text: # 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
  
  # Utils
  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":"😠","disgust":"🤮", "fear":"😨😱", "happy":"🤗", "joy":"😂", "neutral":"😐", "sad":"😔", "sadness":"😔", "shame":"😳", "surprise":"😮"}
  
  
  # Main Application
  def main():
  	st.title("Emotion Classifier App")
  	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()