| | import gradio as gr |
| | import os |
| | from mtranslate import translate |
| | import requests |
| |
|
| | HF_AUTH_TOKEN = os.environ.get("HF_AUTH_TOKEN") |
| | indochat_api = 'https://cahya-indonesian-whisperer.hf.space/api/text-generator/v1' |
| | indochat_api_auth_token = os.getenv("INDOCHAT_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 ' + indochat_api_auth_token} |
| | data = { |
| | "model_name": "indochat-tiny", |
| | "text": user_input, |
| | "min_length": len(user_input) + 20, |
| | "max_length": 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(indochat_api, headers=headers, data=data) |
| | if r.status_code == 200: |
| | result = r.json() |
| | answer = result["generated_text"] |
| | user_input_en = translate(user_input, "en", "id") |
| | answer_en = translate(answer, "en", "id") |
| | 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("""## 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_method = gr.inputs.Dropdown(["Beam Search", "Sampling", "Contrastive Search"], |
| | 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.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) |
