Update app.py

app.py

@@ -7,7 +7,7 @@ tokenizer = AutoTokenizer.from_pretrained("facebook/m2m100_418M")
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
 LANG_CODES = {
     "English":"en",
-    "toki pona":"tl"
+    "Zelsik":"tl"
 }
 
 def translate(text, src_lang, tgt_lang, candidates:int):
@@ -41,13 +41,11 @@ def translate(text, src_lang, tgt_lang, candidates:int):
 
 with gr.Blocks() as app:
     markdown="""
-    # An English / toki pona Neural Machine Translation App!
+    # An English / Zelsik Neural Machine Translation App!
     
-    ### toki a! 💬
+    This is an English to Zelsik / Zelsik to English neural machine translation app.
 
-    This is an english to toki pona / toki pona to english neural machine translation app.
-
-    Input your text to translate, a source language and target language, and desired number of return sequences! 
+    Input your text to translate, a source language and target language, and the desired number of return sequences! 
 
     ### Grammar Regularization
     An interesting quirk of training a many-to-many translation model is that pseudo-grammar correction 
@@ -55,23 +53,20 @@ with gr.Blocks() as app:
     
     Remember, this can ***approximate*** grammaticality, but it isn't always the best.
     
-    For example, "mi li toki e toki pona" (Source Language: toki pona & Target Language: toki pona) will result in: 
-    - ['mi toki e toki pona.', 'mi toki pona.', 'mi toki e toki pona']
-    - (Thus, the ungrammatical "li" is dropped)
 
     ### Model and Data
-    This app utilizes a fine-tuned version of Facebook/Meta AI's M2M100 418M param model. 
+    This app utilizes a fine-tuned version of Facebook/Meta AI's M2M100 418M param model and the original app was made by Jayyydyyy for Toki Pona.
     
-    By leveraging the pretrained weights of the massively multilingual M2M100 model, 
-    we can jumpstart our transfer learning to accomplish machine translation for toki pona!
+    By leveraging the pre-trained weights of the massively multilingual M2M100 model, 
+    we can jumpstart our transfer learning to accomplish machine translation for Zelsik!
     
-    The model was fine-tuned on the English/toki pona bitexts found at [https://tatoeba.org/](https://tatoeba.org/)
+    The model was fine-tuned on the English/Zelsik bitexts found at [https://tatoeba.org/](https://tatoeba.org/)
     
-    ### This app is a work in progress and obviously not all translations will be perfect. 
+    ### This app is a work in progress; obviously, not all translations will be perfect. 
     In addition to parameter quantity and the hyper-parameters used while training, 
-    the *quality of data* found on Tatoeba directly influences the perfomance of projects like this! 
+    the *quality of data* found on Tatoeba directly influences the performance of projects like this! 
 
-    If you wish to contribute, please add high quality and diverse translations to Tatoeba!
+    im sorry jayyydyyy, im too lazy and dumb to change any of the descriptions
     """
 
     with gr.Row():
@@ -79,7 +74,7 @@ with gr.Blocks() as app:
         with gr.Column():
             input_text = gr.components.Textbox(label="Input Text", value="Raccoons are fascinating creatures, but I prefer opossums.")
             source_lang = gr.components.Dropdown(label="Source Language", value="English", choices=list(LANG_CODES.keys()))
-            target_lang = gr.components.Dropdown(label="Target Language", value="toki pona", choices=list(LANG_CODES.keys()))
+            target_lang = gr.components.Dropdown(label="Target Language", value="Zelsik", choices=list(LANG_CODES.keys()))
             return_seqs = gr.Slider(label="Number of return sequences", value=3, minimum=1, maximum=12, step=1)
             
             inputs=[input_text, source_lang, target_lang, return_seqs]
@@ -90,9 +85,9 @@ with gr.Blocks() as app:
 
             gr.Examples(
                 [
-                    ["Hello! How are you?", "English", "toki pona", 3],
-                    ["toki a! ilo pi ante toki ni li pona!", "toki pona", "English",  3],
-                    ["mi li toki e toki pona", "toki pona", "toki pona", 3],
+                    ["Hello! How are you?", "English", "Zelsik", 3],
+                    ["toki a! ilo pi ante toki ni li pona!", "Zelsik", "English",  3],
+                    ["mi li toki e Zelsik", "Zelsik", "Zelsik", 3],
                 ],
                 inputs=inputs
             )