Update app.py

app.py

@@ -14,13 +14,13 @@ def generate_sample_data():
     # Generate devices
     for i in range(10):
         devices.append({
-            "Device_ID": f"DEV_{i+1}",
+            "Device_ID": f"Device_{i+1}",
             "Lab": random.choice(labs),
             "Equipment_Type": random.choice(equipment_types),
             "Status": random.choice(["Operational", "Down", "Maintenance"])
         })
     
-    # Generate logs for a broader date range (Jan 1, 2025 to Jun 30, 2025)
+    # Generate logs for a broad date range (Jan 1, 2025 to Jun 30, 2025)
     start_date = datetime(2025, 1, 1)
     end_date = datetime(2025, 6, 30)
     for device in devices:
@@ -29,7 +29,7 @@ def generate_sample_data():
             logs.append({
                 "Device_ID": device["Device_ID"],
                 "Log_Timestamp": current_date.strftime("%Y-%m-%d %H:%M:%S"),
-                "Usage_Count": random.randint(0, 100),
+                "Usage_Count": random.randint(0, 50),
                 "Status": random.choice(["Operational", "Down", "Maintenance"]),
                 "AMC_Expiry": (current_date + timedelta(days=random.randint(10, 365))).strftime("%Y-%m-%d")
             })
@@ -62,9 +62,9 @@ def process_dashboard_data(lab_filter, equipment_type_filter, start_date, end_da
                 start_date_dt = datetime.strptime(start_date, "%Y-%m-%d")
                 end_date_dt = datetime.strptime(end_date, "%Y-%m-%d")
                 if start_date_dt > end_date_dt:
-                    return "Error: Start date must be before end date.", None, None, None, None, None
+                    return "Error: Start date must be before end date.", None, None, None, "", None
             except ValueError as e:
-                return f"Error: Invalid date format. Use YYYY-MM-DD (e.g., 2025-05-01). Received: Start={start_date}, End={end_date}", None, None, None, None, None
+                return f"Error: Invalid date format. Use YYYY-MM-DD (e.g., 2025-05-01). Received: Start={start_date}, End={end_date}", None, None, None, "", None
         else:
             start_date_dt = datetime(2025, 1, 1)
             end_date_dt = datetime(2025, 6, 30)
@@ -78,7 +78,6 @@ def process_dashboard_data(lab_filter, equipment_type_filter, start_date, end_da
         
         filtered_logs = logs_df[logs_df["Device_ID"].isin(filtered_devices["Device_ID"])]
         if start_date_dt and end_date_dt:
-            # Convert Log_Timestamp to date for filtering
             filtered_logs["Log_Date"] = pd.to_datetime(filtered_logs["Log_Timestamp"]).dt.date
             start_date_str = start_date_dt.date()
             end_date_str = end_date_dt.date()
@@ -95,222 +94,147 @@ def process_dashboard_data(lab_filter, equipment_type_filter, start_date, end_da
             usage_count = device_logs["Usage_Count"].sum() if not device_logs.empty else 0
             last_log = device_logs["Log_Timestamp"].max() if not device_logs.empty else "No logs"
             device_cards += (
-                f"Device: {device['Device_ID']}, Lab: {device['Lab'] Meet BRD requirements to aggregate logs from multiple objects
-            # Generate sample data to simulate SmartLog__c, Cell_Analysis__c, etc.
-            logs = []
-            for device in devices:
-                for day in range(30):  # Simulate 30 days of logs
-                    log_date = datetime.now() - timedelta(days=30 - day)
-                    logs.append({
-                        "Device_ID": device["Device_ID"],
-                        "Log_Timestamp": log_date.strftime("%Y-%m-%d %H:%M:%S"),
-                        "Usage_Count": random.randint(0, 100),
-                        "Status": random.choice(["Operational", "Down", "Maintenance"]),
-                        "AMC_Expiry": (log_date + timedelta(days=random.randint(10, 365))).strftime("%Y-%m-%d")
-                    })
-            logs_df = pd.DataFrame(logs)
-            
-            return pd.DataFrame(devices), logs_df
-
-        # Process logs to create contact-like records (simulating Contact creation)
-        def create_contact_from_can(cands_from_trigger):
-            con_list = []
-            for candidate in cands_from_trigger:
-                contact = {
-                    "FirstName": candidate.get("First_Name__c", ""),
-                    "LastName": candidate.get("Last_Name__c", ""),
-                    "Email": candidate.get("Email__c", "")
-                }
-                con_list.append(contact)
-            return con_list
-
-        # Generate sample data
-        devices_df, logs_df = generate_sample_data()
-
-        # Process dashboard data with filters
-        def process_dashboard_data(lab_filter, equipment_type_filter, start_date, end_date):
-            try:
-                # Validate and parse date inputs
-                if start_date and end_date:
-                    try:
-                        start_date = start_date.strip() if start_date else ""
-                        end_date = end_date.strip() if end_date else ""
-                        start_date_dt = datetime.strptime(start_date, "%Y-%m-%d")
-                        end_date_dt = datetime.strptime(end_date, "%Y-%m-%d")
-                        if start_date_dt > end_date_dt:
-                            return "Error: Start date must be before end date.", None, None, None, None, None
-                    except ValueError as e:
-                        return f"Error: Invalid date format. Use YYYY-MM-DD (e.g., 2025-05-01). Received: Start={start_date}, End={end_date}", None, None, None, None, None
-                else:
-                    start_date_dt = datetime(2025, 1, 1)
-                    end_date_dt = datetime(2025, 6, 30)
-
-                # Apply filters
-                filtered_devices = devices_df.copy()
-                if lab_filter != "All":
-                    filtered_devices = filtered_devices[filtered_devices["Lab"] == lab_filter]
-                if equipment_type_filter != "All":
-                    filtered_devices = filtered_devices[filtered_devices["Equipment_Type"] == equipment_type_filter]
-                
-                filtered_logs = logs_df[logs_df["Device_ID"].isin(filtered_devices["Device_ID"])]
-                if start_date_dt and end_date_dt:
-                    filtered_logs["Log_Date"] = pd.to_datetime(filtered_logs["Log_Timestamp"]).dt.date
-                    start_date_str = start_date_dt.date()
-                    end_date_str = end_date_dt.date()
-                    filtered_logs = filtered_logs[
-                        (filtered_logs["Log_Date"] >= start_date_str) &
-                        (filtered_logs["Log_Date"] <= end_date_str)
-                    ]
-                    print(f"Filtered logs count: {len(filtered_logs)}")  # Debug log
-                
-                # Device Cards
-                device_cards = "Device Cards:\n"
-                for _, device in filtered_devices.iterrows():
-                    device_logs = filtered_logs[filtered_logs["Device_ID"] == device["Device_ID"]]
-                    usage_count = device_logs["Usage_Count"].sum() if not device_logs.empty else 0
-                    last_log = device_logs["Log_Timestamp"].max() if not device_logs.empty else "No logs"
-                    device_cards += (
-                        f"Device: {device['Device_ID']}, Lab: {device['Lab']}, Type: {device['Equipment_Type']}, "
-                        f"Status: {device['Status']}, Usage Count: {usage_count}, Last Log: {last_log}\n"
-                    )
-                
-                # Daily Log Trends (Matplotlib Plot)
-                daily_trend_plot = None
-                if not filtered_logs.empty:
-                    filtered_logs["Date"] = pd.to_datetime(filtered_logs["Log_Timestamp"]).dt.date
-                    daily_trends = filtered_logs.groupby("Date")["Usage_Count"].sum().reset_index()
-                    plt.figure(figsize=(8, 4))
-                    plt.plot(daily_trends["Date"], daily_trends["Usage_Count"], marker="o")
-                    plt.title("Daily Log Trends")
-                    plt.xlabel("Date")
-                    plt.ylabel("Total Usage Count")
-                    plt.xticks(rotation=45)
-                    plt.tight_layout()
-                    daily_trend_plot = plt.gcf()
-                else:
-                    daily_trend_plot = "No data available for Daily Log Trends in the selected range."
-                
-                # Weekly Uptime % (Matplotlib Plot)
-                uptime_plot = None
-                if not filtered_logs.empty:
-                    filtered_logs["Week"] = pd.to_datetime(filtered_logs["Log_Timestamp"]).dt.isocalendar().week
-                    uptime_data = filtered_logs.groupby("Week")["Status"].value_counts().unstack(fill_value=0)
-                    uptime_data["Uptime_%"] = uptime_data.get("Operational", 0) / (
-                        uptime_data.get("Operational", 0) + uptime_data.get("Down", 0) + uptime_data.get("Maintenance", 0)
-                    ) * 100
-                    plt.figure(figsize=(8, 4))
-                    plt.bar(uptime_data.index, uptime_data["Uptime_%"])
-                    plt.title("Weekly Uptime %")
-                    plt.xlabel("Week")
-                    plt.ylabel("Uptime %")
-                    plt.tight_layout()
-                    uptime_plot = plt.gcf()
-                else:
-                    uptime_plot = "No data available for Weekly Uptime % in the selected range."
-                
-                # Anomaly Alerts (Usage spikes: >2x average usage)
-                anomaly_alerts = "Anomaly Alerts:\n"
-                if not filtered_logs.empty:
-                    avg_usage = filtered_logs["Usage_Count"].mean()
-                    anomalies = filtered_logs[filtered_logs["Usage_Count"] > 2 * avg_usage]
-                    if anomalies.empty:
-                        anomaly_alerts += "No anomalies detected.\n"
-                    for _, log in anomalies.iterrows():
-                        anomaly_alerts += (
-                            f"Device: {log['Device_ID']}, Timestamp: {log['Log_Timestamp']}, "
-                            f"Usage Spike: {log['Usage_Count']} (Avg: {avg_usage:.2f})\n"
-                        )
-                else:
-                    anomaly_alerts += "No data available for anomaly detection.\n"
-                
-                # AMC Reminders (simulated)
-                report = "LabOps Dashboard Report:\n"
-                report += f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
-                report += device_cards + "\n"
-                report += anomaly_alerts + "\n"
-                report += "AMC Reminders:\n"
-                if not filtered_logs.empty:
-                    amc_expiring = filtered_logs[pd.to_datetime(filtered_logs["AMC_Expiry"]) <= (datetime.now() + timedelta(days=14))]
-                    if amc_expiring.empty:
-                        report += "No AMC expiries within the next 14 days.\n"
-                    for _, log in amc_expiring.iterrows():
-                        report += (
-                            f"Device: {log['Device_ID']}, AMC Expiry: {log['AMC_Expiry']}\n"
-                        )
-                else:
-                    report += "No data available for AMC reminders.\n"
-                
-                return device_cards, daily_trend_plot, uptime_plot, anomaly_alerts, report, "labops_report.txt"
-            except Exception as e:
-                return f"Error: {str(e)}", None, None, None, None, None
-
-        # Define Gradio interface
-        with gr.Blocks(title="LabOps Dashboard") as demo:
-            gr.Markdown("# LabOps Dashboard")
-            gr.Markdown("Monitor smart lab devices, view usage trends, uptime, anomalies, and export reports.")
-            gr.Markdown("**Note**: Use the calendar picker to select dates or manually enter in YYYY-MM-DD format (e.g., 2025-05-01). If the calendar picker doesn't appear, enter dates manually.")
-
-            # Filters
-            gr.Markdown("## Filters")
-            lab_filter = gr.Dropdown(choices=["All"] + list(devices_df["Lab"].unique()), label="Lab Site")
-            equipment_type_filter = gr.Dropdown(choices=["All"] + list(devices_df["Equipment_Type"].unique()), label="Equipment Type")
-            start_date = gr.Textbox(
-                label="Start Date (YYYY-MM-DD)",
-                placeholder="Select or enter date (e.g., 2025-05-01)",
-                elem_id="start_date_picker"
+                f"Device: {device['Device_ID']}, Lab: {device['Lab']}, Type: {device['Equipment_Type']}, "
+                f"Status: {device['Status']}, Usage Count: {usage_count}, Last Log: {last_log}\n"
             )
-            end_date = gr.Textbox(
-                label="End Date (YYYY-MM-DD)",
-                placeholder="Select or enter date (e.g., 2025-05-30)",
-                elem_id="end_date_picker"
-            )
-            
-            # Custom JavaScript to enable browser-native date picker
-            gr.HTML("""
-            <script>
-                document.addEventListener('DOMContentLoaded', function() {
-                    const startPicker = document.getElementById('start_date_picker');
-                    const endPicker = document.getElementById('end_date_picker');
-                    if (startPicker && endPicker) {
-                        startPicker.type = 'date';
-                        endPicker.type = 'date';
-                    } else {
-                        console.error('Date picker elements not found');
-                    }
-                });
-            </script>
-            """)
-            
-            # Dashboard Components
-            gr.Markdown("## Device Cards")
-            device_cards_output = gr.Textbox(label="Device Status", lines=10, interactive=False)
-            gr.Markdown("## Daily Log Trends")
-            daily_trend_plot = gr.Plot(label="Daily Usage Trends")
-            gr.Markdown("## Weekly Uptime %")
-            uptime_plot = gr.Plot(label="Weekly Uptime")
-            gr.Markdown("## Anomaly Alerts")
-            anomaly_alerts_output = gr.Textbox(label="Anomaly Alerts", lines=5, interactive=False)
-            
-            # Export Report
-            gr.Markdown("## Export Report")
-            report_output = gr.Textbox(label="Report Preview", lines=10, interactive=False)
-            download_button = gr.File(label="Download Report as Text")
-            
-            # Update dashboard on filter change
-            def update_dashboard(lab, equipment, start_date, end_date):
-                device_cards, daily_trend, uptime, anomalies, report, report_file = process_dashboard_data(
-                    lab, equipment, start_date, end_date
+        
+        # Daily Log Trends (Matplotlib Plot)
+        daily_trend_plot = None
+        if not filtered_logs.empty:
+            filtered_logs["Date"] = pd.to_datetime(filtered_logs["Log_Timestamp"]).dt.date
+            daily_trends = filtered_logs.groupby("Date")["Usage_Count"].sum().reset_index()
+            plt.figure(figsize=(8, 4))
+            plt.plot(daily_trends["Date"], daily_trends["Usage_Count"], marker="o", color="#1f77b4")
+            plt.title("Daily Log Trends")
+            plt.xlabel("Date")
+            plt.ylabel("Total Usage Count")
+            plt.xticks(rotation=45)
+            plt.tight_layout()
+            daily_trend_plot = plt.gcf()
+        else:
+            daily_trend_plot = "No data available for Daily Log Trends in the selected range."
+        
+        # Weekly Uptime % (Matplotlib Plot)
+        uptime_plot = None
+        if not filtered_logs.empty:
+            filtered_logs["Week"] = pd.to_datetime(filtered_logs["Log_Timestamp"]).dt.isocalendar().week
+            uptime_data = filtered_logs.groupby("Week")["Status"].value_counts().unstack(fill_value=0)
+            uptime_data["Uptime_%"] = uptime_data.get("Operational", 0) / (
+                uptime_data.get("Operational", 0) + uptime_data.get("Down", 0) + uptime_data.get("Maintenance", 0)
+            ) * 100
+            plt.figure(figsize=(8, 4))
+            plt.bar(uptime_data.index, uptime_data["Uptime_%"], color="#ff7f0e")
+            plt.title("Weekly Uptime %")
+            plt.xlabel("Week")
+            plt.ylabel("Uptime %")
+            plt.tight_layout()
+            uptime_plot = plt.gcf()
+        else:
+            uptime_plot = "No data available for Weekly Uptime % in the selected range."
+        
+        # Anomaly Alerts (Usage spikes: >2x average usage)
+        anomaly_alerts = "Anomaly Alerts:\n"
+        if not filtered_logs.empty:
+            avg_usage = filtered_logs["Usage_Count"].mean()
+            anomalies = filtered_logs[filtered_logs["Usage_Count"] > 2 * avg_usage]
+            if anomalies.empty:
+                anomaly_alerts += "No anomalies detected.\n"
+            for _, log in anomalies.iterrows():
+                anomaly_alerts += (
+                    f"Device: {log['Device_ID']}, Timestamp: {log['Log_Timestamp']}, "
+                    f"Usage Spike: {log['Usage_Count']} (Avg: {avg_usage:.2f})\n"
                 )
-                if report_file:
-                    with open(report_file, "w") as f:
-                        f.write(report)
-                return device_cards, daily_trend, uptime, anomalies, report, report_file
-            
-            gr.Button("Update Dashboard").click(
-                fn=update_dashboard,
-                inputs=[lab_filter, equipment_type_filter, start_date, end_date],
-                outputs=[device_cards_output, daily_trend_plot, uptime_plot, anomaly_alerts_output, report_output, download_button]
-            )
+        else:
+            anomaly_alerts += "No data available for anomaly detection.\n"
+        
+        # AMC Reminders (simulated)
+        report = "LabOps Dashboard Report:\n"
+        report += f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
+        report += device_cards + "\n"
+        report += anomaly_alerts + "\n"
+        report += "AMC Reminders:\n"
+        if not filtered_logs.empty:
+            amc_expiring = filtered_logs[pd.to_datetime(filtered_logs["AMC_Expiry"]) <= (datetime.now() + timedelta(days=14))]
+            if amc_expiring.empty:
+                report += "No AMC expiries within the next 14 days.\n"
+            for _, log in amc_expiring.iterrows():
+                report += (
+                    f"Device: {log['Device_ID']}, AMC Expiry: {log['AMC_Expiry']}\n"
+                )
+        else:
+            report += "No data available for AMC reminders.\n"
+        
+        return device_cards, daily_trend_plot, uptime_plot, anomaly_alerts, report, "labops_report.txt"
+    except Exception as e:
+        return f"Error: {str(e)}", None, None, None, "", None
+
+# Define Gradio interface
+with gr.Blocks(title="LabOps Dashboard") as demo:
+    gr.Markdown("# LabOps Dashboard")
+    gr.Markdown("Monitor smart lab devices, view usage trends, uptime, anomalies, and export reports.")
+    gr.Markdown("**Note**: Use the calendar picker to select dates or manually enter in YYYY-MM-DD format (e.g., 2025-05-01). If the calendar picker doesn't appear, enter dates manually.")
+
+    # Filters
+    gr.Markdown("## Filters")
+    lab_filter = gr.Dropdown(choices=["All"] + list(devices_df["Lab"].unique()), label="Lab Site")
+    equipment_type_filter = gr.Dropdown(choices=["All"] + list(devices_df["Equipment_Type"].unique()), label="Equipment Type")
+    start_date = gr.Textbox(
+        label="Start Date (YYYY-MM-DD)",
+        placeholder="Select or enter date (e.g., 2025-05-01)",
+        elem_id="start_date_picker"
+    )
+    end_date = gr.Textbox(
+        label="End Date (YYYY-MM-DD)",
+        placeholder="Select or enter date (e.g., 2025-05-30)",
+        elem_id="end_date_picker"
+    )
+    
+    # Custom JavaScript to enable browser-native date picker
+    gr.HTML("""
+    <script>
+        document.addEventListener('DOMContentLoaded', function() {
+            const startPicker = document.getElementById('start_date_picker');
+            const endPicker = document.getElementById('end_date_picker');
+            if (startPicker && endPicker) {
+                startPicker.type = 'date';
+                endPicker.type = 'date';
+            } else {
+                console.error('Date picker elements not found');
+            }
+        });
+    </script>
+    """)
+    
+    # Dashboard Components
+    gr.Markdown("## Device Cards")
+    device_cards_output = gr.Textbox(label="Device Status", lines=10, interactive=False)
+    gr.Markdown("## Daily Log Trends")
+    daily_trend_plot = gr.Plot(label="Daily Usage Trends")
+    gr.Markdown("## Weekly Uptime %")
+    uptime_plot = gr.Plot(label="Weekly Uptime")
+    gr.Markdown("## Anomaly Alerts")
+    anomaly_alerts_output = gr.Textbox(label="Anomaly Alerts", lines=5, interactive=False)
+    
+    # Export Report
+    gr.Markdown("## Export Report")
+    report_output = gr.Textbox(label="Report Preview", lines=10, interactive=False)
+    download_button = gr.File(label="Download Report as Text")
+    
+    # Update dashboard on filter change
+    def update_dashboard(lab, equipment, start_date, end_date):
+        device_cards, daily_trend, uptime, anomalies, report, report_file = process_dashboard_data(
+            lab, equipment, start_date, end_date
+        )
+        if report_file:
+            with open(report_file, "w") as f:
+                f.write(report)
+        return device_cards, daily_trend, uptime, anomalies, report, report_file
+    
+    gr.Button("Update Dashboard").click(
+        fn=update_dashboard,
+        inputs=[lab_filter, equipment_type_filter, start_date, end_date],
+        outputs=[device_cards_output, daily_trend_plot, uptime_plot, anomaly_alerts_output, report_output, download_button]
+    )
 
-        # Launch the app
-        demo.launch()
+# Launch the app
+demo.launch()