Update app.py

app.py

@@ -9,30 +9,34 @@ from reportlab.lib.pagesizes import letter
 from reportlab.pdfgen import canvas
 import tempfile
 
-# Configure logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+# Configure logging to match the log format
+logging.basicConfig(level=logging.INFO, format='%(asctime)s,%(msecs)03d - %(levelname)s - %(message)s')
 
 def process_files(uploaded_files):
     """
     Process uploaded CSV files, generate usage plots, detect anomalies, and process AMC expiries.
-    Returns a dataframe, plot, PDF path, and status message.
+    Returns a dataframe, plot path, PDF path, and AMC expiry message.
     """
+    # Log received files
+    logging.info(f"Received uploaded files: {uploaded_files}")
+
     if not uploaded_files:
         logging.warning("No files uploaded.")
         return None, None, None, "Please upload at least one valid CSV file."
 
     valid_files = [f for f in uploaded_files if f.name.endswith('.csv')]
+    logging.info(f"Processing {len(valid_files)} valid files: {valid_files}")
+
     if not valid_files:
         logging.warning("No valid CSV files uploaded.")
         return None, None, None, "Please upload at least one valid CSV file."
 
-    logging.info(f"Processing {len(valid_files)} valid files: {[f.name for f in valid_files]}")
+    logging.info("Loading logs from uploaded files...")
     all_data = []
 
     # Load and combine CSV files
     for file in valid_files:
         try:
-            logging.info(f"Loading logs from {file.name}")
             df = pd.read_csv(file.name)
             logging.info(f"Loaded {len(df)} records from {file.name}")
             all_data.append(df)
@@ -46,25 +50,37 @@ def process_files(uploaded_files):
 
     combined_df = pd.concat(all_data, ignore_index=True)
     logging.info(f"Combined {len(combined_df)} total records.")
-    logging.info(f"CSV columns: {combined_df.columns.tolist()}")
+    logging.info(f"Loaded {len(combined_df)} log records from uploaded files.")
 
     # Generate usage plot
+    logging.info("Generating usage plot...")
     plot_path = generate_usage_plot(combined_df)
-    
+    if plot_path:
+        logging.info("Usage plot generated successfully.")
+    else:
+        logging.error("Failed to generate usage plot.")
+        return combined_df, None, None, "Failed to generate usage plot."
+
     # Detect anomalies
+    logging.info("Detecting anomalies...")
     anomaly_df = detect_anomalies(combined_df)
-    
+    if anomaly_df is None:
+        logging.error("Failed to detect anomalies.")
+    else:
+        logging.info(f"Detected {sum(anomaly_df['anomaly'] == -1)} anomalies.")
+
     # Process AMC expiries
+    logging.info("Processing AMC expiries...")
     amc_message, amc_df = process_amc_expiries(combined_df)
-    
+
     # Generate PDF report
     pdf_path = generate_pdf_report(combined_df, anomaly_df, amc_df)
-    
+
     # Prepare output dataframe (combine original data with anomalies)
     output_df = combined_df.copy()
     if anomaly_df is not None:
         output_df['anomaly'] = anomaly_df['anomaly']
-    
+
     return output_df, plot_path, pdf_path, amc_message
 
 def generate_usage_plot(df):
@@ -72,7 +88,6 @@ def generate_usage_plot(df):
     Generate a bar plot of usage_count by equipment and status.
     Returns the path to the saved plot.
     """
-    logging.info("Generating usage plot...")
     try:
         plt.figure(figsize=(10, 6))
         for status in df['status'].unique():
@@ -84,13 +99,12 @@ def generate_usage_plot(df):
         plt.legend()
         plt.xticks(rotation=45)
         plt.tight_layout()
-        
+
         # Save plot to temporary file
         with tempfile.NamedTemporaryFile(delete=False, suffix='.png') as tmp:
             plt.savefig(tmp.name, format='png')
             plot_path = tmp.name
         plt.close()
-        logging.info("Usage plot generated successfully.")
         return plot_path
     except Exception as e:
         logging.error(f"Failed to generate usage plot: {str(e)}")
@@ -101,13 +115,11 @@ def detect_anomalies(df):
     Detect anomalies in usage_count using Isolation Forest.
     Returns a dataframe with an 'anomaly' column (-1 for anomalies, 1 for normal).
     """
-    logging.info("Detecting anomalies...")
     try:
         model = IsolationForest(contamination=0.1, random_state=42)
         anomalies = model.fit_predict(df[['usage_count']].values)
         anomaly_df = df.copy()
         anomaly_df['anomaly'] = anomalies
-        logging.info(f"Detected {sum(anomalies == -1)} anomalies.")
         return anomaly_df
     except Exception as e:
         logging.error(f"Failed to detect anomalies: {str(e)}")
@@ -118,14 +130,13 @@ def process_amc_expiries(df):
     Identify devices with AMC expiries within 7 days from 2025-06-05.
     Returns a message and a dataframe of devices with upcoming expiries.
     """
-    logging.info("Processing AMC expiries...")
     try:
         current_date = datetime(2025, 6, 5)
         threshold = current_date + timedelta(days=7)
         df['amc_expiry'] = pd.to_datetime(df['amc_expiry'])
         upcoming_expiries = df[df['amc_expiry'] <= threshold]
         unique_devices = upcoming_expiries['equipment'].unique()
-        message = f"Found {len(unique_devices)} devices with upcoming AMC expiries: {', '.join(unique_devices)}"
+        message = f"Found {len(unique_devices)} devices with upcoming AMC expiries."
         logging.info(message)
         return message, upcoming_expiries
     except Exception as e:
@@ -137,18 +148,21 @@ def generate_pdf_report(original_df, anomaly_df, amc_df):
     Generate a PDF report with data summary, anomalies, and AMC expiries.
     Returns the path to the saved PDF.
     """
-    logging.info("Generating PDF report...")
     try:
+        if original_df is None:
+            logging.warning("No data available for PDF generation.")
+            return None
+
         with tempfile.NamedTemporaryFile(delete=False, suffix='.pdf') as tmp:
             c = canvas.Canvas(tmp.name, pagesize=letter)
             c.drawString(100, 750, "Equipment Log Analysis Report")
             y = 700
-            
+
             # Summary
             c.drawString(100, y, f"Total Records: {len(original_df)}")
             c.drawString(100, y-20, f"Devices: {', '.join(original_df['equipment'].unique())}")
             y -= 40
-            
+
             # Anomalies
             if anomaly_df is not None:
                 num_anomalies = sum(anomaly_df['anomaly'] == -1)
@@ -160,9 +174,9 @@ def generate_pdf_report(original_df, anomaly_df, amc_df):
             else:
                 c.drawString(100, y, "Anomaly detection failed.")
                 y -= 20
-            
+
             # AMC Expiries
-            if amc_df is not None:
+            if amc_df is not None and not amc_df.empty:
                 c.drawString(100, y, f"Devices with Upcoming AMC Expiries: {len(amc_df['equipment'].unique())}")
                 for _, row in amc_df.iterrows():
                     c.drawString(100, y-20, f"{row['equipment']}: {row['amc_expiry'].strftime('%Y-%m-%d')}")
@@ -170,12 +184,10 @@ def generate_pdf_report(original_df, anomaly_df, amc_df):
             else:
                 c.drawString(100, y, "No AMC expiry data available.")
                 y -= 20
-            
+
             c.showPage()
             c.save()
-            pdf_path = tmp.name
-        logging.info("PDF report generated successfully.")
-        return pdf_path
+            return tmp.name
     except Exception as e:
         logging.error(f"Failed to generate PDF report: {str(e)}")
         return None
@@ -192,7 +204,7 @@ with gr.Blocks() as demo:
     with gr.Row():
         output_message = gr.Textbox(label="AMC Expiry Status")
         output_pdf = gr.File(label="Download PDF Report")
-    
+
     process_button.click(
         fn=process_files,
         inputs=[file_input],
@@ -200,4 +212,5 @@ with gr.Blocks() as demo:
     )
 
 if __name__ == "__main__":
+    logging.info("Application starting...")
     demo.launch(server_name="0.0.0.0", server_port=7860)