Update app.py

app.py

@@ -20,14 +20,26 @@ def upload_csv(file):
         if df.empty:
             return ["All"], ["All"], ["All"], "The uploaded CSV file is empty.", "All", "All", "All"
         
-        # Define required columns (removed 'Health')
+        # Define required columns
         required_columns = {'DeviceID', 'Lab', 'Type', 'Timestamp', 'Status', 'UsageCount'}
         if not required_columns.issubset(df.columns):
             missing_cols = required_columns - set(df.columns)
             return ["All"], ["All"], ["All"], f"CSV is missing required columns: {', '.join(missing_cols)}", "All", "All", "All"
         
-        # Convert Timestamp to datetime
-        df['Timestamp'] = pd.to_datetime(df['Timestamp'])
+        # Debug: Print the DataFrame to check its content
+        print("DataFrame content:\n", df.to_string())
+        
+        # Check for empty or all-NaN columns
+        if df['Lab'].dropna().empty or df['Type'].dropna().empty:
+            return ["All"], ["All"], ["All"], "Lab or Type columns are empty or contain only NaN values.", "All", "All", "All"
+        
+        # Convert Timestamp to datetime with error handling
+        try:
+            df['Timestamp'] = pd.to_datetime(df['Timestamp'], errors='coerce')
+            if df['Timestamp'].isna().all():
+                return ["All"], ["All"], ["All"], "All Timestamp values are invalid or unparseable.", "All", "All", "All"
+        except Exception as e:
+            return ["All"], ["All"], ["All"], f"Failed to parse Timestamp column: {str(e)}", "All", "All", "All"
         
         # Extract unique values for dropdowns
         labs = ['All'] + sorted([str(lab) for lab in df['Lab'].dropna().unique() if str(lab).strip()])
@@ -47,6 +59,9 @@ def filter_and_visualize(selected_lab, selected_type, selected_date_range):
     if df.empty:
         return None, None, None, None, "No data available."
     
+    # Debug: Print the DataFrame before filtering
+    print("DataFrame before filtering:\n", df.to_string())
+    
     # Filter the DataFrame
     filtered_df = df.copy()
     if selected_lab != "All":
@@ -62,7 +77,10 @@ def filter_and_visualize(selected_lab, selected_type, selected_date_range):
     if filtered_df.empty:
         return None, None, None, None, "No data matches the selected filters."
     
-    # Device Cards (as a table, removed 'Health')
+    # Debug: Print the filtered DataFrame
+    print("Filtered DataFrame:\n", filtered_df.to_string())
+    
+    # Device Cards (as a table)
     device_cards = filtered_df[['DeviceID', 'Lab', 'Type', 'UsageCount', 'Timestamp']].sort_values(by='Timestamp', ascending=False)
     
     # Daily Log Trends (Line Chart)
@@ -128,7 +146,6 @@ def download_pdf(selected_lab, selected_type, selected_date_range):
     pdf.cell(200, 10, txt="LabOps Dashboard Report", ln=True, align='C')
     pdf.ln(10)
     
-    # Add filtered data (removed 'Health')
     for index, row in filtered_df.iterrows():
         line = f"{row['Timestamp']} | {row['DeviceID']} | {row['Lab']} | {row['Type']} | {row['Status']} | {row['UsageCount']}"
         pdf.multi_cell(0, 10, txt=line)