Update app.py

app.py

@@ -381,7 +381,7 @@ def validate_analyst_output(analyst_output):
     return analyst_output
 
 
-# Visualization and Table Display
+# Visualization
 def create_visualizations(analyst_output):
     chart_paths = []
     validated_data = validate_analyst_output(analyst_output)
@@ -392,7 +392,7 @@ def create_visualizations(analyst_output):
             values = item["Values"]
 
             try:
-                # Handle dictionary data for bar charts
+                # Handle dictionary data for bar/pie charts
                 if isinstance(values, dict):
                     df = pd.DataFrame(list(values.items()), columns=["Label", "Count"])
                     if len(df) <= 5:
@@ -402,21 +402,27 @@ def create_visualizations(analyst_output):
 
                 # Handle list data for bar/pie charts
                 elif isinstance(values, list):
-                    # Check if it's a list of dictionaries (e.g., Technology Spotlight)
+                    # Handle 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
+                        # Optional: Generate bar chart for complex data
+                        for col in df.columns:
+                            if pd.api.types.is_numeric_dtype(df[col]):
+                                chart = px.bar(df, x=df.index, y=col, title=f"{category} - {col} Analysis")
+                                st.plotly_chart(chart)
+                                break
+                        continue
+
+                    # Handle simple lists
                     else:
                         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 string data (Insights)
+                # Handle text data
                 elif isinstance(values, str):
                     st.subheader(f"{category} Insights")
                     st.table(pd.DataFrame({"Insights": [values]}))
@@ -427,10 +433,10 @@ def create_visualizations(analyst_output):
                     logging.warning(f"Unsupported data format in {category}: {values}")
                     continue
 
-                # Display in Streamlit
+                # Display the chart
                 st.plotly_chart(chart)
 
-                # Save for PDF export
+                # Save the chart 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)
@@ -441,6 +447,7 @@ def create_visualizations(analyst_output):
 
     return chart_paths
 
+
 def display_table(analyst_output):
     table_data = []
     validated_data = validate_analyst_output(analyst_output)
@@ -451,34 +458,35 @@ def display_table(analyst_output):
             values = item["Values"]
 
             try:
-                # Handle dictionary data (Table View)
+                # Handle dictionary data 
                 if isinstance(values, dict):
                     df = pd.DataFrame(list(values.items()), columns=["Label", "Count"])
                     st.subheader(f"{category} (Table View)")
                     st.dataframe(df)
                     table_data.extend(df.to_dict(orient="records"))
 
-                # Handle list data 
+                # Handle list data
                 elif isinstance(values, list):
-                    # Handle complex lists (list of dictionaries)
                     if all(isinstance(v, dict) for v in values):
+                        # Detailed View for list of dictionaries
                         df = pd.DataFrame(values)
                         st.subheader(f"{category} (Detailed View)")
                         st.dataframe(df)
                         table_data.extend(df.to_dict(orient="records"))
-                    # Handle simple lists 
                     else:
+                        # Simple List View
                         df = pd.DataFrame(values, columns=["Items"])
                         st.subheader(f"{category} (List View)")
                         st.dataframe(df)
                         table_data.extend(df.to_dict(orient="records"))
 
-                # Handle text data
+                # Handle string data 
                 elif isinstance(values, str):
                     st.subheader(f"{category} (Summary)")
                     st.table(pd.DataFrame({"Insights": [values]}))
                     table_data.append({"Category": category, "Values": values})
 
+                # Handle unsupported data types
                 else:
                     st.warning(f"Unsupported data format for {category}")
                     logging.warning(f"Unsupported data in {category}: {values}")
@@ -489,7 +497,6 @@ def display_table(analyst_output):
 
     return table_data
 
-
 def parse_analyst_output(raw_output):
     key_insights = []
     data_insights = []