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import streamlit as st
import altair as alt
import plotly.express as px
import pandas as pd
import numpy as np
from datetime import datetime
from transformers import pipeline
# Loading pre-trained emotion classifier pipeline
emotion_classifier = pipeline("text-classification", model="j-hartmann/emotion-english-roberta-large", top_k=None)
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
def predict_emotions(docx):
results = emotion_classifier(docx)
results_sorted = sorted(results[0], key=lambda x: x['score'], reverse=True)
return results_sorted[0]['label']
def get_prediction_proba(docx):
results = emotion_classifier(docx)
return {result['label']: result['score'] for result in results[0]}
def set_bg_hack_url():
'''
A function to unpack an image from url and set as bg.
Returns
-------
The background.
'''
st.markdown(
f"""
<style>
.stApp {{
background: url("https://png.pngtree.com/background/20210709/original/pngtree-simple-technology-business-line-picture-image_938206.jpg");
background-size: cover;
}}
/* General body styling */
body {{
font-family: 'Arial', sans-serif;
}}
/* Sidebar styling */
[data-testid="stSidebar"] {{
background: linear-gradient(180deg, #52079A, #062879); /* Gradient from dark blue to orange */
color: white;
}}
[data-testid="stSidebar"] .css-1d391kg {{
color: white;
}}
/* Title and headers */
h1, h2, h3 {{
color: #FFFFFF; /* White */
}}
/* Custom button style */
.stButton button {{
background-color: #004080; /* Dark Blue */
color: white;
border-radius: 8px;
border: none;
font-size: 16px;
padding: 10px 20px;
cursor: pointer;
}}
.stButton button:hover {{
background-color: #FFA500; /* Orange */
}}
/* DataFrame styling */
.css-17z80pu {{
background-color: #d3d3d3; /* Grey */
border: 1px solid #ddd;
border-radius: 4px;
padding: 10px;
}}
/* Custom chart area */
.stAltairChart {{
background-color: #d3d3d3; /* Grey */
border: 1px solid #ddd;
border-radius: 5px;
padding: 10px;
}}
/* Text area styling */
.css-91z34k {{
background-color: #e0e0e0; /* Light Grey for Text Area Box */
border: 1px solid #ddd;
border-radius: 4px;
padding: 10px;
}}
/* Top bar styling */
header[data-testid="stHeader"] {{
background: rgba(0, 0, 0, 0); /* Transparent */
}}
</style>
""",
unsafe_allow_html=True
)
emotions_emoji_dict = {"anger":"😠","disgust":"🤮", "fear":"😨😱", "happiness":"🤗", "joy":"😂", "neutral":"😐", "sadness":"😔", "surprise":"😮"}
def main():
st.set_page_config(page_title="Emotion Classifier App: Veer", layout="wide")
set_bg_hack_url()
st.sidebar.title("Menu")
menu = ["🏠 Home", "📊 Monitor", "ℹ️ About"]
choice = st.sidebar.selectbox("Select an Option", menu)
create_page_visited_table()
create_emotionclf_table()
if choice == "🏠 Home":
add_page_visited_details("Home", datetime.now())
st.title("Emotion Classifier App")
st.subheader("Enter text to analyze its emotion")
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:
prediction = predict_emotions(raw_text)
probability = get_prediction_proba(raw_text)
add_prediction_details(raw_text, prediction, max(probability.values()), datetime.now())
col1, col2 = st.columns(2)
with col1:
st.success("Input Text")
st.write(raw_text)
st.success("Sentiment Prediction")
emoji_icon = emotions_emoji_dict[prediction]
st.write(f"{prediction}: {emoji_icon}")
st.write(f"Confidence: {max(probability.values()):.2f}")
with col2:
st.success("Prediction Probability")
proba_df = pd.DataFrame(list(probability.items()), columns=["emotions", "probability"])
fig = alt.Chart(proba_df).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.title("App Monitoring")
with st.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.expander('Emotion Classifier Metrics'): #initially showed Unicode decode error: utf-8 codec cant decode byte; fix:
try:
prediction_details = view_all_prediction_details()
df_emotions = pd.DataFrame(prediction_details, columns=['Rawtext','Prediction','Probability','Time_of_Visit'])
# fix for unicodedecodeerror: Ensuring all columns are converted to strings to avoid decoding errors.
df_emotions = df_emotions.applymap(lambda x: x.decode('utf-8', 'ignore') if isinstance(x, bytes) else str(x))
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)
except UnicodeDecodeError as e:
st.error(f"Error decoding data: {e}")
else:
st.title("About")
add_page_visited_details("About", datetime.now())
st.subheader("Emotion Classifier App")
st.text("A simple application to classify emotions from text.")
if __name__ == '__main__':
main()
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