erichare

    st.title("NOAA Weather API Interface")
    st.write("This Daisi, powered by the `noaa-sdk` Python package, allows for an interaction with the NOAA API for pulling weather forecast data.")
    
    with st.sidebar:
        cc = st.text_input("Country Code", value="US")
        pc = st.text_input("Postal Code", value="77001")

        wx_keys, wx_dat = available_wx_data(cc, pc)

        vars = st.multiselect("Weather Metrics", options=wx_keys, default=["temperature", "dewpoint"])

erichare

def available_wx_data(country_code, postal_code):
    res = n.get_observations(postal_code, country_code)

    return sorted(list(list(res)[0].keys())), res

def forecast(country_code, postal_code, vars, dat=None):
    if not dat:
        dat = n.get_forecasts(postal_code, country_code, type='forecastGridData')

    return {k:v for k, v in dat.items() if k in vars}

erichare

import pydaisi as pyd

twitter_sentiment = pyd.Daisi("erichare/Twitter Sentiment")
google_spreadsheets = pyd.Daisi("erichare/Google Spreadsheets")

result = twitter_sentiment.get_twitter_sentiment("Pizza", count=10, tweets=None)
with open("service-account-file.json", "r") as my_f:
    print(google_spreadsheets.store_in_gs(result, "[email protected]", my_f.read(), title="Pizza Dat", append = True).value)

erichare

import pydaisi as pyd
import pandas as pd

google_spreadsheets = pyd.Daisi("erichare/Google Spreadsheets")

df = pd.read_csv("https://gist.githubusercontent.com/netj/8836201/raw/6f9306ad21398ea43cba4f7d537619d0e07d5ae3/iris.csv")
email = "<your_email_here>"

with open("service_account.json") as my_f:
    service_account = my_f.read()

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import pydaisi as pyd

twitter_sentiment = pyd.Daisi("erichare/Twitter Sentiment")
result = twitter_sentiment.get_twitter_sentiment("Python", count=50).value

result

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import tweepy
import pandas as pd
 
# Add Twitter API key and secret
consumer_key = "<redacted>"
consumer_secret = "<redacted>"
 
# Handling authentication with Twitter
auth = tweepy.AppAuthHandler(consumer_key, consumer_secret)
 

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import tensorflow as tf
from transformers import pipeline

sentiment_task = pipeline("sentiment-analysis", model="sentiment-roberta-large-english", tokenizer="sentiment-roberta-large-english")

def get_sentiment(text):
    '''
    Use a Sentiment Detection model on the given text
    ...
    

erichare

    import pydaisi as pyd
  
    tw_daisi = pyd.Daisi("erichare/Twitter Search")
    sa_daisi = pyd.Daisi("erichare/Sentiment Analysis")
  
    result = tw_daisi.fetch_tweets("Pepperoni Pizza", count=10).value
    sentiment = sa_daisi.get_sentiment([x for x in result["text"].tolist()]).value

    result["label"] = [x["label"] for x in sentiment]
    result["score"] = [x["score"] for x in sentiment]

erichare

import pydaisi as pyd

sentiment_analysis = pyd.Daisi("erichare/Sentiment Analysis")

sentiment_analysis.get_sentiment("I am so happy").value
sentiment_analysis.get_sentiment(["I am so happy", "Actually, I'm not sure", "I'm pretty upset"]).value

erichare

import pydaisi as pyd

twitter_search = pyd.Daisi("erichare/Twitter Search")
tweets = twitter_search.fetch_tweets(query="Pepperoni Pizza", count=10).value

tweets