Update app.py

app.py

@@ -4,89 +4,101 @@ import matplotlib.pyplot as plt
 from fpdf import FPDF
 import io
 from datetime import datetime, timedelta
+import sys
+
+# Check library versions for debugging
+debug_msg = "Library Versions:\n"
+try:
+    debug_msg += f"Python: {sys.version}\n"
+    debug_msg += f"Gradio: {gr.__version__}\n"
+    debug_msg += f"Pandas: {pd.__version__}\n"
+    debug_msg += f"Matplotlib: {matplotlib.__version__}\n"
+    debug_msg += f"FPDF: {FPDF.FPDF_VERSION}\n"
+except Exception as e:
+    debug_msg += f"Error checking library versions: {str(e)}\n"
 
 # Global DataFrame to store the CSV data
 df = pd.DataFrame()
 
 def upload_csv(file):
     global df
-    debug_msg = "Starting CSV upload process...\n"
+    debug_msg_local = debug_msg + "\nStarting CSV upload process...\n"
     try:
         if file is None:
-            return ["All"], ["All"], ["All"], f"{debug_msg}No file uploaded. Please upload a CSV file.", "All", "All", "All", None, None, None, None
+            return ["All"], ["All"], ["All"], f"{debug_msg_local}No file uploaded. Please upload a CSV file.", "All", "All", "All", None, None, None, None
         
         # Read the CSV file
-        debug_msg += "Reading CSV file...\n"
+        debug_msg_local += "Reading CSV file...\n"
         df = pd.read_csv(file)
         
         if df.empty:
-            return ["All"], ["All"], ["All"], f"{debug_msg}The uploaded CSV file is empty.", "All", "All", "All", None, None, None, None
+            return ["All"], ["All"], ["All"], f"{debug_msg_local}The uploaded CSV file is empty.", "All", "All", "All", None, None, None, None
         
         # Debug: Show the CSV content and column names
-        debug_msg += f"CSV Columns: {', '.join(df.columns)}\nRaw CSV Content:\n{df.to_string()}\n\n"
+        debug_msg_local += f"CSV Columns: {', '.join(df.columns)}\nRaw CSV Content:\n{df.to_string()}\n\n"
         
         # 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"{debug_msg}Error: CSV is missing required columns: {', '.join(missing_cols)}", "All", "All", "All", None, None, None, None
+            return ["All"], ["All"], ["All"], f"{debug_msg_local}Error: CSV is missing required columns: {', '.join(missing_cols)}", "All", "All", "All", None, None, None, None
         
         # Debug: Check data types and sample values
-        debug_msg += f"Data Types:\n{df.dtypes}\n\nSample Values:\n{df.head().to_string()}\n\n"
+        debug_msg_local += f"Data Types:\n{df.dtypes}\n\nSample Values:\n{df.head().to_string()}\n\n"
         
         # Check for empty or all-NaN columns
         if df['Lab'].dropna().empty:
-            debug_msg += "Error: Lab column is empty or contains only NaN values.\n"
+            debug_msg_local += "Error: Lab column is empty or contains only NaN values.\n"
         if df['Type'].dropna().empty:
-            debug_msg += "Error: Type column is empty or contains only NaN values.\n"
+            debug_msg_local += "Error: Type column is empty or contains only NaN values.\n"
         if df['Lab'].dropna().empty or df['Type'].dropna().empty:
-            return ["All"], ["All"], ["All"], debug_msg, "All", "All", "All", None, None, None, None
+            return ["All"], ["All"], ["All"], debug_msg_local, "All", "All", "All", None, None, None, None
         
         # Convert Timestamp to datetime with error handling
-        debug_msg += "Converting Timestamp column...\n"
+        debug_msg_local += "Converting Timestamp column...\n"
         try:
             df['Timestamp'] = pd.to_datetime(df['Timestamp'], errors='coerce')
-            debug_msg += f"Timestamps after conversion:\n{df['Timestamp'].to_string()}\n\n"
+            debug_msg_local += f"Timestamps after conversion:\n{df['Timestamp'].to_string()}\n\n"
             timestamps_invalid = df['Timestamp'].isna().all()
             if timestamps_invalid:
-                debug_msg += "Warning: All Timestamp values are invalid or unparseable. Date range filtering will be disabled.\n"
+                debug_msg_local += "Warning: All Timestamp values are invalid or unparseable. Date range filtering will be disabled.\n"
         except Exception as e:
-            return ["All"], ["All"], ["All"], f"{debug_msg}Error: Failed to parse Timestamp column: {str(e)}", "All", "All", "All", None, None, None, None
+            return ["All"], ["All"], ["All"], f"{debug_msg_local}Error: Failed to parse Timestamp column: {str(e)}", "All", "All", "All", None, None, None, None
         
         # Extract unique values for dropdowns
-        debug_msg += "Extracting unique values for dropdowns...\n"
+        debug_msg_local += "Extracting unique values for dropdowns...\n"
         labs = ['All'] + sorted([str(lab) for lab in df['Lab'].dropna().unique()])
         types = ['All'] + sorted([str(v) for v in df['Type'].dropna().unique()])
-        debug_msg += f"Lab options: {', '.join(labs)}\nType options: {', '.join(types)}\n\n"
+        debug_msg_local += f"Lab options: {', '.join(labs)}\nType options: {', '.join(types)}\n\n"
         
         # Extract date range for filter
         if timestamps_invalid:
             date_ranges = ['All']
-            debug_msg += "Date range dropdown disabled due to invalid timestamps.\n"
+            debug_msg_local += "Date range dropdown disabled due to invalid timestamps.\n"
         else:
             min_date = df['Timestamp'].min()
             max_date = df['Timestamp'].max()
             if pd.isna(min_date) or pd.isna(max_date):
                 date_ranges = ['All']
-                debug_msg += "Warning: Could not determine date range due to invalid timestamps.\n"
+                debug_msg_local += "Warning: Could not determine date range due to invalid timestamps.\n"
             else:
                 min_date_str = min_date.strftime('%Y-%m-%d')
                 max_date_str = max_date.strftime('%Y-%m-%d')
                 date_ranges = ['All', f"{min_date_str} to {max_date_str}"]
-                debug_msg += f"Date Range: {min_date_str} to {max_date_str}\n"
+                debug_msg_local += f"Date Range: {min_date_str} to {max_date_str}\n"
         
         # Automatically trigger filter_and_visualize after upload with default filters
-        debug_msg += "Triggering initial visualization with default filters...\n"
+        debug_msg_local += "Triggering initial visualization with default filters...\n"
         try:
             device_cards, plot_daily, plot_uptime, anomaly_text, filter_msg = filter_and_visualize("All", "All", "All")
-            debug_msg += f"Initial Filter Result: {filter_msg}\n"
+            debug_msg_local += f"Initial Filter Result: {filter_msg}\n"
         except Exception as e:
-            debug_msg += f"Initial Filter Error: {str(e)}\n"
+            debug_msg_local += f"Initial Filter Error: {str(e)}\n"
             device_cards, plot_daily, plot_uptime, anomaly_text = None, None, None, None
         
-        return labs, types, date_ranges, debug_msg, "All", "All", "All", device_cards, plot_daily, plot_uptime, anomaly_text
+        return labs, types, date_ranges, debug_msg_local, "All", "All", "All", device_cards, plot_daily, plot_uptime, anomaly_text
     except Exception as e:
-        return ["All"], ["All"], ["All"], f"{debug_msg}Failed to process CSV: {str(e)}", "All", "All", "All", None, None, None, None
+        return ["All"], ["All"], ["All"], f"{debug_msg_local}Failed to process CSV: {str(e)}", "All", "All", "All", None, None, None, None
 
 def filter_and_visualize(selected_lab, selected_type, selected_date_range):
     global df
@@ -295,49 +307,52 @@ def download_pdf(selected_lab, selected_type, selected_date_range):
         return None
 
 # Build the Gradio interface
-with gr.Blocks() as demo:
-    gr.Markdown("🧪 **Multi-Device LabOps Dashboard**\nMonitor smart lab devices, visualize logs, and generate PDF reports.")
+try:
+    with gr.Blocks() as demo:
+        gr.Markdown("🧪 **Multi-Device LabOps Dashboard**\nMonitor smart lab devices, visualize logs, and generate PDF reports.")
 
-    with gr.Row():
-        csv_input = gr.File(label="Upload Device Logs CSV", file_types=[".csv"])
-    
-    with gr.Row():
-        lab_dropdown = gr.Dropdown(label="Filter by Lab", choices=["All"], value="All")
-        type_dropdown = gr.Dropdown(label="Filter by Equipment Type", choices=["All"], value="All")
-        date_dropdown = gr.Dropdown(label="Filter by Date Range", choices=["All"], value="All")
+        with gr.Row():
+            csv_input = gr.File(label="Upload Device Logs CSV", file_types=[".csv"])
+        
+        with gr.Row():
+            lab_dropdown = gr.Dropdown(label="Filter by Lab", choices=["All"], value="All")
+            type_dropdown = gr.Dropdown(label="Filter by Equipment Type", choices=["All"], value="All")
+            date_dropdown = gr.Dropdown(label="Filter by Date Range", choices=["All"], value="All")
 
-    with gr.Row():
-        submit_btn = gr.Button("Submit Filters")
+        with gr.Row():
+            submit_btn = gr.Button("Submit Filters")
 
-    with gr.Row():
-        device_cards = gr.DataFrame(label="Device Cards (Usage, Last Log)")
-        plot_daily = gr.Image(label="Daily Log Trends")
-        plot_uptime = gr.Image(label="Weekly Uptime %")
+        with gr.Row():
+            device_cards = gr.DataFrame(label="Device Cards (Usage, Last Log)")
+            plot_daily = gr.Image(label="Daily Log Trends")
+            plot_uptime = gr.Image(label="Weekly Uptime %")
 
-    anomaly_output = gr.Textbox(label="Anomaly Alerts")
-    
-    with gr.Row():
-        download_btn = gr.Button("Download PDF Report")
-    
-    error_box = gr.Textbox(label="Status/Error Message", visible=True, interactive=False)
+        anomaly_output = gr.Textbox(label="Anomaly Alerts")
+        
+        with gr.Row():
+            download_btn = gr.Button("Download PDF Report")
+        
+        error_box = gr.Textbox(label="Status/Error Message", visible=True, interactive=False)
 
-    # Connect the components
-    csv_input.change(
-        fn=upload_csv,
-        inputs=csv_input,
-        outputs=[lab_dropdown, type_dropdown, date_dropdown, error_box, lab_dropdown, type_dropdown, date_dropdown, device_cards, plot_daily, plot_uptime, anomaly_output]
-    )
-    
-    submit_btn.click(
-        fn=filter_and_visualize,
-        inputs=[lab_dropdown, type_dropdown, date_dropdown],
-        outputs=[device_cards, plot_daily, plot_uptime, anomaly_output, error_box]
-    )
-    
-    download_btn.click(
-        fn=download_pdf,
-        inputs=[lab_dropdown, type_dropdown, date_dropdown],
-        outputs=gr.File(label="Download PDF")
-    )
+        # Connect the components
+        csv_input.change(
+            fn=upload_csv,
+            inputs=csv_input,
+            outputs=[lab_dropdown, type_dropdown, date_dropdown, error_box, lab_dropdown, type_dropdown, date_dropdown, device_cards, plot_daily, plot_uptime, anomaly_output]
+        )
+        
+        submit_btn.click(
+            fn=filter_and_visualize,
+            inputs=[lab_dropdown, type_dropdown, date_dropdown],
+            outputs=[device_cards, plot_daily, plot_uptime, anomaly_output, error_box]
+        )
+        
+        download_btn.click(
+            fn=download_pdf,
+            inputs=[lab_dropdown, type_dropdown, date_dropdown],
+            outputs=gr.File(label="Download PDF")
+        )
 
-demo.launch()
+    demo.launch()
+except Exception as e:
+    print(f"Error launching Gradio interface: {str(e)}")