ChuanhuChatGPT / modules /overwrites.py
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from __future__ import annotations
import logging
from llama_index import Prompt
from typing import List, Tuple
import mdtex2html
from modules.presets import *
from modules.llama_func import *
def compact_text_chunks(self, prompt: Prompt, text_chunks: List[str]) -> List[str]:
logging.debug("Compacting text chunks...πŸš€πŸš€πŸš€")
combined_str = [c.strip() for c in text_chunks if c.strip()]
combined_str = [f"[{index+1}] {c}" for index, c in enumerate(combined_str)]
combined_str = "\n\n".join(combined_str)
# resplit based on self.max_chunk_overlap
text_splitter = self.get_text_splitter_given_prompt(prompt, 1, padding=1)
return text_splitter.split_text(combined_str)
def postprocess(
self, y: List[Tuple[str | None, str | None]]
) -> List[Tuple[str | None, str | None]]:
"""
Parameters:
y: List of tuples representing the message and response pairs. Each message and response should be a string, which may be in Markdown format.
Returns:
List of tuples representing the message and response. Each message and response will be a string of HTML.
"""
if y is None or y == []:
return []
user, bot = y[-1]
if not detect_converted_mark(user):
user = convert_asis(user)
if not detect_converted_mark(bot):
bot = convert_mdtext(bot)
y[-1] = (user, bot)
return y
with open("./assets/custom.js", "r", encoding="utf-8") as f, open("./assets/Kelpy-Codos.js", "r", encoding="utf-8") as f2:
customJS = f.read()
kelpyCodos = f2.read()
def reload_javascript():
print("Reloading javascript...")
js = f'<script>{customJS}</script><script>{kelpyCodos}</script>'
def template_response(*args, **kwargs):
res = GradioTemplateResponseOriginal(*args, **kwargs)
res.body = res.body.replace(b'</html>', f'{js}</html>'.encode("utf8"))
res.init_headers()
return res
gr.routes.templates.TemplateResponse = template_response
GradioTemplateResponseOriginal = gr.routes.templates.TemplateResponse