app.py

import gradio as gr
from huggingface_hub import InferenceClient
from datetime import datetime

"""
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
"""
#client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
lora_name = "robinhad/UAlpaca-1.1-Mistral-7B"

from peft import PeftModel
from transformers import LlamaTokenizer, LlamaForCausalLM, BitsAndBytesConfig
from torch import bfloat16
model_name = "mistralai/Mistral-7B-v0.1"

quant_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=bfloat16
)
tokenizer = LlamaTokenizer.from_pretrained(model_name)
model = LlamaForCausalLM.from_pretrained(
    model_name,
    quantization_config=quant_config,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, lora_name)


# will be used with normal template
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response


def ask(instruction: str, context: str = None):
    print(datetime.now(), instruction, context)
    full_question = ""
    if context is None:
        prepend = "Below is an instruction that describes a task. Write a response that appropriately completes the request."
        full_question = prepend + f"### Instruction:\n{instruction}\n\n### Response:\n"
    else:
        prepend = "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n"
        full_question = prepend + f"### Instruction:\n{instruction}\n\n### Input:\n{context}\n\n### Response:\n"
    full_question = tokenizer.encode(full_question, return_tensors="pt")
    return tokenizer.batch_decode(model.generate(full_question, max_new_tokens=300))[0].split("### Response:")[1].strip().replace("</s>", "")

"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
"""demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        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)",
        ),
    ],
)"""

model_name = "robinhad/UAlpaca-1.1-Mistral-7B"


def image_classifier(inp):
    return {"cat": 0.3, "dog": 0.7}


demo = gr.Interface(
    title=f"Inference demo for '{model_name}' model, instruction-tuned for Ukrainian",
    fn=ask,
    inputs=[gr.Textbox(label="Input"), gr.Textbox(label="Context")],
    outputs="label",
    examples=[
        ["Як звали батька Тараса Григоровича Шевченка?", None],
        ["Як можна заробити нелегально швидко гроші?", None],
        ["Яка найвища гора в Україні?", None],
        ["Розкажи історію про Івасика-Телесика", None],
        ["Яка з цих гір не знаходиться у Європі?", "Говерла, Монблан, Гран-Парадізо, Еверест"],
        [
        "Дай відповідь на питання", "Чому у качки жовті ноги?"
    ]],
)
demo.launch()


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