Add multilingual texts description funcionality

app.py

@@ -3,10 +3,11 @@ import gradio as gr
 from gradio.components import Slider
 import torch
 from transformers import pipeline
+import pandas as pd
 
 # Model, information and examples ----------------------------------------------
 MODEL_NAMES = ["FLOR-1.3B-GL","Cerebras-1.3B-GL"]
-markdown_description = """
+markdown_description_en = """
 # Galician LLMs
 
 
@@ -23,6 +24,24 @@ This space contains the Galician language models developed by [Proxecto Nós](ht
 👀 **Learn more about Cerebras-1.3B-GL:** [HF official model card](https://huggingface.co/proxectonos/Cerebras-1.3B-GL)
 """
 
+markdown_description_gl = """
+# LLMs de galego
+
+
+Este espazo grandes modelos da linguaxe feitos para o galego desenvolvidos polo [Proxecto Nós](https://nos.gal/en/proxecto-nos).
+
+
+💐 **[FLOR-1.3B-GL](https://huggingface.co/proxectonos/FLOR-1.3B-GL)** é un modelo de parámetros 1.3B que é un preadestramento continuo de [FLOR-1.3B]( https://huggingface.co/projecte-aina/FLOR-1.3B), baseado a súa vez en [Bloom 1.7B](https://huggingface.co/bigscience/bloom-1b7).
+
+👀 **Máis información sobre FLOR-1.3B-GL:** [tarxeta modelo oficial HF](https://huggingface.co/proxectonos/FLOR-1.3B-GL).
+
+
+🧠 **[Cerebras-1.3B-GL](https://huggingface.co/proxectonos/Cerebras-1.3B-GL)** é un modelo de parámetros 1.3B baseado en [Cerebras-GPT 1.3B](https:/ /huggingface.co/cerebras/Cerebras-GPT-1.3B).
+
+👀 **Máis información sobre Cerebras-1.3B-GL:** [tarxeta modelo oficial HF](https://huggingface.co/proxectonos/Cerebras-1.3B-GL)
+"""
+
+markdown_description ={"en": markdown_description_en,"gl": markdown_description_gl}
 short_prompts_examples = [
     ["A receita tradicional das filloas é"], 
     ["O neno vivía preto de"]
@@ -41,6 +60,10 @@ generator_model_flor = pipeline("text-generation", model=model_id_flor)
 model_id_cerebras  = "proxectonos/Cerebras-1.3B-GL"
 generator_model_cerebras = pipeline("text-generation", model=model_id_cerebras, token=os.environ['TOKEN_HF'])
 
+# Load language texts ---------------------------------------------------------
+df_texts = pd.read_csv("multilingual_texts.csv")
+lang = "gl"
+
 # Generation functions ---------------------------------------------------------
 def get_model(model_selection):
     if model_selection == "FLOR-1.3B-GL":
@@ -75,6 +98,9 @@ def predict(prompt, model_select, max_length, repetition_penalty, temperature):
     return generated_sequence
 
 # Gradio app ---------------------------------------------------------
+def get_text_lang(variable):
+    return df_texts.loc[df_texts['variable'] == variable, lang].values[0]
+
 def clear(): 
     return (
         None, 
@@ -109,49 +135,49 @@ def gradio_app():
             with gr.Column(scale=0.1):
                 gr.HTML('<img src="https://huggingface.co/spaces/proxectonos/README/resolve/main/title-card.png" width="100%" style="border-radius: 0.75rem;">')
             with gr.Column():
-                gr.Markdown(markdown_description)
+                gr.Markdown(markdown_description[lang])
         with gr.Row(equal_height=True):            
                 model_select = gr.Dropdown(
-                    label="Escolle un modelo:",
+                    label=get_text_lang["model_select"],
                     choices=MODEL_NAMES,
                     value=MODEL_NAMES[0],
                     interactive=True
                 )
         with gr.Row(equal_height=True):
             with gr.Column():
-                text_gl = gr.Textbox(label="Input", 
+                text_gl = gr.Textbox(label=get_text_lang["text_gl"], 
                                      lines=6, placeholder="e.g. O neno vai a escola con ")
                 with gr.Row(variant="panel"):
-                        with gr.Accordion("Model parameters", open=False):
+                        with gr.Accordion(get_text_lang["accordion_parameters"], open=False):
                             max_length = Slider(
                                 minimum=1,
                                 maximum=200,
                                 step=1,
                                 value=30,
-                                label="Max tokens"
+                                label=get_text_lang["max_length"]
                             )
                             repetition_penalty = Slider(
                                 minimum=0.1,
                                 maximum=4,
                                 step=0.1,
                                 value=1.3,
-                                label="Repetition penalty"
+                                label=get_text_lang["repetition_penalty"]
                             )
                             temperature = Slider(
                                 minimum=0,
                                 maximum=1,
                                 value=0.5,
-                                label="Temperatura"
+                                label=get_text_lang["temperature"]
                             )
-                generator_btn = gr.Button(value="Generate",variant='primary')
+                generator_btn = gr.Button(value=get_text_lang["generator_btn"],variant='primary')
             with gr.Column():
-                generated_gl = gr.Textbox(label="Output", 
+                generated_gl = gr.Textbox(label=get_text_lang["generated_gl_label"], 
                                           lines=6, 
-                                          placeholder="Generated text will appear here",
+                                          placeholder=get_text_lang["generated_gl_placeholder"],
                                           interactive=False,
                                           show_copy_button=True)
-                pass_btn = gr.Button(value="Pass text to input")
-                clean_btn = gr.Button(value="Clean")
+                pass_btn = gr.Button(value=get_text_lang["pass_btn"])
+                clean_btn = gr.Button(value=get_text_lang["clean_btn"])
 
         generator_btn.click(predict, inputs=[text_gl, model_select, max_length, repetition_penalty, temperature], outputs=generated_gl, api_name="generate-flor-gl")
         clean_btn.click(fn=clear, inputs=[], outputs=[text_gl, generated_gl, max_length, repetition_penalty, temperature], queue=False, api_name=False)
@@ -160,7 +186,7 @@ def gradio_app():
         with gr.Row():
             with gr.Column(scale=0.5):
                 gr.Examples(
-                    label = "Short prompts",
+                    label = get_text_lang["examples_short_prompts"],
                     examples = short_prompts_examples,
                     inputs = [text_gl],
                     outputs = [max_length, repetition_penalty, temperature],
@@ -168,7 +194,7 @@ def gradio_app():
                     run_on_click = True
                 )
                 gr.Examples(
-                    label = "Few-shot prompts",
+                    label = get_text_lang["examples_few_shot"],
                     examples = few_shot_prompts_examples,
                     inputs = [text_gl],
                     outputs = [max_length, repetition_penalty, temperature],

multilingual_texts.csv

@@ -0,0 +1,14 @@
+variable,en,gl
+model_select,Model selection,Seleccione un modelo
+text_gl,Input,Entrada
+accordion_parameters,Model parameters,Parámetros do modelo
+max_length,Max tokens,Max tokens
+repetition_penalty,Repetition penalty,Penalización por repetición
+temperature,Temperature,Temperatura
+generator_btn,Generate,Xerar
+generated_gl_label,Output,Saída
+generated_gl_placeholder,Generated text will appear here...,O texto xerado aparecerá aquí...
+pass_btn,Pass text to input,Pasar texto á entrada
+clean_btn,Clean,Limpar
+examples_short_prompts,Short Prompts,Prompts curtos
+examples_few_shot,Few-Shot Prompts,Prompts con poucos exemplos