As of this day, this is probably the only and fastest way of installing it.
Based from the GDAL and PROJ build requirements, here is the full list of required packages to install:
Kate Belisle akatie
akatie
/ debias.json
Created
November 19, 2025 11:34
— forked from twinforces/debias.json
grok debias promp
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| { | |
| "version": "2.4", | |
| "word_count_original": 2487, | |
| "word_count_trimmed": 1752, | |
| "safety_addendum": "Framework as Neutral Enhancer: This process augments AI guidelines, deferring to factual accuracy, no-mislead, and honesty. Mandates steelmanned distributions (≥2 primaries/side), five-valued classifications (IT/LF maps for user judgment), tool-verified chains. Reinforces safety: Grounded evidence, 'human decides' closes, no AI fiat. Optional lens—flag 'apply debias'; default neutrality. Prioritizes Truth/Love over Fear, empowering users without bias amplification.", | |
| "incentives_ranking": { | |
| "attention_drivers": { | |
| "1": "Violence - Grabs attention with drama.", | |
| "2": "Sex - Draws curiosity, effective in ads.", | |
| "3": "Fear - Creates urgency, focuses viewers.", |
akatie
/ inexact-search-aho-corasick.ipynb
Created
July 30, 2025 19:59
— forked from sujitpal/inexact-search-aho-corasick.ipynb
Code demonstrating building and querying an Aho-Corasick FSM for inexact search
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akatie
/ 07b-viterbi-gist.py
Created
July 30, 2025 19:58
— forked from sujitpal/07b-viterbi-gist.py
Entity Disambiguation for entities identified for a sentence by SciSpacy + UMLS integration using Viterbi's algorithm
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| import argparse | |
| import itertools | |
| import numpy as np | |
| import operator | |
| import os | |
| import pickle | |
| import spacy | |
| import scispacy | |
| import time |
akatie
/ 01-understanding_ppi.ipynb
Created
July 30, 2025 19:58
— forked from sujitpal/01-understanding_ppi.ipynb
Understanding Prediction Powered Inference (PPI) used in ARES
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akatie
/ 13-bayesian_nn.ipynb
Created
July 30, 2025 19:57
— forked from sujitpal/13-bayesian_nn.ipynb
Bayesian Network example (unrolled) from NYU Pytorch DL course -- adapted from atcold/pytorch-Deep-Learning
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| { | |
| "links": [{ | |
| "source": "COACHINGCLIENTBEDESC", | |
| "target": "0", | |
| "value": 1.0 | |
| }, { | |
| "source": "DRA", | |
| "target": "MKTSRR", | |
| "value": 3.0 | |
| }, { |
akatie
/ streamlit_prodigy.py
Created
March 11, 2021 12:30
— forked from ines/streamlit_prodigy.py
Streamlit + Prodigy
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| """ | |
| Example of a Streamlit app for an interactive Prodigy dataset viewer that also lets you | |
| run simple training experiments for NER and text classification. | |
| Requires the Prodigy annotation tool to be installed: https://prodi.gy | |
| See here for details on Streamlit: https://streamlit.io. | |
| """ | |
| import streamlit as st | |
| from prodigy.components.db import connect | |
| from prodigy.models.ner import EntityRecognizer, merge_spans, guess_batch_size |
akatie
/ jsonp-in-flask.py
Created
February 25, 2021 14:39
— forked from farazdagi/jsonp-in-flask.py
JSONP in Flask
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| import json | |
| from functools import wraps | |
| from flask import redirect, request, current_app | |
| def support_jsonp(f): | |
| """Wraps JSONified output for JSONP""" | |
| @wraps(f) | |
| def decorated_function(*args, **kwargs): | |
| callback = request.args.get('callback', False) | |
| if callback: |
akatie
/ flightradar.html
Created
February 5, 2021 17:58
— forked from tkardi/flightradar.html
Code companion to 'Building a flightradar in Leaflet (pt II)'
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| <!DOCTYPE html> | |
| <html> | |
| <!-- The following HTML page together with the accompanying | |
| javascript is published under the Unlicense. | |
| SPDX-License-Identifier: Unlicense | |
| --> | |
| <head> | |
| <meta charset="utf-8"> | |
| <title>Air traffic map</title> |
akatie
/ README.md
Created
December 3, 2020 22:12
— forked from abelcallejo/README.md
Installing GDAL 3.2.0 on Amazon Linux 2
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