Update app.py

app.py

@@ -13,6 +13,8 @@ from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
 from sklearn.linear_model import LinearRegression
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import LabelEncoder
+from io import BytesIO
+import base64
 
 # Ensure necessary NLTK data is available
 nltk.download('punkt')
@@ -23,16 +25,6 @@ st.title("🔍 Advanced Sentiment Analysis Dashboard")
 st.markdown("Analyze sentiments with deep insights and visualizations")
 
 # Sidebar for user input
-st.sidebar.header("Upload your dataset")
-uploaded_file = st.sidebar.file_uploader("Upload a CSV file", type=["csv"])
-
-if uploaded_file is not None:
-    df = pd.read_csv(uploaded_file)
-    st.sidebar.success("File uploaded successfully!")
-else:
-    st.sidebar.warning("Please upload a CSV file to proceed.")
-
-# Sample Text Input
 st.sidebar.subheader("Enter Text for Sentiment Analysis")
 user_input = st.sidebar.text_area("Type or paste text here", "The product is amazing!")
 
@@ -48,79 +40,77 @@ def analyze_bert_sentiment(text):
     result = bert_sentiment(text)[0]
     return result['label']
 
+def analyze_textblob_sentiment(text):
+    return "Positive" if TextBlob(text).sentiment.polarity > 0 else "Negative" if TextBlob(text).sentiment.polarity < 0 else "Neutral"
+
 if user_input:
     vader_result = analyze_vader_sentiment(user_input)
     bert_result = analyze_bert_sentiment(user_input)
+    textblob_result = analyze_textblob_sentiment(user_input)
+    
     st.sidebar.markdown(f"**VADER Sentiment:** {vader_result}")
     st.sidebar.markdown(f"**BERT Sentiment:** {bert_result}")
+    st.sidebar.markdown(f"**TextBlob Sentiment:** {textblob_result}")
 
-# If dataset is uploaded, analyze sentiments
-if uploaded_file is not None:
-    df['VADER Sentiment'] = df['text'].apply(analyze_vader_sentiment)
-    df['BERT Sentiment'] = df['text'].apply(analyze_bert_sentiment)
-    
-    # Sentiment Distribution
-    st.subheader("📊 Sentiment Distribution")
-    fig, ax = plt.subplots()
-    sns.countplot(x=df['VADER Sentiment'], palette="coolwarm", ax=ax)
-    st.pyplot(fig)
-    
-    # Word Cloud
-    st.subheader("☁️ Word Cloud")
-    sentiment_filter = st.selectbox("Filter Word Cloud by Sentiment", ["All", "Positive", "Neutral", "Negative"])
-    if sentiment_filter != "All":
-        filtered_df = df[df['VADER Sentiment'] == sentiment_filter]
-    else:
-        filtered_df = df
-    all_text = " ".join(filtered_df['text'].dropna().astype(str))
-    wordcloud = WordCloud(width=800, height=400, background_color='white').generate(all_text)
-    st.image(wordcloud.to_array(), use_column_width=True)
-    
-    # Sentiment Over Time (Simulated for Demo)
-    st.subheader("📈 Sentiment Trend Over Time")
-    df['date'] = pd.date_range(end=datetime.today(), periods=len(df), freq='D')
-    sentiment_map = {'Positive': 1, 'Neutral': 0, 'Negative': -1}
-    df['sentiment_score'] = df['VADER Sentiment'].map(sentiment_map)
-    
-    fig = px.line(df, x='date', y='sentiment_score', title='Sentiment Trend')
-    st.plotly_chart(fig)
-    
-    # Regression Model for Sentiment Prediction
-    st.subheader("📈 Sentiment Prediction")
-    label_encoder = LabelEncoder()
-    df['sentiment_encoded'] = label_encoder.fit_transform(df['VADER Sentiment'])
-    X = np.array(range(len(df))).reshape(-1, 1)
-    y = df['sentiment_encoded']
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-    model = LinearRegression()
-    model.fit(X_train, y_train)
-    future_days = np.array(range(len(df), len(df) + 30)).reshape(-1, 1)
-    predicted_sentiments = model.predict(future_days)
-    
-    fig_pred = px.line(x=range(len(df), len(df) + 30), y=predicted_sentiments, title='Predicted Sentiment Trend')
-    st.plotly_chart(fig_pred)
-    
-    # Interactive Pie Chart
-    st.subheader("📝 Sentiment Breakdown")
-    sentiment_counts = df['VADER Sentiment'].value_counts()
-    fig_pie = px.pie(names=sentiment_counts.index, values=sentiment_counts.values, title="Sentiment Breakdown")
-    st.plotly_chart(fig_pie)
-    
-    # Sentiment Heatmap
-    st.subheader("🔥 Sentiment Heatmap")
-    heatmap_df = df.pivot_table(index=df['date'].dt.date, columns='VADER Sentiment', aggfunc='size', fill_value=0)
-    fig_heatmap = px.imshow(heatmap_df.T, title="Sentiment Heatmap", labels=dict(x="Date", y="Sentiment", color="Count"))
-    st.plotly_chart(fig_heatmap)
-    
-    # Keyword Sentiment Analysis
-    st.subheader("🔍 Keyword Sentiment Analysis")
-    keyword = st.text_input("Enter a keyword to analyze sentiment", "great")
-    keyword_df = df[df['text'].str.contains(keyword, case=False, na=False)]
-    st.write(keyword_df[['text', 'VADER Sentiment']])
-    
-    # Download Report as CSV
-    st.subheader("📄 Download Report")
-    csv = df.to_csv(index=False).encode('utf-8')
-    st.download_button(label="Download CSV", data=csv, file_name="sentiment_analysis.csv", mime='text/csv')
+# Simulated past sentiment data
+dates = [datetime.today() - timedelta(days=i) for i in range(14)]
+sentiment_scores = np.random.uniform(-1, 1, len(dates))
+df = pd.DataFrame({"Date": dates, "Sentiment Score": sentiment_scores})
+
+# Train a regression model
+X = np.array(range(len(df))).reshape(-1, 1)
+y = df["Sentiment Score"]
+model = LinearRegression()
+model.fit(X, y)
+
+# Predict for next 7 days
+future_dates = [datetime.today() + timedelta(days=i) for i in range(1, 8)]
+X_future = np.array(range(len(df), len(df) + 7)).reshape(-1, 1)
+predictions = model.predict(X_future)
+future_df = pd.DataFrame({"Date": future_dates, "Predicted Sentiment": predictions})
+
+# Past sentiment trends
+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='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.plotly_chart(fig3)
+
+# Sentiment scatter plot
+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)")
+df['Rolling Avg Sentiment'] = df['Sentiment Score'].rolling(window=7).mean()
+fig5 = px.line(df, x='Date', y='Rolling Avg Sentiment', title="7-Day Rolling Average Sentiment")
+st.plotly_chart(fig5)
+
+# Generate Word Cloud
+def generate_wordcloud(text):
+    wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)
+    img = BytesIO()
+    wordcloud.to_image().save(img, format='PNG')
+    return base64.b64encode(img.getvalue()).decode()
+
+# Word Cloud
+st.subheader("☁️ Word Cloud")
+if user_input:
+    wordcloud_img = f'data:image/png;base64,{generate_wordcloud(user_input)}'
+    st.image(wordcloud_img, use_column_width=True)
+
+# Download Report as CSV
+st.subheader("📄 Download Report")
+csv = df.to_csv(index=False).encode('utf-8')
+st.download_button(label="Download CSV", data=csv, file_name="sentiment_analysis.csv", mime='text/csv')
 
 st.sidebar.markdown("Developed with ❤️")