Johnbt2016

import pydaisi as pyd
web3 = pyd.Daisi("yogeshbansal/Web3")

#Get the latest block on Ethereum Mainnet
web3.get_latestblock().value

#Returns the current gas price in ETH
web3.get_gasPrice().value

#Returns the balance of the given account

Johnbt2016

import pydaisi as pyd
gpt_3_pdf_metadata_extraction = pyd.Daisi("laiglejm/GPT 3 PDF metadata extraction")

filename = <YOUR_PDF_FILE>  
with open(filename, 'rb') as f:     
    pdfbytes = f.read()
    
gpt_3_pdf_metadata_extraction.get_metadata(pdfbytes, openai_api_key, nb_chars=2000, max_response_tokens=500).value

Johnbt2016

{"title": "Exploring the origin of thick disks using the NewHorizon and Galactica simulations", 
"authors": [
{"author_name": "Minjung J. Park", "author_company": "Yonsei University"}, 
{"author_name": "Sukyoung K. Yi", "author_company": "Yonsei University"}, 
{"author_name": "Sebastien Peirani", "author_company": "Observatoire de la Cote d'Azur"}, 
{"author_name": "Christophe Pichon", "author_company": "Institut d'Astrophysique de Paris"}, 
{"author_name": "Yohan Dubois", "author_company": "Sorbonne Universite"}, 
{"author_name": "Hoseung Choi", "author_company": "Yonsei University"}, 
{"author_name": "Julien Devriendt", "author_company": "University of Oxford"}, 
{"author_name": "Sugata Kaviraj", "author_company": "University of Hertfordshire"}, 

Johnbt2016

{"name": "Developing a robust conceptual model",  
"technical_field": "Geothermal Exploration",  
"20_words_description": "A robust conceptual model is necessary to assess both resource risks and assist the well targeting process.",  
"benefit": "Reduced exploration risk and cost",  
"risk_if_not_applied": "Increased exploration risk and cost"}  

{"name": "Using different strategies for different stages of the project",  
"technical_field": "Geothermal Exploration",  
"20_words_description": "The variation of drilling objectives in each stage of the project (exploration, appraisal, development) requires different strategies in order to minimize the associated risk and project cost.",  
"benefit": "Reduced exploration risk and cost",  

Johnbt2016

import pydaisi as pyd 
drought_monitoring = pyd.Daisi("laiglejm/Drought Monitoring")

drought_monitoring.inference(sen2_img).value

Johnbt2016

import pydaisi as pyd

my_daisi = pyd.Daisi("username/daisiname")
my_daisi.compute(args).value #remote execution of the "compute()" function

Johnbt2016

import boto3
from botocore import UNSIGNED
from botocore.client import Config
import tempfile

s3 = boto3.client('s3', region_name='eu-central-1', config=Config(signature_version=UNSIGNED))

def get_from_lat_long(lat=0,n='N',lon=0, e='E', resolution='90'):
    
    lat_str = str(int(lat))

Johnbt2016

# Import pydaisi
import pydaisi as pyd

# Instantiate a Daisi object
edge_image_computation = pyd.Daisi("laiglejm/Edge Image computation")

# Call the endpoint of the compute_deriv() function
# and return the result immediately with the .value attribute
edge_image_computation.compute_deriv(image=None).value

Johnbt2016

import streamlit as st
from PIL import Image, ImageOps

def st_ui():
    '''
    Function running the Streamlit UI.
    Doesn't return anything.
    '''
    
    st.set_page_config(layout = "wide")

Johnbt2016

import numpy as np
from scipy import signal, misc
from copy import deepcopy

def compute_deriv(image = None):
    '''
    Compute an edge image (the second derivative of a smoothed spline)

    Arguments:
    - image (2D Numpy array) : grayscale image