app.py

import gradio as gr
import spaces
from huggingface_hub import InferenceClient
from transformers import AutoModelForCausalLM, AutoTokenizer

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
model = AutoModelForCausalLM.from_pretrained("BatsResearch/bonito-v1")
tokenizer = AutoTokenizer.from_pretrained("BatsResearch/bonito-v1")
model.to("cuda")

@spaces.GPU
def respond(
    message,
    task_type,
    max_tokens,
    temperature,
    top_p,
):
    task_type = task_type.lower()
    input_text = "<|tasktype|>\n" + task_type.strip()
    input_text += "\n<|context|>\n" + message.strip() + "\n<|task|>\n"

    input_ids = tokenizer.encode(input_text, return_tensors="pt").to("cuda")
    output = model.generate(
        input_ids,
        max_new_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        do_sample=True,
    )
    pred_start = int(input_ids.shape[-1])

    response = tokenizer.decode(output[0][pred_start:], skip_special_tokens=True)

    # check if <|pipe|> is in the response
    if "<|pipe|>" in response:
        pair = response.split("<|pipe|>")
        instruction = pair[0].strip().replace("{{context}}", message)
        response = pair[1].strip()
    else:
        # fallback
        instruction = pair[0].strip().replace("{{context}}", message)
        response = "Unable to generate response. Please regenerate."

    return instruction, response

task_types = [
    "extractive question answering",
    "multiple-choice question answering",
    "question generation",
    "question answering without choices",
    "yes-no question answering",
    "coreference resolution",
    "paraphrase generation",
    "paraphrase identification",
    "sentence completion",
    "sentiment",
    "summarization",
    "text generation",
    "topic classification",
    "word sense disambiguation",
    "textual entailment",
    "natural language inference",
]
# capitalize for better readability
task_types = [task_type.capitalize() for task_type in task_types]

demo = gr.Interface(
    fn=respond,
    inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Dropdown(task_types, label="Task type"),
    ],
    outputs=[gr.Textbox(label="Input"), gr.Textbox(label="Output")],
    additional_inputs=[
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
    title="Zephyr Chatbot",
    description="A chatbot that uses the Hugging Face Zephyr model.",
)


if __name__ == "__main__":
    demo.launch()