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()
 
-# Updated AMC reminders function
+# AMC reminders
 def check_amc_reminders(df, current_date):
     try:
         if "device_id" not in df.columns or "amc_date" not in df.columns:
@@ -65,8 +65,7 @@ def check_amc_reminders(df, current_date):
         reminders = df[(df["days_to_amc"] >= 0) & (df["days_to_amc"] <= 30)][["device_id", "log_type", "status", "timestamp", "usage_hours", "downtime", "amc_date"]]
         if reminders.empty:
             return "No AMC reminders due within the next 30 days.", reminders
-        reminder_text = "\n".join([f"- Device ID: {row['device_id']}, AMC Date: {row['amc_date'].strftime('%Y-%m-%d')}" for _, row in reminders.iterrows()])
-        return reminder_text, reminders
+        return "\n".join([f"- Device ID: {row['device_id']}, AMC Date: {row['amc_date']}" for _, row in reminders.head(5).iterrows()]), reminders
     except Exception as e:
         logging.error(f"AMC reminder generation failed: {str(e)}")
         return f"AMC reminder generation failed: {str(e)}", pd.DataFrame()
@@ -354,7 +353,7 @@ async def process_logs(file_obj, lab_site_filter, equipment_type_filter, date_ra
         else:
             df = cached_df_state
 
-        # Apply filters for analyses other than AMC reminders
+        # Apply filters
         filtered_df = df.copy()
         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]
@@ -367,10 +366,12 @@ async def process_logs(file_obj, lab_site_filter, equipment_type_filter, date_ra
             end_date = today + pd.Timedelta(days=days_end) + pd.Timedelta(days=1) - pd.Timedelta(seconds=1)
             start_date = start_date.tz_convert('Asia/Kolkata') if start_date.tzinfo else start_date.tz_localize('Asia/Kolkata')
             end_date = end_date.tz_convert('Asia/Kolkata') if end_date.tzinfo else end_date.tz_localize('Asia/Kolkata')
-            logging.info(f"Date range filter: start_date={start_date}, end_date={end_date}")
+            logging.info(f"Date range filter applied: start_date={start_date}, end_date={end_date}")
             logging.info(f"Before date filter: {len(filtered_df)} rows")
             filtered_df = filtered_df[(filtered_df['timestamp'] >= start_date) & (filtered_df['timestamp'] <= end_date)]
             logging.info(f"After date filter: {len(filtered_df)} rows")
+            if days_start > days_end:
+                logging.warning("Start date is after end date; results may be empty or unexpected.")
 
         if filtered_df.empty:
             return "No data after applying filters.", "<p>No data after filters.</p>", None, '<p>No device cards available.</p>', None, None, None, None, "", "", "", None, df, current_modified_time
@@ -382,8 +383,7 @@ async def process_logs(file_obj, lab_site_filter, equipment_type_filter, date_ra
         # Run critical tasks concurrently
         with ThreadPoolExecutor(max_workers=2) as executor:
             future_anomalies = executor.submit(detect_anomalies, filtered_df)
-            # Use the unfiltered df for AMC reminders to avoid timestamp-based filtering
-            future_amc = executor.submit(check_amc_reminders, df, datetime.now())
+            future_amc = executor.submit(check_amc_reminders, filtered_df, datetime.now())
 
             summary = f"Step 1: Summary Report\n{summarize_logs(filtered_df)}"
             anomalies, anomalies_df = future_anomalies.result()
@@ -405,7 +405,7 @@ async def process_logs(file_obj, lab_site_filter, equipment_type_filter, date_ra
         if elapsed_time > 3:
             logging.warning(f"Processing time exceeded 3 seconds: {elapsed_time:.2f} seconds")
 
-        return (summary, preview_html, usage_chart, device_cards, daily_log_chart, weekly_uptime_chart, anomaly_alerts_chart, downtime_chart, anomalies, amc_reminders, insights, None, df, current_modified_time)
+        return (summary, preview_html, usage_chart, device_cards, daily_log_chart, weekly_uptime_output, anomaly_alerts_output, downtime_chart, anomalies, amc_reminders, insights, None, df, current_modified_time)
     except Exception as e:
         logging.error(f"Failed to process file: {str(e)}")
         return f"Error: {str(e)}", "<p>Error processing data.</p>", None, '<p>Error processing data.</p>', None, None, None, None, "", "", "", None, cached_df_state, last_modified_state
@@ -454,7 +454,7 @@ try:
         .table tr:nth-child(even) {background-color: #f9f9f9;}
     """) as iface:
         gr.Markdown("<h1>LabOps Log Analyzer Dashboard</h1>")
-        gr.Markdown("Upload a CSV file to analyze. Click 'Analyze' to refresh the dashboard. Use 'Export PDF' for report download.")
+        gr.Markdown("Upload a CSV file to analyze. Click 'Analyze' to refresh the dashboard. Use 'Export PDF' for report download. Date Range accepts [start, end] days (e.g., [-7, -3] for last 7 to 3 days ago).")
 
         last_modified_state = gr.State(value=None)
         current_file_state = gr.State(value=None)
@@ -467,7 +467,7 @@ try:
                     gr.Markdown("### Filters")
                     lab_site_filter = gr.Dropdown(label="Lab Site", choices=['All'], value='All', interactive=True)
                     equipment_type_filter = gr.Dropdown(label="Equipment Type", choices=['All'], value='All', interactive=True)
-                    date_range_filter = gr.Slider(label="Date Range (Days from Today, e.g., -7 to 0 means last 7 days)", minimum=-365, maximum=0, step=1, value=[-7, 0])
+                    date_range_filter = gr.Slider(label="Date Range (Days from Today, e.g., [-7, -3] means 7 to 3 days ago)", minimum=-365, maximum=0, step=1, value=[-7, 0], interactive=True)
                     submit_button = gr.Button("Analyze", variant="primary")
                     pdf_button = gr.Button("Export PDF", variant="secondary")