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import os
from dotenv import load_dotenv
import gradio as gr
from gradio.components import Textbox, Button, Slider, Checkbox
from AinaTheme import theme
from huggingface_hub import InferenceClient
load_dotenv()
def generate(prompt, model_parameters):
output = client.text_generation(prompt, **model_parameters, return_full_text=True)
return output
client = InferenceClient(
os.environ.get("HF_INFERENCE_ENDPOINT_URL"),
token=os.environ.get("HF_INFERENCE_ENDPOINT_TOKEN")
)
MAX_NEW_TOKENS = int(os.environ.get("MAX_NEW_TOKENS", default=100))
MAX_INPUT_CHARACTERS= int(os.environ.get("MAX_INPUT_CHARACTERS", default=100))
SHOW_MODEL_PARAMETERS_IN_UI = os.environ.get("SHOW_MODEL_PARAMETERS_IN_UI", default=True) == "True"
def submit_input(input_, max_new_tokens, repetition_penalty, top_k, top_p, do_sample, temperature):
if input_.strip() == "":
gr.Warning('Not possible to inference an empty input')
return None
model_parameters = {
"max_new_tokens": max_new_tokens,
"repetition_penalty": repetition_penalty,
"top_k": top_k,
"top_p": top_p,
"do_sample": do_sample,
"temperature": temperature
}
output = generate(input_, model_parameters)
if output is None:
gr.Warning('Inference endpoint is not available right now. Please try again later.')
return output
def change_interactive(text):
if len(text.strip()) > MAX_INPUT_CHARACTERS:
return gr.update(interactive = True), gr.update(interactive = False)
return gr.update(interactive = True), gr.update(interactive = True)
def clear():
return (
None,
None,
gr.update(value=MAX_NEW_TOKENS),
gr.update(value=1.2),
gr.update(value=50),
gr.update(value=0.95),
gr.update(value=True),
gr.update(value=0.5),
)
def gradio_app():
with gr.Blocks(theme=theme) as demo:
with gr.Row():
with gr.Column(scale=0.1):
gr.Image("ginesta_small.jpg", elem_id="flor-banner", scale=1, height=256, width=256, show_label=False, show_download_button = False, show_share_button = False)
with gr.Column():
gr.Markdown(
"""# Flor-6.3B (experimental)
🪷 **[Flor](https://huggingface.co/projecte-aina/FLOR-6.3B)** is a 6.3B parameters multilingual LLM that has been trained on a massive mixture of Spanish, Catalan and English data. It is a new open-source Large Language Model (LLM), licensed for both research and commercial use. It uses the [Bloom-7b](https://huggingface.co/bigscience/bloom-7b1) model as a starting point, a state-of-the-art multilingual language model.
⚠️ **Limitations**: This version is for beta testing only. The content generated by these models is unsupervised and might be judged as inappropriate or offensive. Please bear this in mind when exploring this resource.
👀 **Learn more about Flor:** [HF official model card](https://huggingface.co/projecte-aina/FLOR-6.3B) and the [Instruct version](https://huggingface.co/projecte-aina/FLOR_63B_Instruit).
"""
)
with gr.Row(equal_height=True):
with gr.Column(variant="panel"):
placeholder_max_token = Textbox(
visible=False,
interactive=False,
value= MAX_INPUT_CHARACTERS
)
input_ = Textbox(
lines=11,
label="Input",
placeholder="e.g. El mercat del barri és fantàstic hi pots trobar."
)
with gr.Row(variant="panel", equal_height=True):
gr.HTML("""<span id="countertext" style="display: flex; justify-content: start; color:#ef4444; font-weight: bold;"></span>""")
gr.HTML(f"""<span id="counter" style="display: flex; justify-content: end;"> <span id="inputlenght">0</span> / {MAX_INPUT_CHARACTERS}</span>""")
with gr.Row(variant="panel"):
with gr.Accordion("Model parameters", open=False, visible=SHOW_MODEL_PARAMETERS_IN_UI):
max_new_tokens = Slider(
minimum=1,
maximum=200,
step=1,
value=MAX_NEW_TOKENS,
label="Max tokens"
)
repetition_penalty = Slider(
minimum=0.1,
maximum=10,
step=0.1,
value=1.2,
label="Repetition penalty"
)
top_k = Slider(
minimum=1,
maximum=100,
step=1,
value=50,
label="Top k"
)
top_p = Slider(
minimum=0.01,
maximum=0.99,
value=0.95,
label="Top p"
)
do_sample = Checkbox(
value=True,
label="Do sample"
)
temperature = Slider(
minimum=0,
maximum=1,
value=0.5,
label="Temperature"
)
with gr.Column(variant="panel"):
output = Textbox(
lines=11,
label="Output",
interactive=False,
show_copy_button=True
)
with gr.Row(variant="panel"):
clear_btn = Button(
"Clear",
)
submit_btn = Button(
"Submit",
variant="primary",
)
with gr.Row():
with gr.Column(scale=0.5):
gr.Examples(
label="Short prompts:",
examples=[
["""La capital de Suècia"""],
],
inputs=[input_, max_new_tokens, repetition_penalty, top_k, top_p, do_sample, temperature],
outputs=output,
fn=submit_input,
)
gr.Examples(
label="Zero-shot prompts",
examples=[
["Tradueix del Castellà al Català la següent frase: \"Eso es pan comido.\" \nTraducció:"],
],
inputs=[input_, max_new_tokens, repetition_penalty, top_k, top_p, do_sample, temperature],
outputs=output,
fn=submit_input,
)
gr.Examples(
label="Few-Shot prompts:",
examples=[
["""Oració: Els sons melòdics produeixen una sensació de calma i benestar en l'individu. \nParàfrasi: La música és molt relaxant i reconfortant.\n----\nOració: L'animal domèstic mostra una gran alegria i satisfacció. \nParàfrasi: El gos és molt feliç. \n----\nOració: El vehicle es va trencar i vaig haver de contactar amb el servei de remolc perquè el transportés. \nParàfrasi: El cotxe es va trencar i vaig haver de trucar la grua. \n----\nOració: El professor va explicar els conceptes de manera clara i concisa. \nParàfrasi:"""],
],
inputs=[input_, max_new_tokens, repetition_penalty, top_k, top_p, do_sample, temperature],
outputs=output,
fn=submit_input,
)
input_.change(fn=change_interactive, inputs=[input_], outputs=[clear_btn, submit_btn], api_name=False)
input_.change(fn=None, inputs=[input_], api_name=False, js=f"""(i) => document.getElementById('countertext').textContent = i.length > {MAX_INPUT_CHARACTERS} && 'Max length {MAX_INPUT_CHARACTERS} characters. ' || '' """)
input_.change(fn=None, inputs=[input_, placeholder_max_token], api_name=False, js="""(i, m) => {
document.getElementById('inputlenght').textContent = i.length + ' '
document.getElementById('inputlenght').style.color = (i.length > m) ? "#ef4444" : "";
}""")
clear_btn.click(fn=clear, inputs=[], outputs=[input_, output, max_new_tokens, repetition_penalty, top_k, top_p, do_sample, temperature], queue=False, api_name=False)
submit_btn.click(fn=submit_input, inputs=[input_, max_new_tokens, repetition_penalty, top_k, top_p, do_sample, temperature], outputs=[output], api_name="get-results")
demo.launch(show_api=True)
if __name__ == "__main__":
gradio_app() |