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import gradio as gr
from gpt4all import GPT4All
from urllib.request import urlopen
import json
import re
# populate all models available from GPT4All
url = "https://raw.githubusercontent.com/nomic-ai/gpt4all/main/gpt4all-chat/metadata/models3.json"
response = urlopen(url)
data_json = json.loads(response.read())
def model_choices():
model_list = [data_json[i]['filename'] for i in range(len(data_json))]
return model_list
# get each models' description
model_description = {model['filename']: model['description'] for model in data_json}
def remove_endtags(html_string, tags):
"""Remove rear HTML tags from the input string."""
for tag in tags:
html_string = re.sub(fr"</{tag}>", "", html_string)
return html_string
def replace_starttags(html_string, replacements):
"""Replace starting HTML tags with the corresponding values."""
for tag, replacement in replacements.items():
html_string = html_string.replace(tag, replacement)
return html_string
def format_html_string(html_string):
"""Format the HTML string to a readable text format."""
endtags_to_remove = ["ul", "li", "br", "strong", "a"]
html_string = remove_endtags(html_string, endtags_to_remove)
starttag_replacements = {
"<ul>": "",
"<li>": "\n➤ ",
"<br>": "\n",
"<strong>": "",
'<a href="https://opensource.org/license/mit">': "",
'<a href="https://llama.meta.com/llama3/license/">': "",
'<a href="https://atlas.nomic.ai/">': "",
}
formatted_string = replace_starttags(html_string, starttag_replacements)
return formatted_string
def llm_intro(selected_model):
html_string = model_description.get(selected_model, "No description available for this model selection.")
formatted_description = format_html_string(html_string)
return formatted_description
# cache models for faster reloads
model_cache = {}
def load_model(model_name):
"""
This function checks the cache before loading a model.
If the model is cached, it returns the cached version.
Otherwise, it loads the model, caches it, and then returns it.
"""
if model_name not in model_cache:
model = GPT4All(model_name)
model_cache[model_name] = model
return model_cache[model_name] |