(
+        fn=predictL,
+        inputs='text',
+        outputs=gr.Label(),
+        title="SA Large Multilingual",
+        description=description_L,
+        examples=examples,
+        cache_examples=True,
+)
+
+#############################
 
 pipe_S = pipeline(
     "text-classification",
@@ -95,12 +115,16 @@ interface_model_S =  gr.Interface(
         cache_examples=True,
 )
 
+#############################
+
 # interface_model_G = gr.Interface.load(
 #             "models/DReAMy-lib/t5-base-DreamBank-Generation-Emot-Char",
 #             examples=examples_g,
 #             title="SA Generation",
 # )
 
+#############################
+
 interface_model_RE = gr.Interface(
         text_to_graph,
         inputs=gr.Textbox(label="Text", placeholder="Enter a text here."),
@@ -115,13 +139,31 @@ interface_model_RE = gr.Interface(
         cache_examples=True,
 )
 
-# interface_model_NER = gr.Interface.load(
-#             "models/DReAMy-lib/t5-base-DreamBank-Generation-NER-Char",
-#             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]"]
-#             title="NER Generation",
-# )
+#############################
+
+pipe_N = pipeline(
+    ""text2text-generation"",
+    model="models/DReAMy-lib/t5-base-DreamBank-Generation-NER-Char",
+    max_length=300,
+    return_all_scores=True,
+    truncation="do_not_truncate",
+)
+def predictN(text):
+    t = pipe_N(text)
+    return t
+interface_model_N =  gr.Interface(
+        fn=predictN,
+        inputs='text',
+        outputs='text',
+        title="NER",
+        description=description_L,
+        examples=examples,
+        cache_examples=True,
+)
+
+#############################
 
 gr.TabbedInterface(
-    [interface_words, interface_model_S, interface_model_RE], 
-    ["Main", "SA English", "RE"],
+    [interface_words, interface_model_N, interface_model_L, interface_model_S, interface_model_RE], 
+    ["Main", "NER", "SA Multilingual", "SA English", "RE"],
 ).launch()