Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+
+# Set custom theme and configuration
+st.set_page_config(page_title="SentimentSense", page_icon="🧠", layout="wide")
+
+# Add custom styling
+st.markdown(
+    """
+    <style>
+    .main {
+        background-color: #f4f4f9;
+    }
+    .stButton>button {
+        background-color: #4CAF50;
+        color: white;
+        border: None;
+        border-radius: 12px;
+        height: 40px;
+        width: 100%;
+        margin: 0.5rem 0;
+    }
+    .stDownloadButton>button {
+        background-color: #1E88E5;
+        color: white;
+        border: None;
+        border-radius: 12px;
+        height: 40px;
+        width: 100%;
+        margin: 0.5rem 0;
+    }
+    </style>
+    """, unsafe_allow_html=True
+)
+
+# Title and description with an emoji
+st.title("🧠 SentimentSense: Your Text Analysis Companion")
+st.markdown(
+    """
+    **SentimentSense** provides in-depth sentiment analysis to understand the emotions conveyed in your text.
+    Simply input your text below and let the magic happen!
+    """
+)
+
+# Initialize or load session state to keep track of the results
+if 'results' not in st.session_state:
+    st.session_state['results'] = []
+
+# Sidebar for text input and actions
+st.sidebar.header("📝 Input Text for Sentiment Analysis")
+text_input = st.sidebar.text_area("Enter text for sentiment analysis:", height=150)
+clear_button = st.sidebar.button("Clear Input")
+reset_button = st.sidebar.button("Reset Analysis History")
+
+if clear_button:
+    text_input = ""
+if reset_button:
+    st.session_state['results'] = []
+
+
+# Function to analyze sentiment
+def analyze_sentiment(text):
+    analyzer = SentimentIntensityAnalyzer()
+    sentiment = analyzer.polarity_scores(text)
+    return sentiment
+
+
+# Function to get primary sentiment with an emoji
+def get_primary_sentiment(sentiment):
+    if sentiment['compound'] >= 0.05:
+        return "Positive 😊"
+    elif sentiment['compound'] <= -0.05:
+        return "Negative 😠"
+    else:
+        return "Neutral 😐"
+
+
+# Analyze button
+if st.sidebar.button("Analyze Sentiment"):
+    if text_input:
+        sentiment = analyze_sentiment(text_input)
+        primary_sentiment = get_primary_sentiment(sentiment)
+
+        # Display results in the main area
+        st.subheader("Analysis Results")
+        col1, col2 = st.columns(2)
+        with col1:
+            st.metric(label="Primary Sentiment", value=primary_sentiment)
+            st.metric(label="Compound Score", value=round(sentiment['compound'], 2))
+        with col2:
+            st.metric(label="Positive Score", value=round(sentiment['pos'], 2))
+            st.metric(label="Neutral Score", value=round(sentiment['neu'], 2))
+            st.metric(label="Negative Score", value=round(sentiment['neg'], 2))
+
+        # Interactive Visualization - Pie Chart for Sentiment Breakdown
+        st.subheader("Sentiment Score Distribution")
+        pie_chart = px.pie(
+            names=['Positive', 'Neutral', 'Negative'],
+            values=[sentiment['pos'], sentiment['neu'], sentiment['neg']],
+            color=['Positive', 'Neutral', 'Negative'],
+            color_discrete_map={'Positive': '#00C853', 'Neutral': '#039BE5', 'Negative': '#D32F2F'},
+            title='Sentiment Breakdown'
+        )
+        st.plotly_chart(pie_chart, use_container_width=True)
+
+        # Interactive Visualization - Gauge Chart for Compound Score
+        gauge_chart = go.Figure(go.Indicator(
+            mode="gauge+number",
+            value=sentiment['compound'],
+            title={'text': "Compound Sentiment Score"},
+            gauge={'axis': {'range': [-1, 1]},
+                   'bar': {'color': "#1E88E5"},
+                   'steps': [
+                       {'range': [-1, -0.05], 'color': '#D32F2F'},
+                       {'range': [-0.05, 0.05], 'color': '#039BE5'},
+                       {'range': [0.05, 1], 'color': '#00C853'}],
+                   }))
+        st.plotly_chart(gauge_chart, use_container_width=True)
+
+        # Store results in session state
+        st.session_state['results'].append({
+            "Text": text_input,
+            "Primary Sentiment": primary_sentiment,
+            "Positive": sentiment['pos'],
+            "Neutral": sentiment['neu'],
+            "Negative": sentiment['neg'],
+            "Compound": sentiment['compound']
+        })
+    else:
+        st.warning("Please enter text for analysis.")
+
+# Show past analysis results with a unique style
+if st.session_state['results']:
+    st.subheader("📊 Sentiment Analysis History")
+    result_df = pd.DataFrame(st.session_state['results'])
+
+    # Interactive Timeline of Results using Plotly
+    timeline_chart = px.scatter(
+        result_df,
+        x='Compound',
+        y='Primary Sentiment',
+        color='Primary Sentiment',
+        hover_data=['Text'],
+        title='Sentiment Analysis History Timeline',
+        color_discrete_map={"Positive 😊": "#00C853", "Neutral 😐": "#039BE5", "Negative 😠": "#D32F2F"}
+    )
+    st.plotly_chart(timeline_chart, use_container_width=True)
+
+    # Download button for results
+    csv = result_df.to_csv(index=False).encode('utf-8')
+    st.download_button(
+        label="Download Results as CSV",
+        data=csv,
+        file_name='sentiment_analysis_results.csv',
+        mime='text/csv',
+    )