iamaziz

import os
import json
import datetime

import streamlit as st
from llama_index.llms import Ollama
from llama_index.llms import ChatMessage
# https://docs.llamaindex.ai/en/stable/examples/llm/ollama.html

iamaziz

iamaziz

iamaziz

import sys
from io import StringIO

import streamlit as st              # pip install streamlit
from code_editor import code_editor # pip install streamlit_code_editor
import ollama as ol                 # pip install ollama

st.set_page_config(layout='wide')
st.title('`Offline code completion`')

iamaziz

# -*- coding: utf-8 -*-

# calculating the Entropy and Information Gain for: Learning with Trees
# by: Aziz Alto

# see Information Gain:
    # http://www.autonlab.org/tutorials/infogain.html


from __future__ import division

iamaziz

>>> # Multi-layer perceptron in Theano see: http://ir.hit.edu.cn/~jguo/docs/notes/a_simple_tutorial_on_theano.pdf
>>> import numpy
>>> import theano
>>> import theano.tensor as T
>>> rng = numpy.random
>>> N = 400
>>> feats = 784
>>> D = (rng.randn(N, feats), rng.randint(size=N, low=0,high=2) )
>>> training_steps = 10000
>>> x = T.matrix('x')

iamaziz

from fasthtml import common as fh

def df2fhtml(df: pd.DataFrame, with_headers: bool=True, **kwargs):
    cols = df.columns
    header = fh.Tr(*[fh.Th(fh.Label(col)) for col in cols])
    rows = [fh.Tr(*[fh.Td(df[col][i]) for col in cols]) for i in range(len(df))]
    return fh.Table(header if with_headers else '', *rows, **kwargs)

iamaziz

iamaziz

""" To use: install Ollama (or LLM studio), clone OpenVoice, run this script in the OpenVoice directory
    git clone https://github.com/myshell-ai/OpenVoice
    cd OpenVoice
    git clone https://huggingface.co/myshell-ai/OpenVoice
    cp -r OpenVoice/* .
    pip install whisper pynput pyaudio streamlit ollama

script source: https://x.com/Thom_Wolf/status/1758140066285658351?s=20
"""

iamaziz