Update app.py

app.py

@@ -23,6 +23,7 @@ def process_data(file):
     first_sheet = excel.sheet_names[0]
     df = pd.read_excel(file.name, sheet_name=first_sheet)
     
+    # Clean and Standardize Columns
     df.columns = df.columns.map(lambda x: str(x).strip())
     non_date_cols = ["Keyword", "Avg. monthly searches"]
     available_non_date = [c for c in non_date_cols if c in df.columns]
@@ -40,6 +41,7 @@ def process_data(file):
     df.rename(columns=rename_dict, inplace=True)
     date_cols = list(rename_dict.values())
     
+    # Build Visualization Data
     final_df = df[available_non_date].copy()
     viz_data = []
 
@@ -56,21 +58,27 @@ def process_data(file):
 
     full_viz_df = pd.concat(viz_data)
     
-    # Define and sort SEO buckets
+    # Sort SEO buckets correctly
     sort_order = ["01–03", "04–10", "11–20", "21–30", "31–40", "41–50", "51–100", "100+", "Not Talking (Intent Not Correct)"]
     full_viz_df['Range'] = pd.Categorical(full_viz_df['Range'], categories=sort_order, ordered=True)
-    full_viz_df = full_viz_df.sort_values(['Month', 'Range'])
+    full_viz_df = full_viz_df.sort_values(['Range', 'Month'])
     
     return final_df, full_viz_df, final_df
 
 def filter_table(full_data, selection_data: gr.SelectData):
-    # selection_data.value contains the [Range, Keyword Count]
+    # When clicking a bar, selection_data.value[0] is the X-axis value (The Range, e.g., "01-03")
+    if not selection_data or not selection_data.value:
+        return full_data
+        
     clicked_range = selection_data.value[0]
     
-    # Since we want to find keywords in this range across ANY month in the dataset
+    # Filter Logic: Find rows where this range appears in ANY month column
     range_cols = [c for c in full_data.columns if "Rank Range" in c]
     
-    # Filter rows where the clicked range appears in at least one month's column
+    if not range_cols:
+        return full_data
+        
+    # We filter the master dataframe to show keywords that have this rank in any tracked month
     filtered_df = full_data[full_data[range_cols].apply(lambda row: clicked_range in row.values, axis=1)]
     return filtered_df
 
@@ -81,7 +89,7 @@ with gr.Blocks() as demo:
     full_data_state = gr.State()
 
     gr.Markdown("# 🚀 SEO Market Intelligence Dashboard")
-    gr.Markdown("### Interactive Analysis: Click a bar below to filter the table by rank range.")
+    gr.Markdown("### Click a bar segment below to filter the keyword list.")
     
     with gr.Row():
         file_input = gr.File(label="Upload SEO Excel", file_types=[".xlsx"])
@@ -89,14 +97,14 @@ with gr.Blocks() as demo:
             process_btn = gr.Button("📊 Build Dashboard", variant="primary")
             reset_btn = gr.Button("🔄 Reset Table Filters")
 
-    # REMOVED 'vertical' to fix the TypeError
+    # FIX: Removed 'group' and 'vertical' arguments.
+    # 'color' handles the segmentation automatically.
     plot_output = gr.BarPlot(
         x="Range", 
         y="Keyword Count", 
         color="Month", 
         title="Keyword Performance Distribution",
-        tooltip=["Month", "Range", "Keyword Count"],
-        group="Month"
+        tooltip=["Month", "Range", "Keyword Count"]
     )
     
     gr.Markdown("### 🔍 Keywords List")
@@ -121,5 +129,4 @@ with gr.Blocks() as demo:
     )
 
 if __name__ == "__main__":
-    # Standard launch with theme support
     demo.launch(theme=gr.themes.Soft())