text-generation-webui/extensions/example/script.py

"""
An example of extension. It does nothing, but you can add transformations
before the return statements to customize the webui behavior.

Starting from history_modifier and ending in output_modifier, the
functions are declared in the same order that they are called at
generation time.
"""

import torch
from modules import chat
from modules.text_generation import (
    decode,
    encode,
    generate_reply,
)
from transformers import LogitsProcessor

params = {
    "display_name": "Example Extension",
    "is_tab": False,
}

class MyLogits(LogitsProcessor):
    """
    Manipulates the probabilities for the next token before it gets sampled.
    It gets used in the custom_logits_processor function below.
    """
    def __init__(self):
        pass

    def __call__(self, input_ids, scores):
        # probs = torch.softmax(scores, dim=-1, dtype=torch.float)
        # probs[0] /= probs[0].sum()
        # scores = torch.log(probs / (1 - probs))
        return scores

def history_modifier(history):
    """
    Modifies the chat history.
    Only used in chat mode.
    """
    return history

def state_modifier(state):
    """
    Modifies the state variable, which is a dictionary containing the input
    values in the UI like sliders and checkboxes.
    """
    return state

def chat_input_modifier(text, visible_text, state):
    """
    Modifies the internal and visible input strings in chat mode.
    """
    return text, visible_text

def input_modifier(string, state):
    """
    In chat mode, modifies the user input. The modified version goes into
    history['internal'], and the original version goes into history['visible'].

    In default/notebook modes, modifies the whole prompt.
    """
    return string

def bot_prefix_modifier(string, state):
    """
    Modifies the prefix for the next bot reply in chat mode.
    By default, the prefix will be something like "Bot Name:".
    """
    return string

def tokenizer_modifier(state, prompt, input_ids, input_embeds):
    """
    Modifies the input ids and embeds.
    Used by the multimodal extension to put image embeddings in the prompt.
    Only used by loaders that use the transformers library for sampling.
    """
    return prompt, input_ids, input_embeds

def logits_processor_modifier(processor_list, input_ids):
    """
    Adds logits processors to the list.
    Only used by loaders that use the transformers library for sampling.
    """
    processor_list.append(MyLogits())
    return processor_list

def output_modifier(string, state):
    """
    Modifies the LLM output before it gets presented.

    In chat mode, the modified version goes into history['internal'], and the original version goes into history['visible'].
    """
    return string

def custom_generate_chat_prompt(user_input, state, **kwargs):
    """
    Replaces the function that generates the prompt from the chat history.
    Only used in chat mode.
    """
    result = chat.generate_chat_prompt(user_input, state, **kwargs)
    return result

def custom_css():
    """
    Returns a CSS string that gets appended to the CSS for the webui.
    """
    return ''

def custom_js():
    """
    Returns a javascript string that gets appended to the javascript for the webui.
    """
    return ''

def setup():
    """
    Gets executed only once, when the extension is imported.
    """
    pass

def ui():
    """
    Gets executed when the UI is drawn. Custom gradio elements and their corresponding
    event handlers should be defined here.
    """
    pass