Update app.py

app.py

@@ -1,12 +1,10 @@
-import dash
-from dash import dcc, html
-import plotly.express as px
-import plotly.graph_objects as go
-import pandas as pd
-import numpy as np
-from datetime import datetime, timedelta
+import streamlit as st
 from transformers import pipeline
 from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+import numpy as np
+import pandas as pd
+from datetime import datetime, timedelta
+import plotly.express as px
 from sklearn.linear_model import LinearRegression
 from wordcloud import WordCloud
 import base64
@@ -41,48 +39,85 @@ def generate_wordcloud(text):
     wordcloud.to_image().save(img, format='PNG')
     return base64.b64encode(img.getvalue()).decode()
 
-# Dash app setup
-app = dash.Dash(__name__)
-app.layout = html.Div([
-    html.H1("Sentiment Analysis Dashboard", style={'textAlign': 'center', 'color': '#333'}),
-    html.Div([
-        dcc.Input(id='text_input', type='text', placeholder='Enter text here...', style={'width': '70%'}),
-        html.Button('Analyze', id='analyze_btn', style={'margin-left': '10px'})
-    ], style={'textAlign': 'center', 'margin-bottom': '20px'}),
-    
-    html.Div(id='sentiment_output', style={'fontSize': '20px', 'textAlign': 'center'}),
-    
-    dcc.Graph(id='sentiment_trend',
-              figure=px.line(df, x='Date', y='Sentiment Score', title='Past Sentiment Trends', markers=True, line_shape='spline')
-              ),
-    
-    dcc.Graph(id='future_trend',
-              figure=px.line(future_df, x='Date', y='Predicted Sentiment', title='Sentiment Prediction for Next 7 Days', markers=True, line_shape='spline')
-              ),
+# Streamlit app setup
+st.title("Interactive 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')
+
+# Display explanation and detailed sentiment scores
+def display_sentiment_analysis(vader_score, bert_result):
+    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})")
     
-    dcc.Graph(id='sentiment_pie',
-              figure=px.pie(values=[sum(df['Sentiment Score'] > 0), sum(df['Sentiment Score'] <= 0)], 
-                           names=['Positive', 'Negative'], title='Sentiment Distribution', hole=0.3)
-              ),
+    # Display sentiment breakdown in a bar chart
+    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"])
     
-    html.Img(id='wordcloud_img', style={'width': '50%', 'margin-top': '20px', 'display': 'block', 'margin-left': 'auto', 'margin-right': 'auto'})
-])
-
-@app.callback(
-    [dash.dependencies.Output('sentiment_output', 'children'),
-     dash.dependencies.Output('wordcloud_img', 'src')],
-    [dash.dependencies.Input('analyze_btn', 'n_clicks')],
-    [dash.dependencies.State('text_input', 'value')]
-)
-def analyze_sentiment(n_clicks, text):
-    if n_clicks:
-        vader_score = vader_analyzer.polarity_scores(text)['compound']
-        bert_result = bert_sentiment(text)[0]
-        explanation = f"VADER Score: {vader_score:.2f}, BERT Sentiment: {bert_result['label']} ({bert_result['score']:.2f})"
-        wordcloud_img = f'data:image/png;base64,{generate_wordcloud(text)}'
-        return explanation, wordcloud_img
-    return '', ''
-if __name__ == '__main__':
-    app.run_server(host='0.0.0.0', port=7860, debug=False, use_reloader=False)
+    st.bar_chart(sentiment_df.set_index("Sentiment"))
+
+    # Generate and display the word cloud
+    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 user_input:
+        with st.spinner('Analyzing text...'):
+            # VADER sentiment analysis
+            vader_score = vader_analyzer.polarity_scores(user_input)['compound']
+            
+            # BERT sentiment analysis
+            bert_result = bert_sentiment(user_input)[0]
+            
+            # Display sentiment analysis details
+            display_sentiment_analysis(vader_score, bert_result)
+    else:
+        st.warning("Please enter some text for analysis.")
+
+# Section for 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.plotly_chart(fig1)
+
+# Section for 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.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.plotly_chart(fig3)
+
+# Histogram of Sentiment Scores (Distribution)
+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)
+
+# Time-Series Heatmap for Sentiment Over Time
+st.subheader("Sentiment Heatmap (Past 14 Days)")
+df['Day'] = df['Date'].dt.dayofweek
+df['Hour'] = df['Date'].dt.hour
+heatmap_data = df.pivot('Day', 'Hour', 'Sentiment Score')
+fig5 = px.imshow(heatmap_data, title="Heatmap of Sentiment Over Time", labels={'x': 'Hour of Day', 'y': 'Day of Week'})
+st.plotly_chart(fig5)
+
+# Scatter plot of Sentiment over Time
+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)
 
+# 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)
 
+# Reset button to clear inputs and visualizations
+if st.sidebar.button('Reset Analysis'):
+    st.experimental_rerun()