Add PNG/HTML export for charts and CSV export for analysis

src/streamlit_app.py

@@ -8,6 +8,8 @@ import pandas as pd
 import plotly.express as px
 import json
 from api_handler import AINewsAnalyzer
+import io
+
 
 # Page configuration
 st.set_page_config(
@@ -200,72 +202,109 @@ def main():
             st.session_state.df = df
             st.session_state.query = final_query
             st.session_state.days = days
+
+# ===== Display results if data is available =====
+if 'df' in st.session_state and not st.session_state.df.empty:
+    df = st.session_state.df
+
+    # ===== Summary Metrics =====
+    st.markdown("### 📊 Analysis Summary")
+    col1, col2, col3, col4 = st.columns(4)
+
+    with col1:
+        st.metric("📰 Total Articles", len(df))
+    with col2:
+        avg_polarity = df['sentiment_polarity'].mean()
+        delta_polarity = f"{avg_polarity:+.3f}"
+        st.metric("🎭 Avg Sentiment", f"{avg_polarity:.3f}", delta_polarity)
+    with col3:
+        positive_pct = (len(df[df['sentiment_label'] == 'positive']) / len(df) * 100)
+        st.metric("😊 Positive %", f"{positive_pct:.1f}%")
+    with col4:
+        unique_sources = df['source'].nunique()
+        st.metric("📺 News Sources", unique_sources)
+
+
+    # ===== Charts =====
+    st.markdown("### 📈 Visual Analysis")
+    col1, col2 = st.columns(2)
+
+    # Sentiment Distribution
+    dist_fig = create_sentiment_distribution(df)
+    if dist_fig:
+        st.plotly_chart(dist_fig, use_container_width=True, key="dist_fig")
+        # Export buttons
+        buf = io.BytesIO()
+        dist_fig.update_layout(template="plotly_white")
+        dist_fig.update_layout(plot_bgcolor='white', paper_bgcolor='white')  # 设置白底
+        dist_fig.write_image(buf, format="png", engine="kaleido")
+        st.download_button("📷 Download Distribution Chart as PNG", buf.getvalue(),
+                        "distribution_chart.png", mime="image/png")
+        st.download_button("🌐 Download Distribution Chart as HTML",
+                        dist_fig.to_html().encode("utf-8"), "distribution_chart.html",
+                        mime="text/html")
+
+    # Source Analysis
+    source_fig = create_source_analysis(df)
+    if source_fig:
+        st.plotly_chart(source_fig, use_container_width=True, key="source_fig")
+        buf = io.BytesIO()
+        source_fig.update_layout(template="plotly_white")
+        source_fig.update_layout(plot_bgcolor='white', paper_bgcolor='white')  # 白底
+        source_fig.write_image(buf, format="png", engine="kaleido")
+        st.download_button("📷 Download Source Chart as PNG", buf.getvalue(),
+                        "source_chart.png", mime="image/png")
+        st.download_button("🌐 Download Source Chart as HTML",
+                        source_fig.to_html().encode("utf-8"), "source_chart.html",
+                        mime="text/html")
+
+    # Polarity Distribution
+    polarity_fig = create_polarity_distribution(df)
+    if polarity_fig:
+        st.plotly_chart(polarity_fig, use_container_width=True, key="polarity_fig")
+        buf = io.BytesIO()
+        polarity_fig.update_layout(template="plotly_white")
+        polarity_fig.update_layout(plot_bgcolor='white', paper_bgcolor='white')  # 白底
+        polarity_fig.write_image(buf, format="png", engine="kaleido")
+        st.download_button("📷 Download Polarity Chart as PNG", buf.getvalue(),
+                        "polarity_chart.png", mime="image/png")
+        st.download_button("🌐 Download Polarity Chart as HTML",
+                        polarity_fig.to_html().encode("utf-8"), "polarity_chart.html",
+                        mime="text/html")
+
+   
+    # ===== Export CSV button =====
+    csv_data = df.to_csv(index=False).encode('utf-8')
+    st.download_button(
+        label="💾 Export Analysis as CSV",
+        data=csv_data,
+        file_name=f"ai_news_analysis_{st.session_state.query.replace(' ', '_')}.csv",
+        mime='text/csv'
+    )
+       
     
-    # Display results if data is available
-    if 'df' in st.session_state:
-        df = st.session_state.df
-        
-        # Summary metrics
-        st.markdown("### 📊 Analysis Summary")
-        col1, col2, col3, col4 = st.columns(4)
-        
-        with col1:
-            st.metric("📰 Total Articles", len(df))
-        
-        with col2:
-            avg_polarity = df['sentiment_polarity'].mean()
-            delta_polarity = f"{avg_polarity:+.3f}"
-            st.metric("🎭 Avg Sentiment", f"{avg_polarity:.3f}", delta_polarity)
-        
-        with col3:
-            positive_pct = (len(df[df['sentiment_label'] == 'positive']) / len(df) * 100)
-            st.metric("😊 Positive %", f"{positive_pct:.1f}%")
-        
-        with col4:
-            unique_sources = df['source'].nunique()
-            st.metric("📺 News Sources", unique_sources)
-        
-        # Charts
-        st.markdown("### 📈 Visual Analysis")
-        
-        # Row 1: Distribution and source analysis
-        col1, col2 = st.columns(2)
-        
-        with col1:
-            dist_fig = create_sentiment_distribution(df)
-            if dist_fig:
-                st.plotly_chart(dist_fig, use_container_width=True)
-        
-        with col2:
-            source_fig = create_source_analysis(df)
-            if source_fig:
-                st.plotly_chart(source_fig, use_container_width=True)
-        
-        # Row 2: Polarity distribution (full width)
-        polarity_fig = create_polarity_distribution(df)
-        if polarity_fig:
-            st.plotly_chart(polarity_fig, use_container_width=True)
-        
+else:
+    # Welcome message
+    st.info("👋 Welcome! Configure your analysis settings in the sidebar and click 'Analyze News' to get started.")
     
-    else:
-        # Welcome message
-        st.info("👋 Welcome! Configure your analysis settings in the sidebar and click 'Analyze News' to get started.")
-        
-        # Sample visualization or instructions
-        st.markdown("""
-        ### 🚀 How to Use:
-        
-        1. **Choose a topic** from the dropdown or enter your own search term
-        2. **Select time range** (1-30 days) to analyze recent news
-        3. **Pick news sources** or leave as 'All Sources' for comprehensive coverage
-        4. **Click 'Analyze News'** to fetch and analyze articles
-        
-        ### 📊 What You'll Get:
-        
-        - **Sentiment Analysis** of headlines and descriptions
-        - **Interactive Charts** showing trends over time
-        - **Source Breakdown** to see which outlets cover your topic
-        """)
+    # Sample visualization or instructions
+    st.markdown("""
+    ### 🚀 How to Use:
+    
+    1. **Choose a topic** from the dropdown or enter your own search term
+    2. **Select time range** (1-30 days) to analyze recent news
+    3. **Pick news sources** or leave as 'All Sources' for comprehensive coverage
+    4. **Click 'Analyze News'** to fetch and analyze articles
+    
+    ### 📊 What You'll Get:
+    
+    - **Sentiment Analysis** of headlines and descriptions
+    - **Interactive Charts** showing trends over time
+    - **Source Breakdown** to see which outlets cover your topic
+    """)
+
+
+
 
 if __name__ == "__main__":
     main()