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import gradio as gr
from gradio.components import Slider
import torch
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
# Model, information and examples ----------------------------------------------
model_id = "proxectonos/FLOR-1.3B-GL"
title = "Modelo de xeración de texto FLOR-1.3B-GL"
markdown_description = """
# FLOR-1.3B-GL
🪷 **[FLOR-1.3B-GL](https://huggingface.co/proxectonos/FLOR-1.3B-GL)** is a 1.3B parameters multilingual LLM for Galician language.
👀 **Learn more about FLOR-1.3B:** [HF official model card](https://huggingface.co/proxectonos/FLOR-1.3B-GL) and the [Proxecto Nós](https://nos.gal/en/proxecto-nos).
"""
short_prompts_examples = [
["A receita tradicional das filloas é"],
["O neno vivía preto de"]
]
few_shot_prompts_examples = [
["Responde á seguinte pregunta. \nPregunta: \"Cal é a capital de Noruega? \"\nResposta: \"A capital de Noruega é Oslo.\"\n---- \nResponde á seguinte pregunta.\nPregunta: \"Cal é a moeda de Portugal\" \nResposta: \"A moeda de Portugal é o euro.\" \n---- \nResponde á seguinte pregunta. \nPregunta: \"Cal é a capital de Suecia?\"\nResposta:"],
["Extrae as entidades nomeadas do seguinte texto: \nTexto: \"Chámome Wolfgang e vivo en Berlin\" \nEntidades: Wolfgang:PER, Berlin:LOC \n ---- \nExtrae as entidades nomeadas do seguinte texto: \nTexto: \"María e Miguel non teñen ningún problema\" \nEntidades: María:PER, Miguel:PER \n---- \nExtrae as entidades nomeadas do seguinte texto: \nTexto: \"O mellor de Barcelona é o bar do meu amigo Pablo\" \nEntidades: Pablo:PER, Barcelona:LOC \n---- \nExtrae as entidades nomeadas do seguinte texto: \nTexto: \"Carlos comparte cuarto con Marc\" \nEntidades:"]
]
fronted_theme = 'Soft'
# Model charge ---------------------------------------------------------
model_id = "proxectonos/FLOR-1.3B-GL"
generator_model = pipeline("text-generation", model=model_id)
# Generation functions ---------------------------------------------------------
def remove_empty_lines(text):
lines = text.strip().split("\n")
non_empty_lines = [line for line in lines if line.strip()]
return "\n".join(non_empty_lines)
def predict(prompt, max_length, repetition_penalty=1.3):
print("Dentro da xeración...")
prompt_length = len(generator_model.tokenizer.encode(prompt))
generated_text = generator_model(
prompt,
max_length=prompt_length + max_length,
pad_token_id=generator_model.tokenizer.eos_token_id,
repetition_penalty=repetition_penalty)
generated_sequence = generated_text[0]['generated_text']
if generated_sequence is None:
gr.Warning('Inference endpoint is not available right now. Please try again later.')
return
generated_sequence = remove_empty_lines(generated_sequence)
print("Xeración completada")
return generated_sequence
# Gradio app ---------------------------------------------------------
def clear():
return (
None,
None,
gr.update(value=20),
gr.update(value=1.3)
)
def pass_to_input(generated_gl):
return (
gr.update(value=generated_gl),
None,
)
def fewshot_prompt_parameters():
return (
gr.update(value=20), # max_length
gr.update(value=1) # repetition_penalty
)
def gradio_app():
with gr.Blocks(theme=fronted_theme) as demo:
with gr.Row():
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)
with gr.Row(equal_height=True):
with gr.Column():
text_gl = gr.Textbox(label="Input",
lines=6, placeholder="e.g. O neno vai a escola con ")
with gr.Row(variant="panel"):
with gr.Accordion("Model parameters", open=False):
max_length = Slider(
minimum=1,
maximum=200,
step=1,
value=30,
label="Max tokens"
)
repetition_penalty = Slider(
minimum=0.1,
maximum=4,
step=0.1,
value=1.3,
label="Repetition penalty"
)
generator_btn = gr.Button(value="Generate",variant='primary')
with gr.Column():
generated_gl = gr.Textbox(label="Output",
lines=6,
placeholder="Generated text will appear here",
interactive=False,
show_copy_button=True)
pass_btn = gr.Button(value="Pass text to input")
clean_btn = gr.Button(value="Clean")
generator_btn.click(predict, inputs=[text_gl,max_length, repetition_penalty], outputs=generated_gl, api_name="generate-flor-gl")
clean_btn.click(fn=clear, inputs=[], outputs=[text_gl, generated_gl, max_length, repetition_penalty], queue=False, api_name=False)
pass_btn.click(fn=pass_to_input, inputs=[generated_gl], outputs=[text_gl,generated_gl], queue=False, api_name=False)
with gr.Row():
with gr.Column(scale=0.5):
gr.Examples(
label = "Short prompts",
examples = short_prompts_examples,
inputs = [text_gl,max_length, repetition_penalty],
outputs = generated_gl,
fn =predict
)
gr.Examples(
label = "Few-shot prompts",
examples = few_shot_prompts_examples,
inputs = [],
outputs = [max_length, repetition_penalty],
fn =fewshot_prompt_parameters
)
demo.launch()
if __name__ == "__main__":
gradio_app() |