Update app.py

app.py

@@ -1,3 +1,6 @@
+"""
+LabOps Log Analyzer Dashboard with CSV file upload and optional PDF generation
+"""
 import gradio as gr
 import pandas as pd
 from datetime import datetime
@@ -206,7 +209,6 @@ async def process_logs(file_obj, progress=gr.Progress()):
             logging.error("Unsupported file format")
             return "Unsupported file format. Please upload a CSV file.", "", None, "", "", "", None
 
-        progress(0.05, "Loading CSV file...")
         try:
             required_columns = ["device_id", "log_type", "status", "timestamp", "usage_hours", "downtime", "amc_date"]
             dtypes = {
@@ -218,17 +220,16 @@ async def process_logs(file_obj, progress=gr.Progress()):
                 "amc_date": "string"
             }
             df = pd.read_csv(file_obj, dtype=dtypes)
-            # Validate required columns
+            # Check for required columns
             missing_columns = [col for col in required_columns if col not in df.columns]
             if missing_columns:
-                logging.error(f"Missing required columns: {missing_columns}")
+                logging.error(f"Missing columns: {missing_columns}")
                 return f"Missing required columns: {missing_columns}", None, None, None, None, None, None
             logging.info(f"File loaded successfully with {len(df)} rows")
         except Exception as e:
             logging.error(f"Failed to load CSV: {str(e)}")
             return f"Failed to load CSV: {str(e)}", None, None, None, None, None, None
 
-        progress(0.1, "Converting timestamps...")
         try:
             df["timestamp"] = pd.to_datetime(df["timestamp"], errors='coerce')
         except Exception as e:
@@ -237,47 +238,56 @@ async def process_logs(file_obj, progress=gr.Progress()):
 
         if df.empty:
             logging.warning("No data provided in the file")
-            return "No data available in provided.", "No data to preview.", None, "No anomalies detected.", "No AMC reminders.", "No insights generated.", None
+            return "No data available in provided.", None, None, None, None, None, None
 
         # Step 1: Summary
-        progress(0.2, "Generating summary...")
-        summary = f"Step 1: Summary Report\n{summarize_logs(df, progress)}"
+        progress(0.2, "Summary...")
+        summary = f"Step 1: Summary Report\n{summarize_logs(df)}"
 
         # Step 2: Log Preview
-        progress(0.3, "Previewing logs...")
+        progress(0.3, "Previewing...")
         if not df.empty:
             preview_lines = ["Step 2: Log Preview (First 5 Rows)"]
-            for idx, row in df.head().iterrows():
-                preview_lines.append(f"Row {idx + 1}: Device ID: {row['device_id']}, Log Type: {row['log_type']}, Status: {row['status']}, Timestamp: {row['timestamp']}, Usage Hours: {row['usage_hours']}, Downtime: {row['downtime']}, AMC Date: {row['amc_date']}")
-            preview = "\ scorer_lines)
+            for idx, row in df.head(5).iterrows():
+                preview_lines.append(
+                    f"Row {idx + 1}: Device ID: {row['device_id']}, "
+                    f"Log Type: {row['log_type']}, Status: {row['status']}, "
+                    f"Timestamp: {row['timestamp']}, Usage Hours: {row['usage_hours']}, "
+                    f"Downtime: {row['downtime']}, AMC Date: {row['amc_date']}"
+                )
+            preview = "\n".join(preview_lines)
         else:
-            preview.append("Step 2: Log Preview\nNo data available.")
+            preview = "Step 2: Log Preview\nNo data available."
 
         # Step 3: Usage Chart
-        chart = create_usage_chart(df, progress)
+        progress(0.5, "Chart...")
+        chart = create_usage_chart(df)
 
         # Step 4: Anomaly Detection
-        anomalies = f"Step 3: Anomaly Detection\n{detect_anomalies(df, progress)}"
+        progress(0.7, "Anomalies...")
+        anomalies = f"Anomaly Detection\n{detect_anomalies(df)}"
 
         # Step 5: AMC Reminders
-        amc_reminders = f"Step 4: AMC Reminders\n{check_amc_reminders(df, datetime.now(), progress)}"
+        progress(0.8, "AMC...")
+        amc_reminders = f"AMC Reminders\n{check_amc_reminders(df, datetime.now())}"
 
         # Step 6: Insights
-        insights = f"Step 5: Dashboard Insights (AI)\n{generate_dashboard_insights(df, progress)}"
+        progress(0.9, "Insights...")
+        insights = f"Dashboard Insights (AI)\n{generate_dashboard_insights(df)}"
 
         # Generate PDF if available
-        progress(0.95, "Generating PDF report...")
+        progress(0.95, "PDF...")
         pdf_file = generate_pdf_content(summary, preview, anomalies, amc_reminders, insights) if reportlab_available else None
         if pdf_file is None and reportlab_available:
-            logging.warning("PDF generation failed, but analysis completed")
+            logging.warning("PDF generation failed")
         elif pdf_file is None:
-            logging.info("PDF generation skipped due to missing reportlab")
+            logging.info("PDF skipped: no reportlab")
 
-        progress(1.0, "Processing complete!")
+        progress(1.0, "Done!")
         return summary, preview, chart, anomalies, amc_reminders, insights, pdf_file
     except Exception as e:
         logging.error(f"Failed to process file: {str(e)}")
-        return f"Error processing file: {error(e)}", None, None, None, None, None, None
+        return f"Error processing file: {str(e)}", None, None, None, None, None, None
 
 # Gradio Interface
 try: