dirkliebich

# We have several addresses and need a routing plan that provides the fastest path to all places 
# for a single driver.
#
# The code below should do just that but we find access errors?

#
# Loaded addresses: [{'id': '1', 'address': 'Diercksenstrasse 51'}, {'id': '2', 'address': 'Nibelungenstraße 59'}, {'id': '3', 'address': 'Tiergartenstrasse 48a'}]
# Geocoding: Diercksenstrasse 51
# Geocoding: Nibelungenstraße 59
# Geocoding: Tiergartenstrasse 48a

dirkliebich

Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.
- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.
- Reasoning Before Conclusions: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!
    - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.
    - Conclusion, classifications, or results should ALWAYS appear last.
- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.
   - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from p

dirkliebich

You are tasked with creating a name tag-like display for a given text. Your job is to evaluate the content and present key information in a visually organized manner. Follow these instructions carefully:

1. Read and analyze the following text:

{{TEXT}}

2. Extract and categorize the following information from the text: a. Sentiment: Determine if the overall sentiment is negative, positive, or neutral.

dirkliebich

from deepgram import DeepgramClient, PrerecordedOptions, AnalyzeOptions

DEEPGRAM_API_KEY = "c54089a7b3d72c96a9f880d9e3a00aaf26322021"

TEXT =  {
    "buffer": "Enter your text here."
}

def dg_ttt():

dirkliebich

dirkliebich

import streamlit as st

data = "video.mp4"

st.write("Click to go to that moment in video.")

time_stamps = [
    (st.button("Part A", "a"), 1),  # bool, moment (seconds)
    (st.button("Part B", "b"), 20),
    (st.button("Part C", "c"), 40),

dirkliebich

import os
import requests

def download_models():
    # Create MODELS folder if it doesn't exist
    if not os.path.exists('../com.docker.devenvironments.code/MODELS'):
        os.makedirs('../com.docker.devenvironments.code/MODELS')
    
    # Dictionary of URLs and corresponding filenames
    urls = {

dirkliebich

"""
Filename: /home/dirk/dev/iCUE/iCUE-SmartScribe/langchain_sum.py
Path: /home/dirk/dev/iCUE/iCUE-SmartScribe
Created Date: Friday, April 21st 2023, 2:20:37 pm
Author: Dirk Liebich

Copyright (c) 2023 iCUE Solutions GmbH
"""
import os
import glob

dirkliebich

'''
Filename: /home/dirk/dev/iCUE/iCUE-SmartScribe/langchain.py
Path: /home/dirk/dev/iCUE/iCUE-SmartScribe
Created Date: Monday, April 24th 2023, 12:37:32 pm
Author: Dirk Liebich

Copyright (c) 2023 iCUE Solutions GmbH

Example
https://github.com/hwchase17/langchain/issues/2491

dirkliebich

# FILEPATH: /tiktoken
import tiktoken

def num_tokens_from_string(string: str, encoding_name: str) -> int:
    """
    Returns the number of tokens in a text string.

    Args:
        string (str): The text string to count tokens in.
        encoding_name (str): The name of the encoding to use.