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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) |