Update app.py

app.py

@@ -9,9 +9,13 @@ from sklearn.linear_model import LinearRegression
 from wordcloud import WordCloud
 import base64
 from io import BytesIO
+import nltk
+from textblob import TextBlob
+
+nltk.download('punkt')
 
 # Initialize sentiment models
-bert_sentiment = pipeline("sentiment-analysis")
+bert_sentiment = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
 vader_analyzer = SentimentIntensityAnalyzer()
 
 # Generate sample past sentiment data
@@ -40,81 +44,62 @@ def generate_wordcloud(text):
     return base64.b64encode(img.getvalue()).decode()
 
 # Streamlit app setup
-st.title("Interactive Sentiment Analysis Dashboard")
+st.title("🌟 Advanced Sentiment Analysis Dashboard")
 
-# Sidebar for navigation and settings
-st.sidebar.header("Sentiment Analysis Controls")
-st.sidebar.subheader("Input")
-user_input = st.sidebar.text_input('Enter text for sentiment analysis')
+# Sidebar for user input
+st.sidebar.header("🔍 Sentiment Analysis Controls")
+user_input = st.sidebar.text_area("Enter text for sentiment analysis")
 
 # Display sentiment analysis results
-def display_sentiment_analysis(vader_score, bert_result):
-    st.subheader("Sentiment Analysis Results:")
+def display_sentiment_analysis(vader_score, bert_result, textblob_score):
+    st.subheader("📊 Sentiment Analysis Results:")
     st.write(f"**VADER Sentiment Score**: {vader_score:.2f}")
     st.write(f"**BERT Sentiment**: {bert_result['label']} ({bert_result['score']:.2f})")
-    
+    st.write(f"**TextBlob Sentiment Polarity**: {textblob_score:.2f}")
+
     sentiment_data = {'Positive': max(0, vader_score), 'Negative': min(0, vader_score), 'Neutral': 1 - abs(vader_score)}
     sentiment_df = pd.DataFrame(list(sentiment_data.items()), columns=["Sentiment", "Score"])
     st.bar_chart(sentiment_df.set_index("Sentiment"))
-    
+
     wordcloud_img = f'data:image/png;base64,{generate_wordcloud(user_input)}'
     st.image(wordcloud_img, use_column_width=True)
 
-# Analyze sentiment on button click
-if st.sidebar.button('Analyze Sentiment'):
+if st.sidebar.button("Analyze Sentiment"):
     if user_input:
-        with st.spinner('Analyzing text...'):
+        with st.spinner("Analyzing text..."):
             vader_score = vader_analyzer.polarity_scores(user_input)['compound']
             bert_result = bert_sentiment(user_input)[0]
-            display_sentiment_analysis(vader_score, bert_result)
+            textblob_score = TextBlob(user_input).sentiment.polarity
+            display_sentiment_analysis(vader_score, bert_result, textblob_score)
     else:
-        st.warning("Please enter some text for analysis.")
+        st.warning("⚠️ Please enter some text for analysis.")
 
 # Past sentiment trends
-st.subheader("Past Sentiment Trends (Last 14 Days)")
-fig1 = px.line(df, x='Date', y='Sentiment Score', title='Past Sentiment Trends', markers=True, line_shape='spline')
+st.subheader("📅 Past Sentiment Trends (Last 14 Days)")
+fig1 = px.line(df, x='Date', y='Sentiment Score', title='Sentiment Over Time', markers=True, line_shape='spline')
 st.plotly_chart(fig1)
 
 # Future sentiment predictions
-st.subheader("Sentiment Prediction for Next 7 Days")
-fig2 = px.line(future_df, x='Date', y='Predicted Sentiment', title='Sentiment Prediction for Next 7 Days', markers=True, line_shape='spline')
+st.subheader("🔮 Sentiment Prediction for Next 7 Days")
+fig2 = px.line(future_df, x='Date', y='Predicted Sentiment', title='Predicted Sentiment Trend', markers=True, line_shape='spline')
 st.plotly_chart(fig2)
 
 # Sentiment distribution pie chart
-st.subheader("Sentiment Distribution")
-fig3 = px.pie(values=[sum(df['Sentiment Score'] > 0), sum(df['Sentiment Score'] <= 0)], 
-              names=['Positive', 'Negative'], title='Sentiment Distribution', hole=0.3)
+st.subheader("📊 Sentiment Distribution")
+fig3 = px.pie(values=[sum(df['Sentiment Score'] > 0), sum(df['Sentiment Score'] <= 0)], names=['Positive', 'Negative'], title='Sentiment Distribution', hole=0.3)
 st.plotly_chart(fig3)
 
-# Histogram of Sentiment Scores
-st.subheader("Sentiment Score Distribution (Past 14 Days)")
-fig4 = px.histogram(df, x='Sentiment Score', nbins=20, title="Sentiment Score Distribution")
-st.plotly_chart(fig4)
-
-# Sentiment heatmap (corrected version)
-st.subheader("Sentiment Heatmap (Past 14 Days)")
-df['Day'] = df['Date'].dt.dayofweek  # Monday=0, Sunday=6
-df['Hour'] = np.random.randint(0, 24, len(df))  # Simulating hourly data
-heatmap_data = df.pivot(index='Day', columns='Hour', values='Sentiment Score')
-fig5 = px.imshow(
-    heatmap_data, 
-    title="Heatmap of Sentiment Over Time", 
-    labels={'x': 'Hour of Day', 'y': 'Day of Week'}, 
-    color_continuous_scale='RdBu'
-)
-st.plotly_chart(fig5)
-
 # Sentiment scatter plot
-st.subheader("Sentiment Scatter Plot (Past 14 Days)")
-fig6 = px.scatter(df, x='Date', y='Sentiment Score', title='Sentiment Over Time')
-st.plotly_chart(fig6)
+st.subheader("🔎 Sentiment Scatter Plot (Last 14 Days)")
+fig4 = px.scatter(df, x='Date', y='Sentiment Score', title='Sentiment Over Time')
+st.plotly_chart(fig4)
 
 # Rolling average sentiment
-st.subheader("Rolling Average of Sentiment (7-Day Window)")
+st.subheader("📈 Rolling Average of Sentiment (7-Day Window)")
 df['Rolling Avg Sentiment'] = df['Sentiment Score'].rolling(window=7).mean()
-fig7 = px.line(df, x='Date', y='Rolling Avg Sentiment', title="Rolling Average of Sentiment (7-Day Window)")
-st.plotly_chart(fig7)
+fig5 = px.line(df, x='Date', y='Rolling Avg Sentiment', title="7-Day Rolling Average Sentiment")
+st.plotly_chart(fig5)
 
 # Reset button
-if st.sidebar.button('Reset Analysis'):
-    st.experimental_rerun()
+if st.sidebar.button('🔄 Reset Analysis'):
+    st.experimental_rerun()