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import streamlit as st
from src.configs import Languages
def write(*args):
# ==== HPW IT WORKS ==== #
with st.beta_container():
st.markdown("")
st.markdown("")
st.header("How it works")
st.subheader("Step 1 - Prepare your data")
st.markdown(
"""
Create an Excel or CSV file with two columns for each row:
- a column with the name or the label identifying a specific object or class (e.g., in our
wine example above it would be the type of wine or the name of a specific brand). It is
common practice naming this column `label`
- a column with the text describing that specific object or class (e.g., in the wine example
above it could be the description that you find on the rear of the bottle label). It is
common practice naming this column `text`
To have reliable results, we suggest providing at least 2000 labelled texts. If you provide
less we will still wordify your file, but the results should then be taken with a grain of
salt.
Consider that we also support multi-language texts, therefore you'll be able to
automatically discriminate between international wines, even if your preferred Italian
producer does not provide you with a description written in English!
"""
)
st.subheader("Step 2 - Upload your file and Wordify!")
st.markdown(
"""
Once you have prepared your Excel or CSV file, click the "Browse File" button.
Browse for your file.
Choose the language of your texts (select multi-language if your file contains text in
different languages).
Push the "Wordify|" button, set back, and wait for wordify to do its tricks.
Depending on the size of your data, the process can take from 1 minute to 5 minutes
"""
)
# ==== FAQ ==== #
with st.beta_container():
st.markdown("")
st.markdown("")
st.header(":question:Frequently Asked Questions")
with st.beta_expander("What is Wordify?"):
st.markdown(
"""
Wordify is a way to find out which terms are most indicative for each of your dependent
variable values.
"""
)
with st.beta_expander("What happens to my data?"):
st.markdown(
"""
Nothing. We never store the data you upload on disk: it is only kept in memory for the
duration of the modeling, and then deleted. We do not retain any copies or traces of
your data.
"""
)
with st.beta_expander("What input formats do you support?"):
st.markdown(
"""
The file you upload should be .xlsx, with two columns: the first should be labeled
'text' and contain all your documents (e.g., tweets, reviews, patents, etc.), one per
line. The second column should be labeled 'label', and contain the dependent variable
label associated with each text (e.g., rating, author gender, company, etc.).
"""
)
with st.beta_expander("How does it work?"):
st.markdown(
"""
It uses a variant of the Stability Selection algorithm
[(Meinshausen and Bühlmann, 2010)](https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1467-9868.2010.00740.x)
to fit hundreds of logistic regression models on random subsets of the data, using
different L1 penalties to drive as many of the term coefficients to 0. Any terms that
receive a non-zero coefficient in at least 30% of all model runs can be seen as stable
indicators.
"""
)
with st.beta_expander("How much data do I need?"):
st.markdown(
"""
We recommend at least 2000 instances, the more, the better. With fewer instances, the
results are less replicable and reliable.
"""
)
with st.beta_expander("Is there a paper I can cite?"):
st.markdown(
"""
Yes please! Reference coming soon...
"""
)
with st.beta_expander("What languages are supported?"):
st.markdown(
f"""
Currently we support: {", ".join([i.name for i in Languages])}.
"""
)
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