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import torch
import gradio as gr
from transformers import pipeline
import os
from mtranslate import translate
device = torch.cuda.current_device() if torch.cuda.is_available() else "cpu"
HF_AUTH_TOKEN = os.environ.get("HF_AUTH_TOKEN")
text_generation_model = "cahya/indochat-tiny"
text_generation = pipeline("text-generation", text_generation_model, use_auth_token=HF_AUTH_TOKEN, device=device)
def get_answer(user_input, decoding_methods, num_beams, top_k, top_p, temperature, repetition_penalty, penalty_alpha):
if decoding_methods == "Beam Search":
do_sample = False
penalty_alpha = 0
elif decoding_methods == "Sampling":
do_sample = True
penalty_alpha = 0
num_beams = 1
else:
do_sample = False
num_beams = 1
print(user_input, decoding_methods, do_sample, top_k, top_p, temperature, repetition_penalty, penalty_alpha)
prompt = f"User: {user_input}\nAssistant: "
generated_text = text_generation(f"{prompt}", min_length=50, max_length=200, num_return_sequences=1,
num_beams=num_beams, do_sample=do_sample, top_k=top_k, top_p=top_p,
temperature=temperature, repetition_penalty=repetition_penalty,
penalty_alpha=penalty_alpha)
answer = generated_text[0]["generated_text"]
answer_without_prompt = answer[len(prompt)+1:]
user_input_en = translate(user_input, "en", "id")
answer_without_prompt_en = translate(answer_without_prompt, "en", "id")
return [(f"{user_input} ", None), (answer_without_prompt, "")], \
[(f"{user_input_en} ", None), (answer_without_prompt_en, "")]
with gr.Blocks() as demo:
with gr.Row():
gr.Markdown("""## IndoChat
A Prove of Concept of a multilingual Chatbot (in this case a bilingual, English and Indonesian), fine-tuned with
multilingual instructions dataset. The base model is a GPT2-Medium (340M params) which was pretrained with 75GB
of Indonesian and English dataset, where English part is only less than 1% of the whole dataset.
""")
with gr.Row():
with gr.Column():
user_input = gr.inputs.Textbox(placeholder="",
label="Ask me something in Indonesian or English",
default="Bagaimana cara mendidik anak supaya tidak berbohong?")
decoding_methods = gr.inputs.Dropdown(["Beam Search", "Sampling", "Contrastive Search"],
default="Sampling")
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(
label="Generated Text",
combine_adjacent=True,
).style(color_map={"": "blue", "-": "green"})
generated_answer_en = gr.HighlightedText(
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_methods, num_beams, top_k, top_p, temperature,
repetition_penalty, penalty_alpha],
outputs=[generated_answer, generated_answer_en])
demo.launch(enable_queue=False) |