kristjan-eljand

import plotly
import plotly.graph_objects as go

x = pd.Series({0:2020, 1:2021, 2:2022})
y = pd.Series({0: 10, 1:20, 2:30})
fig = go.Figure()
fig.add_trace(go.Bar(x = x, y = y))
fig.update_traces(
    marker_color='rgb(68, 84, 106)', 
    marker_line_width=0, 

kristjan-eljand

<!-- 
============================================================
Plotly.js barchart 
============================================================
-->
<div class="row">
<div class="col-lg-4" style="outline:1px solid black;">
    <strong>Day-ahead prices</strong>
    <div id="chart" class="container col-12"></div>
</div>

kristjan-eljand

# Profiler
# cumulative time -- time in function + calls to other functions
# total time -- time only in function without calls to other funs
import cProfile
import pstats
import io
def print_c_profiler(pr, lines_to_print=25):
    """ 
    Create the speed profile of the arbitrary code.
    Example usage:

kristjan-eljand

# 1. Initiate pipeline for Text summarization
summarizer = pipeline("summarization", model="t5-base")

# 2. Input sentence in Estonian
sentence_est = r"""
E-Lab on Eesti Energia IT osakonda kuuluv uurimis- ja arendusüksus.
Üksuse eesmärk on kiirendada innovatsiooni ja aidata kaasa uute ideede 
esimeste arendusetappide (kontseptsiooni tõestus ja prototüüpimine) läbimisele. 
Tiimis on täna 12 liiget, kelle seas seitse tarkvarainseneri, kaks andmeteadurit, 
tarkvaraarhitekt, tooteomanik ning tehnoloogiaskaut. 

kristjan-eljand

# 1. Initiate pipeline for Named Entity Recognition (ner)
ner = pipeline("ner")

# The output included encoded classes
# Here I give reasonable Estonian names to these classes
classes_est = {
    "O": "Ei ole nimi",
    "B-MIS": "Nime algus kohe pärast teist nimeüksust",
    "I-MIS": "Muu üksus",
    "B-PER": "Inimese nime algus kohe pärast teise inimese nime",

kristjan-eljand

from pprint import pprint #for user-friendly output printing
# 1. Create a pipeline for text-generation task
generator = pipeline('text-generation', model='distilgpt2')

# 2. Translate the sentence beginnings from Est to Eng
beginnings_origin = [
                     "Eesti toodab elektrienergiat peamiselt",
                     "Taastuvenergia on oluline, sest"
                     ]
translated_beginnings = [translate(b, EST_TO_ENG)[0]['translation_text'] for b in beginnings_origin]

kristjan-eljand

# 1. create a pipeline for question answering task
respondent = pipeline("question-answering")

# 2. Translate the text from Estonian to English
context_to_translate = r"""
E-Lab on Eesti Energia IT osakonda kuuluv uurimis- ja arendusüksus. 
Üksuse eesmärk on kiirendada innovatsiooni ja aidata kaasa uute ideede
esimeste arendusetappide läbimisele. Kui mõnel äriüksusel on soov innovaatilise
IT-lahenduse loomiseks, aitame teostada kontseptsiooni tõestuse ja arendada
välja prototüübi. Lisaks testib ja demonstreerib E-Lab ka uusi digitehnoloogiaid,

kristjan-eljand

# Translate the text from input language to english 
input_to_translate = "Parim argument demokraatia vastu on viieminutiline vestlus keskmise valijaga"
translated_input = translate(input_to_translate)[0]['translation_text']

# Using the sentiment classifier is oneliner
result = classifier(translated_input)[0]
print(translated_input)
print(f"label: {result['label']}, with score: {round(result['score'], 4)}")
      
# Output:

kristjan-eljand

# 1. Create sentiment classifier with pipeline function
# and the name of the task
classifier = pipeline('sentiment-analysis')

# 2. Translate the text from input language to English
input_to_translate = "Tahtsime parimat, aga välja kukkus nagu alati"
translated_input = translate(input_to_translate)[0]['translation_text']

# 3. Using the sentiment classifier is oneliner
result = classifier(translated_input)[0]

kristjan-eljand

import json
import requests

# We use "Helsinki-NLP/opus-mt-et-en" model for translation
API_URL = "https://api-inference.huggingface.co/models/Helsinki-NLP/opus-mt-et-en"
# Register an account in Hugging Face to get your API_TOKEN 
# you'll find it under settings
headers = {"Authorization": f"Bearer {API_TOKEN}"}

# Function to run the post request to the API