amn41

function gpt() {
  local url="https://api.openai.com/v1/chat/completions"
  local model="gpt-3.5-turbo"
  local body="{\"model\":\"$model\", \"messages\":[{\"role\": \"user\", \"content\": \"$1\"}]}"
  local headers="Content-Type: application/json"
  local auth="Authorization: Bearer ${OPENAI_API_KEY}"

  curl -s -H "$headers" -H "$auth" -d "$body" "$url" \
    | jq -r '.choices[0].message.content'
}

amn41

import streamlit as st
import numpy as np
import pandas as pd
import json
import requests

@st.cache
def get_auth_token(host, user, pw):
    st.write("cache miss token!")
    url = f"{host}/api/auth"

amn41

import json
import requests

def get_auth_token(host, user, pw):
    url = f"{host}/api/auth"
    payload = {"username": user, "password": pw}
    response = requests.post(url, json=payload)
    try:
        token = response.json()["access_token"]
        return token

amn41

Comment on a GitHub PR with Rasa NLU cross-validation results

Instructions

Save the yaml file at .github/workflows/comment_crossval_results.yml

Add the format_results.py script at the root of your repo.

amn41

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import logging

from flask import Blueprint, request, jsonify
import requests
from rasa_dm.channels.channel import UserMessage, OutputChannel

amn41

from rasa_dm.actions import Action
import requests

class ActionHTTPRequest(Action):
    def name(self):
        return "make_request"

    def run(self, dispatcher, tracker, domain):
        url = 'https://query.yahooapis.com/v1/public/yql?q=select%20*%20from%20weather.forecast%20where%20woeid%20in%20(select%20woeid%20from%20geo.places(1)%20where%20text%3D%22nome%2C%20ak%22)&format=json'
        result = requests.get(url).json()

amn41

from __future__ import unicode_literals
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
from builtins import str as text

import argparse
import io
import json

amn41

"""Implements the long-short term memory character model.
This version vectorizes over multiple examples, but each string
has a fixed length."""

from __future__ import absolute_import
from __future__ import print_function
from builtins import range
from os.path import dirname, join
import numpy as np
import numpy.random as npr

amn41

import rinocloud as rino
import shutil, os
import subprocess
import hashlib

"""
persist = Persistor(config.rino_token,config.rino_dir)

def save_model_new(persist,model_file,score):
    temp_file="tmp_{0:06d}.txt".format(random.choice(range(10000)))                                                                                                                                                                                  

amn41