Fix column mismatch and improve error handling

- Made column parsing flexible to handle stations with different numbers of columns (7, 9, 12, etc.)
- Added missing columns as NaN when station has fewer columns than expected
- Improved error messages for station data fetching failures
- Removed Great Bear from dropdown as it's currently unavailable
- Added info note about Great Bear unavailability

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <[email protected]>

app.py

@@ -77,11 +77,16 @@ def scrape_weather_data(site_id, hours=720):
             
             print(f"Found {len(content['rows'] if content else [])} rows of data")
             browser.close()
+
+            if not content or not content.get('rows'):
+                raise ValueError(f"No data found for station {site_id}. Station may not exist or may not be reporting.")
+
             return content
-            
+
     except Exception as e:
-        print(f"Error scraping data: {str(e)}")
-        raise e
+        error_msg = f"Error scraping data for {site_id}: {str(e)}"
+        print(error_msg)
+        raise ValueError(error_msg)
 
 def parse_date(date_str):
     """Parse date string to datetime"""
@@ -95,15 +100,29 @@ def parse_weather_data(data):
     """Parse the weather data into a pandas DataFrame"""
     if not data or 'rows' not in data:
         raise ValueError("No valid weather data found")
-        
+
     df = pd.DataFrame(data['rows'])
-    
-    columns = ['datetime', 'temp', 'dew_point', 'humidity', 'wind_chill', 
-              'wind_dir', 'wind_speed', 'snow_depth', 'snowfall_3hr', 
-              'snowfall_6hr', 'snowfall_24hr', 'swe']
-    
-    df = df.iloc[:, :12]
-    df.columns = columns
+
+    # Different stations have different numbers of columns
+    # Standard columns we expect (in order)
+    expected_columns = ['datetime', 'temp', 'dew_point', 'humidity', 'wind_chill',
+                       'wind_dir', 'wind_speed', 'snow_depth', 'snowfall_3hr',
+                       'snowfall_6hr', 'snowfall_24hr', 'swe']
+
+    # Get actual number of columns
+    num_cols = len(df.columns)
+    print(f"Data has {num_cols} columns")
+
+    # Assign column names based on actual number of columns
+    if num_cols <= len(expected_columns):
+        df.columns = expected_columns[:num_cols]
+        # Add missing columns with NaN values
+        for col in expected_columns[num_cols:]:
+            df[col] = np.nan
+    else:
+        # If more columns than expected, just use the first 12
+        df = df.iloc[:, :12]
+        df.columns = expected_columns
     
     numeric_cols = ['temp', 'dew_point', 'humidity', 'wind_chill', 'snow_depth',
                    'snowfall_3hr', 'snowfall_6hr', 'snowfall_24hr', 'swe']
@@ -324,11 +343,11 @@ with gr.Blocks(title="Weather Station Data Analyzer") as demo:
                 ("Yellowstone Club - Andesite", "YCAND"),
                 ("Yellowstone Club - American Spirit", "YCAMS"),
                 ("Yellowstone Club - Base", "YCBAS"),
-                ("Yellowstone Club - Great Bear", "YCGBR"),
                 ("Bozeman Airport", "KBZN"),
                 ("Salt Lake City", "KSLC")
             ],
-            value="YCTIM"
+            value="YCTIM",
+            info="Note: Great Bear (YCGBR) is currently unavailable"
         )
         hours = gr.Number(
             label="Hours of Data",