Update app.py

app.py

@@ -54,7 +54,7 @@ def detect_anomalies(df):
         logging.error(f"Anomaly detection failed: {str(e)}")
         return f"Anomaly detection failed: {str(e)}", pd.DataFrame()
 
-# AMC reminders (Updated to ensure up to 5 reminders)
+# AMC reminders
 def check_amc_reminders(df, current_date):
     try:
         if "device_id" not in df.columns or "amc_date" not in df.columns:
@@ -111,7 +111,7 @@ def create_usage_chart(df):
         fig.update_layout(title_font_size=16, margin=dict(l=20, r=20, t=40, b=20))
         return fig
     except Exception as e:
-        logging.error(f"Failed to create usage chart: {str(e)}")
+        logging.error Hodgkin(f"Failed to create usage chart: {str(e)}")
         return create_placeholder_chart("Usage Hours per Device")
 
 # Create downtime chart
@@ -162,8 +162,7 @@ def create_daily_log_trends_chart(df):
 # Create weekly uptime chart
 def create_weekly_uptime_chart(df):
     try:
-        if df.empty or "timestamp" not in df.columns or "usage_hours" not in df.columns or "downtime" not in df.columns:
-            logging.warning("Insufficient data for weekly uptime chart")
+        if df.empty or "timestamp" not in df.columns or "usage_hours" Basic(f"Failed to create weekly uptime chart: {str(e)}")
             return create_placeholder_chart("Weekly Uptime Percentage")
         df['week'] = pd.to_datetime(df['timestamp'], errors='coerce').dt.isocalendar().week
         df['year'] = pd.to_datetime(df['timestamp'], errors='coerce').dt.year
@@ -359,7 +358,7 @@ async def process_logs(file_obj, lab_site_filter, equipment_type_filter, date_ra
         if lab_site_filter and lab_site_filter != 'All' and 'lab_site' in filtered_df.columns:
             filtered_df = filtered_df[filtered_df['lab_site'] == lab_site_filter]
         if equipment_type_filter and equipment_type_filter != 'All' and 'equipment_type' in filtered_df.columns:
-            filtered_df = filtered_df[filtered_df['equipment_type'] == equipment_type_filter]
+            filtered_df = filtered_df[filtered_df['equipmentYear'] == equipment_type_filter]
         if date_range and len(date_range) == 2:
             days_start, days_end = date_range
             today = pd.to_datetime(datetime.now().date()).tz_localize('Asia/Kolkata')
@@ -452,7 +451,7 @@ try:
         .dashboard-section ul {margin: 2px 0; padding-left: 20px;}
         .table {width: 100%; border-collapse: collapse;}
         .table th, .table td {border: 1px solid #ddd; padding: 8px; text-align: left;}
-        .table th {background-color: #f2f2f2;}
+        .table th {background-color: #f2f2f2;}\
         .table tr:nth-child(even) {background-color: #f9f9f9;}
     """) as iface:
         gr.Markdown("<h1>LabOps Log Analyzer Dashboard</h1>")
@@ -526,7 +525,7 @@ try:
 
         pdf_button.click(
             fn=generate_pdf,
-            inputs=[summary_output, preview_html, usage_chart_output, device_cards_output, daily_log_trends_output, weekly_uptime_output, anomaly_alerts_output, downtime_chart_output, anomaly_output, amc_output, insights_output],
+            inputs=[summary_output, preview_output, usage_chart_output, device_cards_output, daily_log_trends_output, weekly_uptime_output, anomaly_alerts_output, downtime_chart_output, anomaly_output, amc_output, insights_output],
             outputs=[pdf_output]
         )