Update app.py

app.py

@@ -392,50 +392,52 @@ def create_visualizations(analyst_output):
             values = item["Values"]
 
             try:
-                # Handle dictionary data
+                # Handle dictionary data for bar charts
                 if isinstance(values, dict):
                     df = pd.DataFrame(list(values.items()), columns=["Label", "Count"])
-
-                    # Choose Pie Chart for fewer categories, else Bar Chart
                     if len(df) <= 5:
                         chart = px.pie(df, names="Label", values="Count", title=f"{category} Distribution")
                     else:
                         chart = px.bar(df, x="Label", y="Count", title=f"{category} Analysis")
 
-                # Handle list data 
+                # Handle list data for bar/pie charts
                 elif isinstance(values, list):
-                    # Convert the list into a frequency count without dummy values
-                    df = pd.DataFrame(values, columns=["Label"])
-                    df = df["Label"].value_counts().reset_index()
-                    df.columns = ["Label", "Count"]
-
-                    # Plot as a bar chart or pie chart
-                    if len(df) <= 5:
-                        chart = px.pie(df, names="Label", values="Count", title=f"{category} Distribution")
+                    # Check if it's a list of dictionaries (e.g., Technology Spotlight)
+                    if all(isinstance(v, dict) for v in values):
+                        df = pd.DataFrame(values)
+                        st.subheader(f"{category} (Detailed View)")
+                        st.dataframe(df)
+                        continue  # Skip chart for detailed data
+
+                    # Frequency analysis for simple lists
                     else:
-                        chart = px.bar(df, x="Label", y="Count", title=f"{category} Frequency")
+                        df = pd.DataFrame(values, columns=["Items"])
+                        df = df["Items"].value_counts().reset_index()
+                        df.columns = ["Label", "Count"]
+                        chart = px.pie(df, names="Label", values="Count", title=f"{category} Distribution") if len(df) <= 5 else px.bar(df, x="Label", y="Count", title=f"{category} Frequency")
 
-                # Handle text data
+                # Handle string data (Insights)
                 elif isinstance(values, str):
                     st.subheader(f"{category} Insights")
                     st.table(pd.DataFrame({"Insights": [values]}))
-                    continue  # No chart for text data
+                    continue
 
                 else:
                     st.warning(f"Unsupported data format for category: {category}")
+                    logging.warning(f"Unsupported data format in {category}: {values}")
                     continue
 
-                # Display the chart in Streamlit
+                # Display in Streamlit
                 st.plotly_chart(chart)
 
-                # Save the chart for PDF export
+                # Save for PDF export
                 with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_chart:
                     chart.write_image(temp_chart.name)
                     chart_paths.append(temp_chart.name)
 
             except Exception as e:
                 st.error(f"Failed to generate visualization for {category}: {e}")
-                logging.error(f"Error in {category} visualization: {e}")
+                logging.error(f"Visualization error in {category}: {e}")
 
     return chart_paths