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# Core Pkgs | |
import streamlit as st | |
import altair as alt | |
import plotly.express as px | |
# EDA Pkgs | |
import pandas as pd | |
import numpy as np | |
from datetime import datetime | |
# Utilsลล | |
import joblib | |
pipe_lr = joblib.load(open("models/emotion_classifier_pipe_lr_03_june_2021.pkl","rb")) | |
# Track Utils | |
from track_utils import create_page_visited_table,add_page_visited_details,view_all_page_visited_details,add_prediction_details,view_all_prediction_details,create_emotionclf_table | |
# Fxn | |
def predict_emotions(docx): | |
results = pipe_lr.predict([docx]) | |
return results[0] | |
def get_prediction_proba(docx): | |
results = pipe_lr.predict_proba([docx]) | |
return results | |
emotions_emoji_dict = {"anger":"๐ ","disgust":"๐คฎ", "fear":"๐จ๐ฑ", "happy":"๐ค", "joy":"๐", "neutral":"๐", "sad":"๐", "sadness":"๐", "shame":"๐ณ", "surprise":"๐ฎ"} | |
# Main Application | |
def main(): | |
st.title("Text Emotion Detection \n Mini-Project By Sanyam, Aditya & Manas") | |
menu = ["Home","Monitor","About"] | |
choice = st.sidebar.selectbox("Menu",menu) | |
create_page_visited_table() | |
create_emotionclf_table() | |
if choice == "Home": | |
add_page_visited_details("Home",datetime.now()) | |
st.subheader("Home-Emotion In Text") | |
with st.form(key='emotion_clf_form'): | |
raw_text = st.text_area("Type Here") | |
submit_text = st.form_submit_button(label='Submit') | |
if submit_text: | |
col1,col2 = st.beta_columns(2) | |
# Apply Fxn Here | |
prediction = predict_emotions(raw_text) | |
probability = get_prediction_proba(raw_text) | |
add_prediction_details(raw_text,prediction,np.max(probability),datetime.now()) | |
with col1: | |
st.success("Original Text") | |
st.write(raw_text) | |
st.success("Prediction") | |
emoji_icon = emotions_emoji_dict[prediction] | |
st.write("{}:{}".format(prediction,emoji_icon)) | |
st.write("Confidence:{}".format(np.max(probability))) | |
with col2: | |
st.success("Prediction Probability") | |
# st.write(probability) | |
proba_df = pd.DataFrame(probability,columns=pipe_lr.classes_) | |
# st.write(proba_df.T) | |
proba_df_clean = proba_df.T.reset_index() | |
proba_df_clean.columns = ["emotions","probability"] | |
fig = alt.Chart(proba_df_clean).mark_bar().encode(x='emotions',y='probability',color='emotions') | |
st.altair_chart(fig,use_container_width=True) | |
elif choice == "Monitor": | |
add_page_visited_details("Monitor",datetime.now()) | |
st.subheader("Monitor App") | |
with st.beta_expander("Page Metrics"): | |
page_visited_details = pd.DataFrame(view_all_page_visited_details(),columns=['Pagename','Time_of_Visit']) | |
st.dataframe(page_visited_details) | |
pg_count = page_visited_details['Pagename'].value_counts().rename_axis('Pagename').reset_index(name='Counts') | |
c = alt.Chart(pg_count).mark_bar().encode(x='Pagename',y='Counts',color='Pagename') | |
st.altair_chart(c,use_container_width=True) | |
p = px.pie(pg_count,values='Counts',names='Pagename') | |
st.plotly_chart(p,use_container_width=True) | |
with st.beta_expander('Emotion Classifier Metrics'): | |
df_emotions = pd.DataFrame(view_all_prediction_details(),columns=['Rawtext','Prediction','Probability','Time_of_Visit']) | |
st.dataframe(df_emotions) | |
prediction_count = df_emotions['Prediction'].value_counts().rename_axis('Prediction').reset_index(name='Counts') | |
pc = alt.Chart(prediction_count).mark_bar().encode(x='Prediction',y='Counts',color='Prediction') | |
st.altair_chart(pc,use_container_width=True) | |
else: | |
st.subheader("About") | |
add_page_visited_details("About",datetime.now()) | |
if __name__ == '__main__': | |
main() |