File size: 1,763 Bytes
769af1a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
"""Summary tab rendering functionality"""
from config import app_config
import plot
import streamlit as st
import data
import utils
from config import app_config
###
### INTERNAL FUNCTIONS
###
def __section(header, df):
"""Build page UI elements"""
st.header(header)
### accept text input, make prediction and show results
st.write(
"`Enter the text` to be classified in the text area and then click `Detect`"
)
text = st.text_area("Enter Text:", height=200)
if st.button("Predict"):
model = data.load_model(app_config.model_file)
pred, pred_proba = utils.make_prediction(model, text, proba=True)
pred_col, conf_col = st.columns(2)
with pred_col:
emotion = pred[0]
st.success(
f"Detected Emotion: {emotion.upper()} {app_config.emoji_map[emotion]}"
)
with conf_col:
st.success(f"Confidence: {pred_proba.max():.2f}%")
fig = plot.plot_proba(model.classes_, pred_proba)
st.plotly_chart(fig, use_container_width=True)
### Supplementary details about the model used
st.divider()
with st.expander("Supplementary under-the-hood details:"):
st.info(
body="""
A trained LogisticRegression model is used here for emotion detection. The model
has been trained on a labeled data of 34,000 samples. Sample data and class
distribution is shown below.
""",
icon=app_config.icon_info,
)
st.dataframe(df.loc[:15, ["Clean_Text", "Emotion"]])
fig = plot.plot_class_dist(df)
st.plotly_chart(fig, use_container_width=True)
###
### MAIN FLOW, entry point
###
def render(df):
__section("Emotions Detection", df)
|