Update app.py

app.py

@@ -78,8 +78,7 @@ def upload_csv(file):
         # Automatically trigger filter_and_visualize after upload with default filters
         debug_msg += "Triggering initial visualization with default filters...\n"
         try:
-            result = filter_and_visualize("All", "All", "All")
-            device_cards, plot_daily, plot_uptime, anomaly_text, filter_msg = result
+            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"
         except Exception as e:
             debug_msg += f"Initial Filter Error: {str(e)}\n"
@@ -91,182 +90,209 @@ def upload_csv(file):
 
 def filter_and_visualize(selected_lab, selected_type, selected_date_range):
     global df
-    if df.empty:
-        return None, None, None, None, "No data available."
-    
-    # Debug: Log the filter parameters
-    error_msg = f"Applying filters: Lab={selected_lab}, Type={selected_type}, Date Range={selected_date_range}\n"
-    
-    # Filter the DataFrame
-    filtered_df = df.copy()
-    error_msg += f"Initial DataFrame: {len(filtered_df)} rows\n"
-    
-    if selected_lab != "All":
-        filtered_df = filtered_df[filtered_df["Lab"] == selected_lab]
-        error_msg += f"After Lab filter ({selected_lab}): {len(filtered_df)} rows\n"
-    if selected_type != "All":
-        filtered_df = filtered_df[filtered_df["Type"] == selected_type]
-        error_msg += f"After Type filter ({selected_type}): {len(filtered_df)} rows\n"
-    if selected_date_range != "All" and selected_date_range != "No data available." and not df['Timestamp'].isna().all():
+    error_msg = "Starting filter and visualize process...\n"
+    try:
+        if df.empty:
+            return None, None, None, None, f"{error_msg}No data available."
+        
+        # Debug: Log the filter parameters
+        error_msg += f"Applying filters: Lab={selected_lab}, Type={selected_type}, Date Range={selected_date_range}\n"
+        
+        # Filter the DataFrame
+        filtered_df = df.copy()
+        error_msg += f"Initial DataFrame: {len(filtered_df)} rows\n"
+        
+        if selected_lab != "All":
+            filtered_df = filtered_df[filtered_df["Lab"] == selected_lab]
+            error_msg += f"After Lab filter ({selected_lab}): {len(filtered_df)} rows\n"
+        if selected_type != "All":
+            filtered_df = filtered_df[filtered_df["Type"] == selected_type]
+            error_msg += f"After Type filter ({selected_type}): {len(filtered_df)} rows\n"
+        if selected_date_range != "All" and selected_date_range != "No data available." and not df['Timestamp'].isna().all():
+            try:
+                start_date, end_date = selected_date_range.split(" to ")
+                start_date = pd.to_datetime(start_date)
+                end_date = pd.to_datetime(end_date) + timedelta(days=1)  # Include end date
+                filtered_df = filtered_df[(filtered_df["Timestamp"] >= start_date) & (filtered_df["Timestamp"] < end_date)]
+                error_msg += f"After Date Range filter ({start_date} to {end_date}): {len(filtered_df)} rows\n"
+            except Exception as e:
+                error_msg += f"Error parsing date range: {str(e)}\n"
+        
+        if filtered_df.empty:
+            return None, None, None, None, f"{error_msg}No data matches the selected filters."
+        
+        # Debug: Log the filtered DataFrame
+        error_msg += f"Filtered DataFrame:\n{filtered_df.to_string()}\n"
+        
+        # Device Cards (as a table)
+        device_cards = filtered_df[['DeviceID', 'Lab', 'Type', 'UsageCount', 'Timestamp']].sort_values(by='Timestamp', ascending=False)
+        
+        # Daily Log Trends (Line Chart)
         try:
-            start_date, end_date = selected_date_range.split(" to ")
-            start_date = pd.to_datetime(start_date)
-            end_date = pd.to_datetime(end_date) + timedelta(days=1)  # Include end date
-            filtered_df = filtered_df[(filtered_df["Timestamp"] >= start_date) & (filtered_df["Timestamp"] < end_date)]
-            error_msg += f"After Date Range filter ({start_date} to {end_date}): {len(filtered_df)} rows\n"
+            if df['Timestamp'].isna().all():
+                error_msg += "Warning: All timestamps are invalid. Skipping Daily Log Trends.\n"
+                plt.figure(figsize=(8, 4))
+                plt.title("Daily Log Trends - No Data (Invalid Timestamps)")
+                plt.xlabel("Date")
+                plt.ylabel("Number of Logs")
+                plot_daily = io.BytesIO()
+                plt.savefig(plot_daily, format="png", bbox_inches="tight")
+                plt.close()
+                plot_daily.seek(0)
+            else:
+                daily_logs = filtered_df.groupby(filtered_df['Timestamp'].dt.date).size()
+                if daily_logs.empty:
+                    error_msg += "Warning: No data for Daily Log Trends.\n"
+                    plt.figure(figsize=(8, 4))
+                    plt.title("Daily Log Trends - No Data")
+                    plt.xlabel("Date")
+                    plt.ylabel("Number of Logs")
+                    plot_daily = io.BytesIO()
+                    plt.savefig(plot_daily, format="png", bbox_inches="tight")
+                    plt.close()
+                    plot_daily.seek(0)
+                else:
+                    plt.figure(figsize=(8, 4))
+                    daily_logs.plot(kind='line', marker='o', color='blue')
+                    plt.title("Daily Log Trends")
+                    plt.xlabel("Date")
+                    plt.ylabel("Number of Logs")
+                    plt.xticks(rotation=45)
+                    plot_daily = io.BytesIO()
+                    plt.savefig(plot_daily, format="png", bbox_inches="tight")
+                    plt.close()
+                    plot_daily.seek(0)
         except Exception as e:
-            error_msg += f"Error parsing date range: {str(e)}\n"
-    
-    if filtered_df.empty:
-        return None, None, None, None, f"{error_msg}No data matches the selected filters."
-    
-    # Debug: Log the filtered DataFrame
-    error_msg += f"Filtered DataFrame:\n{filtered_df.to_string()}\n"
-    
-    # Device Cards (as a table)
-    device_cards = filtered_df[['DeviceID', 'Lab', 'Type', 'UsageCount', 'Timestamp']].sort_values(by='Timestamp', ascending=False)
-    
-    # Daily Log Trends (Line Chart)
-    try:
-        if df['Timestamp'].isna().all():
-            error_msg += "Warning: All timestamps are invalid. Skipping Daily Log Trends.\n"
+            error_msg += f"Error generating Daily Log Trends: {str(e)}\n"
             plt.figure(figsize=(8, 4))
-            plt.title("Daily Log Trends - No Data (Invalid Timestamps)")
+            plt.title("Daily Log Trends - Error")
             plt.xlabel("Date")
             plt.ylabel("Number of Logs")
-            buf1 = io.BytesIO()
-            plt.savefig(buf1, format="png", bbox_inches="tight")
+            plot_daily = io.BytesIO()
+            plt.savefig(plot_daily, format="png", bbox_inches="tight")
             plt.close()
-            buf1.seek(0)
-        else:
-            daily_logs = filtered_df.groupby(filtered_df['Timestamp'].dt.date).size()
-            if daily_logs.empty:
-                error_msg += "Warning: No data for Daily Log Trends.\n"
+            plot_daily.seek(0)
+        
+        # Weekly Uptime % (Bar Chart)
+        try:
+            if df['Timestamp'].isna().all():
+                error_msg += "Warning: All timestamps are invalid. Skipping Weekly Uptime.\n"
                 plt.figure(figsize=(8, 4))
-                plt.title("Daily Log Trends - No Data")
+                plt.title("Weekly Uptime % - No Data (Invalid Timestamps)")
                 plt.xlabel("Date")
-                plt.ylabel("Number of Logs")
-                buf1 = io.BytesIO()
-                plt.savefig(buf1, format="png", bbox_inches="tight")
+                plt.ylabel("Uptime %")
+                plot_uptime = io.BytesIO()
+                plt.savefig(plot_uptime, format="png", bbox_inches="tight")
                 plt.close()
-                buf1.seek(0)
+                plot_uptime.seek(0)
             else:
-                plt.figure(figsize=(8, 4))
-                daily_logs.plot(kind='line', marker='o', color='blue')
-                plt.title("Daily Log Trends")
-                plt.xlabel("Date")
-                plt.ylabel("Number of Logs")
-                plt.xticks(rotation=45)
-                buf1 = io.BytesIO()
-                plt.savefig(buf1, format="png", bbox_inches="tight")
-                plt.close()
-                buf1.seek(0)
-    except Exception as e:
-        error_msg += f"Error generating Daily Log Trends: {str(e)}\n"
-        plt.figure(figsize=(8, 4))
-        plt.title("Daily Log Trends - Error")
-        plt.xlabel("Date")
-        plt.ylabel("Number of Logs")
-        buf1 = io.BytesIO()
-        plt.savefig(buf1, format="png", bbox_inches="tight")
-        plt.close()
-        buf1.seek(0)
-    
-    # Weekly Uptime % (Bar Chart)
-    try:
-        if df['Timestamp'].isna().all():
-            error_msg += "Warning: All timestamps are invalid. Skipping Weekly Uptime.\n"
+                end_date = filtered_df['Timestamp'].max()
+                start_date = end_date - timedelta(days=7)
+                weekly_df = filtered_df[(filtered_df['Timestamp'] >= start_date) & (filtered_df['Timestamp'] <= end_date)]
+                if weekly_df.empty:
+                    error_msg += "Warning: No data for Weekly Uptime % (date range too narrow).\n"
+                    plt.figure(figsize=(8, 4))
+                    plt.title("Weekly Uptime % - No Data")
+                    plt.xlabel("Date")
+                    plt.ylabel("Uptime %")
+                    plot_uptime = io.BytesIO()
+                    plt.savefig(plot_uptime, format="png", bbox_inches="tight")
+                    plt.close()
+                    plot_uptime.seek(0)
+                else:
+                    uptime = weekly_df.groupby(weekly_df['Timestamp'].dt.date)['Status'].apply(lambda x: (x == 'Up').mean() * 100)
+                    plt.figure(figsize=(8, 4))
+                    uptime.plot(kind='bar', color='green')
+                    plt.title("Weekly Uptime %")
+                    plt.xlabel("Date")
+                    plt.ylabel("Uptime %")
+                    plt.xticks(rotation=45)
+                    plot_uptime = io.BytesIO()
+                    plt.savefig(plot_uptime, format="png", bbox_inches="tight")
+                    plt.close()
+                    plot_uptime.seek(0)
+        except Exception as e:
+            error_msg += f"Error generating Weekly Uptime %: {str(e)}\n"
             plt.figure(figsize=(8, 4))
-            plt.title("Weekly Uptime % - No Data (Invalid Timestamps)")
+            plt.title("Weekly Uptime % - Error")
             plt.xlabel("Date")
             plt.ylabel("Uptime %")
-            buf2 = io.BytesIO()
-            plt.savefig(buf2, format="png", bbox_inches="tight")
+            plot_uptime = io.BytesIO()
+            plt.savefig(plot_uptime, format="png", bbox_inches="tight")
             plt.close()
-            buf2.seek(0)
-        else:
-            end_date = filtered_df['Timestamp'].max()
-            start_date = end_date - timedelta(days=7)
-            weekly_df = filtered_df[(filtered_df['Timestamp'] >= start_date) & (filtered_df['Timestamp'] <= end_date)]
-            if weekly_df.empty:
-                error_msg += "Warning: No data for Weekly Uptime % (date range too narrow).\n"
-                plt.figure(figsize=(8, 4))
-                plt.title("Weekly Uptime % - No Data")
-                plt.xlabel("Date")
-                plt.ylabel("Uptime %")
-                buf2 = io.BytesIO()
-                plt.savefig(buf2, format="png", bbox_inches="tight")
-                plt.close()
-                buf2.seek(0)
+            plot_uptime.seek(0)
+        
+        # Anomaly Alerts (Text)
+        try:
+            anomalies = filtered_df[(filtered_df['UsageCount'] > 80) | (filtered_df['Status'] == 'Down')]
+            if anomalies.empty:
+                anomaly_text = "No anomalies detected."
             else:
-                uptime = weekly_df.groupby(weekly_df['Timestamp'].dt.date)['Status'].apply(lambda x: (x == 'Up').mean() * 100)
-                plt.figure(figsize=(8, 4))
-                uptime.plot(kind='bar', color='green')
-                plt.title("Weekly Uptime %")
-                plt.xlabel("Date")
-                plt.ylabel("Uptime %")
-                plt.xticks(rotation=45)
-                buf2 = io.BytesIO()
-                plt.savefig(buf2, format="png", bbox_inches="tight")
-                plt.close()
-                buf2.seek(0)
+                anomaly_text = "Anomalies Detected:\n" + anomalies[['DeviceID', 'Lab', 'Type', 'Status', 'UsageCount']].to_string(index=False)
+        except Exception as e:
+            error_msg += f"Error generating Anomaly Alerts: {str(e)}\n"
+            anomaly_text = "Error generating anomaly alerts."
+        
+        return device_cards, plot_daily, plot_uptime, anomaly_text, f"{error_msg}Filters applied successfully."
     except Exception as e:
-        error_msg += f"Error generating Weekly Uptime %: {str(e)}\n"
+        error_msg += f"Unexpected error in filter_and_visualize: {str(e)}\n"
+        plt.figure(figsize=(8, 4))
+        plt.title("Daily Log Trends - Error")
+        plt.xlabel("Date")
+        plt.ylabel("Number of Logs")
+        plot_daily = io.BytesIO()
+        plt.savefig(plot_daily, format="png", bbox_inches="tight")
+        plt.close()
+        plot_daily.seek(0)
+        
         plt.figure(figsize=(8, 4))
         plt.title("Weekly Uptime % - Error")
         plt.xlabel("Date")
         plt.ylabel("Uptime %")
-        buf2 = io.BytesIO()
-        plt.savefig(buf2, format="png", bbox_inches="tight")
+        plot_uptime = io.BytesIO()
+        plt.savefig(plot_uptime, format="png", bbox_inches="tight")
         plt.close()
-        buf2.seek(0)
-    
-    # Anomaly Alerts (Text)
-    try:
-        anomalies = filtered_df[(filtered_df['UsageCount'] > 80) | (filtered_df['Status'] == 'Down')]
-        if anomalies.empty:
-            anomaly_text = "No anomalies detected."
-        else:
-            anomaly_text = "Anomalies Detected:\n" + anomalies[['DeviceID', 'Lab', 'Type', 'Status', 'UsageCount']].to_string(index=False)
-    except Exception as e:
-        error_msg += f"Error generating Anomaly Alerts: {str(e)}\n"
-        anomaly_text = "Error generating anomaly alerts."
-    
-    return device_cards, buf1, buf2, anomaly_text, f"{error_msg}Filters applied successfully."
+        plot_uptime.seek(0)
+        
+        return None, plot_daily, plot_uptime, "Error generating anomaly alerts.", error_msg
 
 def download_pdf(selected_lab, selected_type, selected_date_range):
     global df
-    if df.empty:
-        return None
-    
-    filtered_df = df.copy()
-    if selected_lab != "All":
-        filtered_df = filtered_df[filtered_df["Lab"] == selected_lab]
-    if selected_type != "All":
-        filtered_df = filtered_df[filtered_df["Type"] == selected_type]
-    if selected_date_range != "All" and selected_date_range != "No data available." and not df['Timestamp'].isna().all():
-        start_date, end_date = selected_date_range.split(" to ")
-        start_date = pd.to_datetime(start_date)
-        end_date = pd.to_datetime(end_date) + timedelta(days=1)
-        filtered_df = filtered_df[(filtered_df["Timestamp"] >= start_date) & (filtered_df["Timestamp"] < end_date)]
-    
-    if filtered_df.empty:
+    try:
+        if df.empty:
+            return None
+        
+        filtered_df = df.copy()
+        if selected_lab != "All":
+            filtered_df = filtered_df[filtered_df["Lab"] == selected_lab]
+        if selected_type != "All":
+            filtered_df = filtered_df[filtered_df["Type"] == selected_type]
+        if selected_date_range != "All" and selected_date_range != "No data available." and not df['Timestamp'].isna().all():
+            start_date, end_date = selected_date_range.split(" to ")
+            start_date = pd.to_datetime(start_date)
+            end_date = pd.to_datetime(end_date) + timedelta(days=1)
+            filtered_df = filtered_df[(filtered_df["Timestamp"] >= start_date) & (filtered_df["Timestamp"] < end_date)]
+        
+        if filtered_df.empty:
+            return None
+        
+        pdf = FPDF()
+        pdf.add_page()
+        pdf.set_font("Arial", size=12)
+        pdf.cell(200, 10, txt="LabOps Dashboard Report", ln=True, align='C')
+        pdf.ln(10)
+        
+        for index, row in filtered_df.iterrows():
+            line = f"{row['Timestamp']} | {row['DeviceID']} | {row['Lab']} | {row['Type']} | {row['Status']} | {row['UsageCount']}"
+            pdf.multi_cell(0, 10, txt=line)
+        
+        output = io.BytesIO()
+        pdf.output(output)
+        output.seek(0)
+        return output
+    except Exception as e:
+        print(f"Error in download_pdf: {str(e)}")
         return None
-    
-    pdf = FPDF()
-    pdf.add_page()
-    pdf.set_font("Arial", size=12)
-    pdf.cell(200, 10, txt="LabOps Dashboard Report", ln=True, align='C')
-    pdf.ln(10)
-    
-    for index, row in filtered_df.iterrows():
-        line = f"{row['Timestamp']} | {row['DeviceID']} | {row['Lab']} | {row['Type']} | {row['Status']} | {row['UsageCount']}"
-        pdf.multi_cell(0, 10, txt=line)
-    
-    output = io.BytesIO()
-    pdf.output(output)
-    output.seek(0)
-    return output
 
 # Build the Gradio interface
 with gr.Blocks() as demo: