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import gradio as gr |
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import os |
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from mtranslate import translate |
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import requests |
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HF_AUTH_TOKEN = os.environ.get("HF_AUTH_TOKEN") |
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indochat_api = 'https://cahya-indonesian-whisperer.hf.space/api/indochat/v1' |
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indochat_api_auth_token = os.getenv("INDOCHAT_API_AUTH_TOKEN", "") |
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def get_answer(user_input, decoding_method, num_beams, top_k, top_p, temperature, repetition_penalty, penalty_alpha): |
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print(user_input, decoding_method, top_k, top_p, temperature, repetition_penalty, penalty_alpha) |
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headers = {'Authorization': 'Bearer ' + indochat_api_auth_token} |
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data = { |
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"text": user_input, |
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"min_length": len(user_input) + 50, |
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"max_length": 300, |
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"decoding_method": decoding_method, |
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"num_beams": num_beams, |
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"top_k": top_k, |
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"top_p": top_p, |
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"temperature": temperature, |
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"seed": -1, |
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"repetition_penalty": repetition_penalty, |
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"penalty_alpha": penalty_alpha |
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} |
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r = requests.post(indochat_api, headers=headers, data=data) |
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if r.status_code == 200: |
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result = r.json() |
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answer = result["generated_text"] |
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user_input_en = translate(user_input, "en", "id") |
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answer_en = translate(answer, "en", "id") |
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return [(f"{user_input}\n", None), (answer, "")], \ |
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[(f"{user_input_en}\n", None), (answer_en, "")] |
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else: |
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return "Error: " + r.text |
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css = """ |
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#answer_id span {white-space: pre-line} |
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#answer_id span.label {display: none} |
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#answer_en span {white-space: pre-line} |
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#answer_en span.label {display: none} |
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""" |
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with gr.Blocks(css=css) as demo: |
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with gr.Row(): |
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gr.Markdown("""## IndoChat |
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A Prove of Concept of a multilingual Chatbot (in this case a bilingual, English and Indonesian), fine-tuned with |
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multilingual instructions dataset. The base model is a GPT2-Medium (340M params) which was pretrained with 75GB |
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of Indonesian and English dataset, where English part is only less than 1% of the whole dataset. |
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""") |
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with gr.Row(): |
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with gr.Column(): |
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user_input = gr.inputs.Textbox(placeholder="", |
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label="Ask me something in Indonesian or English", |
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default="Bagaimana cara mendidik anak supaya tidak berbohong?") |
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decoding_method = gr.inputs.Dropdown(["Beam Search", "Sampling", "Contrastive Search"], |
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default="Sampling", label="Decoding Method") |
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num_beams = gr.inputs.Slider(label="Number of beams for beam search", |
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default=1, minimum=1, maximum=10, step=1) |
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top_k = gr.inputs.Slider(label="Top K", |
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default=30, maximum=50, minimum=1, step=1) |
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top_p = gr.inputs.Slider(label="Top P", default=0.9, step=0.05, minimum=0.1, maximum=1.0) |
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temperature = gr.inputs.Slider(label="Temperature", default=0.5, step=0.05, minimum=0.1, maximum=1.0) |
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repetition_penalty = gr.inputs.Slider(label="Repetition Penalty", default=1.1, step=0.05, minimum=1.0, maximum=2.0) |
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penalty_alpha = gr.inputs.Slider(label="The penalty alpha for contrastive search", |
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default=0.5, step=0.05, minimum=0.05, maximum=1.0) |
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with gr.Row(): |
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button_generate_story = gr.Button("Submit") |
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with gr.Column(): |
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generated_answer = gr.HighlightedText( |
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elem_id="answer_id", |
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label="Generated Text", |
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combine_adjacent=True, |
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css="#htext span {white-space: pre-line}", |
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).style(color_map={"": "blue", "-": "green"}) |
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generated_answer_en = gr.HighlightedText( |
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elem_id="answer_en", |
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label="Translation", |
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combine_adjacent=True, |
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).style(color_map={"": "blue", "-": "green"}) |
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with gr.Row(): |
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gr.Markdown("") |
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button_generate_story.click(get_answer, |
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inputs=[user_input, decoding_method, num_beams, top_k, top_p, temperature, |
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repetition_penalty, penalty_alpha], |
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outputs=[generated_answer, generated_answer_en]) |
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demo.launch(enable_queue=False) |
