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import gradio as gr
import os
from mtranslate import translate
import requests
HF_AUTH_TOKEN = os.environ.get("HF_AUTH_TOKEN")
text_generator_api = 'https://cahya-indonesian-whisperer.hf.space/api/text-generator/v1'
text_generator_api_auth_token = os.getenv("TEXT_GENERATOR_API_AUTH_TOKEN", "")
def get_answer(user_input, decoding_method, num_beams, top_k, top_p, temperature, repetition_penalty, penalty_alpha):
print(user_input, decoding_method, top_k, top_p, temperature, repetition_penalty, penalty_alpha)
headers = {'Authorization': 'Bearer ' + text_generator_api_auth_token}
data = {
"model_name": "bloomz-1b1-instruct",
"text": user_input,
"min_length": len(user_input) + 10,
"max_length": len(user_input) + 200,
"decoding_method": decoding_method,
"num_beams": num_beams,
"top_k": top_k,
"top_p": top_p,
"temperature": temperature,
"seed": -1,
"repetition_penalty": repetition_penalty,
"penalty_alpha": penalty_alpha
}
r = requests.post(text_generator_api, headers=headers, data=data)
if r.status_code == 200:
result = r.json()
answer = result["generated_text"]
user_input_en = translate(user_input, "en", "auto")
answer_en = translate(answer, "en", "auto")
return [(f"{user_input}\n", None), (answer, "")], \
[(f"{user_input_en}\n", None), (answer_en, "")]
else:
return "Error: " + r.text
css = """
#answer_id span {white-space: pre-line}
#answer_id span.label {display: none}
#answer_en span {white-space: pre-line}
#answer_en span.label {display: none}
"""
with gr.Blocks(css=css) as demo:
with gr.Row():
gr.Markdown("""## Bloomz-1b7-Instruct
We fine-tuned the BigScience model Bloomz-1b7 with cross-lingual instructions dataset. Some of the supported
languages are: English, Indonesian, Vietnam, Hindi, Spanish, French, and Chinese.
""")
with gr.Row():
with gr.Column():
user_input = gr.inputs.Textbox(placeholder="",
label="Ask me something",
default="Will we ever cure cancer? Please answer in Chinese.")
decoding_method = gr.inputs.Dropdown(["Beam Search", "Sampling"],
default="Sampling", label="Decoding Method")
num_beams = gr.inputs.Slider(label="Number of beams for beam search",
default=1, minimum=1, maximum=10, step=1)
top_k = gr.inputs.Slider(label="Top K",
default=30, maximum=50, minimum=1, step=1)
top_p = gr.inputs.Slider(label="Top P", default=0.9, step=0.05, minimum=0.1, maximum=1.0)
temperature = gr.inputs.Slider(label="Temperature", default=0.5, step=0.05, minimum=0.1, maximum=1.0)
repetition_penalty = gr.inputs.Slider(label="Repetition Penalty", default=1.1, step=0.05, minimum=1.0, maximum=2.0)
penalty_alpha = gr.inputs.Slider(label="The penalty alpha for contrastive search",
default=0.5, step=0.05, minimum=0.05, maximum=1.0)
with gr.Row():
button_generate_story = gr.Button("Submit")
with gr.Column():
# generated_answer = gr.Textbox()
generated_answer = gr.HighlightedText(
elem_id="answer_id",
label="Generated Text",
combine_adjacent=True,
css="#htext span {white-space: pre-line}",
).style(color_map={"": "blue", "-": "green"})
generated_answer_en = gr.HighlightedText(
elem_id="answer_en",
label="Translation",
combine_adjacent=True,
).style(color_map={"": "blue", "-": "green"})
with gr.Row():
gr.Markdown("")
button_generate_story.click(get_answer,
inputs=[user_input, decoding_method, num_beams, top_k, top_p, temperature,
repetition_penalty, penalty_alpha],
outputs=[generated_answer, generated_answer_en])
demo.launch(enable_queue=False) |