Add Streamlit sentiment analysis app

app.py

@@ -33,19 +33,25 @@ if uploaded_file is not None:
     if st.button("Analyze Sentiment"):
         df["Sentiment"] = predict_sentiment(df[text_column].astype(str).tolist())
         
-        # Display results
-        st.write("Sentiment Analysis Results:")
-        st.dataframe(df[[text_column, "Sentiment"]])
-        
-        # Pie chart
-        sentiment_counts = df["Sentiment"].value_counts()
-        all_sentiments = ["Very Negative", "Negative", "Neutral", "Positive", "Very Positive"]
-        sentiment_counts = sentiment_counts.reindex(all_sentiments, fill_value=0)
-        
-        fig, ax = plt.subplots()
-        ax.pie(sentiment_counts, labels=sentiment_counts.index, autopct='%1.1f%%', colors=["red", "orange", "gray", "lightgreen", "green"])
-        ax.set_title("Sentiment Distribution")
-        st.pyplot(fig)
-        
-        # Download option
-        st.download_button("Download Results", df.to_csv(index=False).encode('utf-8'), "sentiment_results.csv", "text/csv")
+        # Store results in session state
+        st.session_state["df_results"] = df
+
+# Display the results only if available in session state
+if "df_results" in st.session_state:
+    df = st.session_state["df_results"]
+
+    st.write("### Sentiment Analysis Results:")
+    st.dataframe(df[[text_column, "Sentiment"]])
+
+    # Pie chart
+    sentiment_counts = df["Sentiment"].value_counts()
+    all_sentiments = ["Very Negative", "Negative", "Neutral", "Positive", "Very Positive"]
+    sentiment_counts = sentiment_counts.reindex(all_sentiments, fill_value=0)
+
+    fig, ax = plt.subplots()
+    ax.pie(sentiment_counts, labels=sentiment_counts.index, autopct='%1.1f%%', colors=["red", "orange", "gray", "lightgreen", "green"])
+    ax.set_title("Sentiment Distribution")
+    st.pyplot(fig)
+
+    # Download option
+    st.download_button("Download Results", df.to_csv(index=False).encode('utf-8'), "sentiment_results.csv", "text/csv")