Update app.py

app.py

@@ -47,34 +47,39 @@ def upload_csv(file):
         try:
             df['Timestamp'] = pd.to_datetime(df['Timestamp'], errors='coerce')
             debug_msg += f"Timestamps after conversion:\n{df['Timestamp'].to_string()}\n\n"
-            if df['Timestamp'].isna().all():
-                return ["All"], ["All"], ["All"], f"{debug_msg}Error: All Timestamp values are invalid or unparseable.", "All", "All", "All", None, None, None, None
+            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"
         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
         
         # Extract unique values for dropdowns
         debug_msg += "Extracting unique values for dropdowns...\n"
-        labs = ['All'] + sorted([str(lab) for lab in df['Lab'].fillna('Unknown').unique()])
-        types = ['All'] + sorted([str(type_) for type_ in df['Type'].fillna('Unknown').unique()])
-        debug_msg += f"Lab options: {labs}\nType options: {types}\n\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"
         
         # Extract date range for filter
-        min_date = df['Timestamp'].min()
-        max_date = df['Timestamp'].max()
-        if pd.isna(min_date) or pd.isna(max_date):
+        if timestamps_invalid:
             date_ranges = ['All']
-            debug_msg += "Warning: Could not determine date range due to invalid timestamps.\n"
+            debug_msg += "Date range dropdown disabled due to invalid timestamps.\n"
         else:
-            min_date = min_date.strftime('%Y-%m-%d')
-            max_date = max_date.strftime('%Y-%m-%d')
-            date_ranges = ['All', f"{min_date} to {max_date}"]
-            debug_msg += f"Date Range: {min_date} to {max_date}\n"
+            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"
+            else:
+                min_date = min_date.strftime('%Y-%m-%d')
+                max_date = max_date.strftime('%Y-%m-%d')
+                date_ranges = ['All', f"{min_date_str} to {max_date_str}"]
+                debug_msg += f"Date Range: {min_date} to {max_date}\n"
         
-        # Automatically trigger filter_and_visualize after upload with default filters
+        # Automatically trigger filter_and_display after CSV 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
+            result = filter_and_display("All", "All", "All")
+            device_cards, plot_daily, plot_uptime_buf, anomaly_text, filter_msg = result
             debug_msg += f"Initial Filter Result: {filter_msg}\n"
         except Exception as e:
             debug_msg += f"Initial Filter Error: {str(e)}\n"
@@ -82,9 +87,9 @@ def upload_csv(file):
         
         return labs, types, date_ranges, debug_msg, "All", "All", "All", device_cards, plot_daily, plot_uptime, anomaly_text
     except Exception as e:
-        return ["All"], ["All"], ["All"], f"{debug_msg}Failed to load CSV: {str(e)}", "All", "All", "All", None, None, None, None
+        return ["All"], ["All"], ["All"], f"{debug_msg}Failed to process CSV: {str(e)}", "All", "All", "All", None, None, None, None"
 
-def filter_and_visualize(selected_lab, selected_type, selected_date_range):
+def filter_and_display(selected_lab, selected_type, selected_date_range):
     global df
     if df.empty:
         return None, None, None, None, "No data available."
@@ -97,38 +102,36 @@ def filter_and_visualize(selected_lab, selected_type, selected_date_range):
     error_msg += f"Initial DataFrame: {len(filtered_df)} rows\n"
     
     if selected_lab != "All":
-        filtered_df = filtered_df[filtered_df["Lab"] == selected_lab]
+        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.":
+    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)]
+            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"
-            return None, None, None, None, error_msg
     
     if filtered_df.empty:
-        return None, None, None, None, f"{error_msg}No data matches the selected filters."
+        return None, None, None, None, f"{error_msg}No data matches the selected filters.\n"
     
     # Debug: Log the filtered DataFrame
-    error_msg += f"Filtered DataFrame:\n{filtered_df.to_string()}\n"
+    error_msg += f"Filtered DataFrame:\n{filtered_df.to_string()}\n\n"
     
     # Device Cards (as a table)
-    device_cards = filtered_df[['DeviceID', 'Lab', 'Type', 'UsageCount', 'Timestamp']].sort_values(by='Timestamp', ascending=False)
+    device_cards = filtered_df[['DeviceID', 'Lab', 'Type', 'UsageCount', 'Timestamp']].sort_values(by='Timestamp')
     
-    # Daily Log Trends (Line Chart)
+    # Daily Log Trends
     try:
-        daily_logs = filtered_df.groupby(filtered_df['Timestamp'].dt.date).size()
-        if daily_logs.empty:
-            error_msg += "Warning: No data for Daily Log Trends (invalid timestamps).\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")
+            plt.title("Daily Log Trends - No Data (Invalid Timestamps)")
             plt.xlabel("Date")
             plt.ylabel("Number of Logs")
             buf1 = io.BytesIO()
@@ -136,16 +139,28 @@ def filter_and_visualize(selected_lab, selected_type, selected_date_range):
             plt.close()
             buf1.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)
+            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")
+                buf1 = io.BytesIO()
+                plt.savefig(buf1, format="png", bbox_inches="tight")
+                plt.close()
+                buf1.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))
@@ -159,13 +174,10 @@ def filter_and_visualize(selected_lab, selected_type, selected_date_range):
     
     # Weekly Uptime % (Bar Chart)
     try:
-        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"
+        if df['Timestamp'].isna().all():
+            error_msg += "Warning: All timestamps are invalid. Skipping Weekly Uptime.\n"
             plt.figure(figsize=(8, 4))
-            plt.title("Weekly Uptime % - No Data")
+            plt.title("Weekly Uptime % - No Data (Invalid Timestamps)")
             plt.xlabel("Date")
             plt.ylabel("Uptime %")
             buf2 = io.BytesIO()
@@ -173,17 +185,31 @@ def filter_and_visualize(selected_lab, selected_type, selected_date_range):
             plt.close()
             buf2.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)
-            buf2 = io.BytesIO()
-            plt.savefig(buf2, format="png", bbox_inches="tight")
-            plt.close()
-            buf2.seek(0)
+            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)
+            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)
     except Exception as e:
         error_msg += f"Error generating Weekly Uptime %: {str(e)}\n"
         plt.figure(figsize=(8, 4))
@@ -206,7 +232,7 @@ def filter_and_visualize(selected_lab, selected_type, selected_date_range):
         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."
+    return device_cards, buf1, buf2, anomaly_text, f"{error_msg}\nFilters applied successfully."
 
 def download_pdf(selected_lab, selected_type, selected_date_range):
     global df
@@ -218,7 +244,7 @@ def download_pdf(selected_lab, selected_type, selected_date_range):
         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.":
+    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)
@@ -258,7 +284,7 @@ with gr.Blocks() as demo:
         submit_btn = gr.Button("Submit Filters")
 
     with gr.Row():
-        device_table = gr.DataFrame(label="Device Cards (Usage, Last Log)")
+        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 %")
 
@@ -273,13 +299,13 @@ with gr.Blocks() as demo:
     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_table, plot_daily, plot_uptime, anomaly_output]
+        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_table, plot_daily, plot_uptime, anomaly_output, error_box]
+        outputs=[device_cards, plot_daily, plot_uptime, anomaly_output, error_box],
     )
     
     download_btn.click(