Spaces:
Runtime error
Runtime error
File size: 3,546 Bytes
3539b08 |
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 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 |
# 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() |