app.py

import os
import logging

from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr

log_level = os.environ.get("LOG_LEVEL", "WARNING")
logging.basicConfig(encoding='utf-8', level=log_level)

logging.info("Loading Model")
tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", trust_remote_code=True)

def format_prompt(message, history):
    """Formats the prompt for the AI"""
    logging.info("Formatting Prompt")
    logging.debug("Input Message: %s", message)
    logging.debug("Input History: %s", history)

    prompt = f"Instruct: {message}\n"
    prompt += "Output: "
    return prompt


def generate(
    prompt, history, system_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
):
    logging.info("Generating Response")
    logging.debug("Input Prompt: %s", prompt)
    logging.debug("Input History: %s", history)
    logging.debug("Input System Prompt: %s", system_prompt)
    logging.debug("Input Temperature: %s", temperature)
    logging.debug("Input Max New Tokens: %s", max_new_tokens)
    logging.debug("Input Top P: %s", top_p)
    logging.debug("Input Repetition Penalty: %s", repetition_penalty)

    logging.info("Converting Parameters to Correct Type")
    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)
    logging.debug("Temperature: %s", temperature)
    logging.debug("Top P: %s", top_p)

    logging.info("Creating Generate kwargs")
    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True
    )
    logging.debug("Generate Args: %s", generate_kwargs)

    formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
    logging.debug("Prompt: %s", formatted_prompt)

    logging.info("Generating Text")
    stream = model.generate(tokenizer(prompt, return_tensors="pt").input_ids, **generate_kwargs)

    logging.info("Creating Output")
    output = ""
    for response in stream:
        output += response.token.text
        yield output

    logging.debug("Output: %s", output)
    return output


additional_inputs = [
    gr.Textbox(
        label="System Prompt",
        max_lines=1,
        interactive=True,
    ),
    gr.Slider(
        label="Temperature",
        value=0.9,
        minimum=0.0,
        maximum=1.0,
        step=0.05,
        interactive=True,
        info="Higher values produce more diverse outputs",
    ),
    gr.Slider(
        label="Max new tokens",
        value=256,
        minimum=0,
        maximum=1048,
        step=64,
        interactive=True,
        info="The maximum numbers of new tokens",
    ),
    gr.Slider(
        label="Top-p (nucleus sampling)",
        value=0.90,
        minimum=0.0,
        maximum=1,
        step=0.05,
        interactive=True,
        info="Higher values sample more low-probability tokens",
    ),
    gr.Slider(
        label="Repetition penalty",
        value=1.2,
        minimum=1.0,
        maximum=2.0,
        step=0.05,
        interactive=True,
        info="Penalize repeated tokens",
    )
]

examples = []

logging.info("Creating Chat Interface")
gr.ChatInterface(
    fn=generate,
    chatbot=gr.Chatbot(show_label=False, show_share_button=False,
                       show_copy_button=True, likeable=True, layout="panel"),
    additional_inputs=additional_inputs,
    title="Mixtral Instruct",
    examples=examples,
    concurrency_limit=20,
).launch(show_api=False)