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import streamlit as st
import webbrowser
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import nltk
import time
import plotly.graph_objects as go
# Set page config at the very beginning
st.set_page_config(page_title="Theaimart - Sentiment Analysis", layout="wide", initial_sidebar_state="auto")
# Download necessary NLTK data
@st.cache_resource
def download_nltk_data():
nltk.download('vader_lexicon', quiet=True)
download_nltk_data()
sid = SentimentIntensityAnalyzer()
def analyze_sentiment(sentence):
if sentence:
sentiment_scores = sid.polarity_scores(sentence)
highest_score = max(sentiment_scores, key=sentiment_scores.get)
sentiment_dict = {
'neg': 'Negative 😔',
'neu': 'Neutral 😐',
'pos': 'Positive 😊',
'compound': 'Mixed 🤔'
}
highest_sentiment = sentiment_dict[highest_score]
return sentiment_scores, highest_sentiment
return None, None
# Custom CSS to improve the app's appearance
st.markdown("""
<style>
.main {
background-color: #f0f2f6;
}
.stButton>button {
color: #ffffff;
background-color: #4CAF50;
border-radius: 5px;
border: none;
padding: 10px 24px;
transition: all 0.3s ease-in-out;
}
.stButton>button:hover {
background-color: #45a049;
transform: translateY(-2px);
}
.stTextInput>div>div>input {
border-radius: 5px;
}
.stTextArea textarea {
border: 2px solid #4CAF50;
border-radius: 5px;
}
.stTextArea textarea:focus {
box-shadow: 0 0 5px #4CAF50;
}
div.row-widget.stButton {
text-align: center;
}
</style>
""", unsafe_allow_html=True)
# App title with animation
st.markdown(
"""
<h1 style='text-align: center; color: #2E86C1; animation: fadeIn 1.5s;'>
Sentiment Analysis Tool
</h1>
""",
unsafe_allow_html=True
)
# Animated description
st.markdown(
"""
<p style='text-align: center; font-size: 18px; animation: slideIn 1.5s;'>
Analyze the sentiment of your text with our advanced AI-powered tool!
</p>
""",
unsafe_allow_html=True
)
input_text = st.text_area("Enter text for sentiment analysis", height=150)
# Center the Analyze button
col1, col2, col3 = st.columns([1,1,1])
with col2:
analyze_button = st.button("Analyze")
if analyze_button:
with st.spinner("Analyzing sentiment..."):
sentiment_scores, highest_sentiment = analyze_sentiment(input_text)
if sentiment_scores:
# Create a radar chart for sentiment scores
categories = ['Negative', 'Neutral', 'Positive', 'Compound']
values = [sentiment_scores['neg'], sentiment_scores['neu'], sentiment_scores['pos'], sentiment_scores['compound']]
fig = go.Figure(data=go.Scatterpolar(
r=values,
theta=categories,
fill='toself',
line=dict(color='#2E86C1')
))
fig.update_layout(
polar=dict(
radialaxis=dict(visible=True, range=[0, 1])
),
showlegend=False
)
st.plotly_chart(fig, use_container_width=True)
st.markdown(f"<h2 style='text-align: center; color: #2E86C1;'>Overall Sentiment: {highest_sentiment}</h2>", unsafe_allow_html=True)
# Display detailed scores
col1, col2, col3, col4 = st.columns(4)
col1.metric("Negative", f"{sentiment_scores['neg']:.2f}")
col2.metric("Neutral", f"{sentiment_scores['neu']:.2f}")
col3.metric("Positive", f"{sentiment_scores['pos']:.2f}")
col4.metric("Compound", f"{sentiment_scores['compound']:.2f}")
with st.spinner("Loading ad..."):
time.sleep(3) # Simulate a delay for loading
webbrowser.open("https://www.highrevenuenetwork.com/dt90hpe6y?key=472b3a55761032431e75af65c838cef4")
# Footer
st.markdown(
"""
<div style='text-align: center; padding: 20px; animation: fadeIn 2s;'>
<p>Powered by Theaimart © 2024</p>
</div>
""",
unsafe_allow_html=True
) |