Update app.py

app.py

@@ -29,17 +29,21 @@ def upload_csv(file):
             missing_cols = required_columns - set(df.columns)
             return ["All"], ["All"], ["All"], f"{csv_info}\n\nError: 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"{csv_info}\n\nData Types:\n{df.dtypes}\n\nSample Values:\n{df.head().to_string()}"
+        
         # Check for empty or all-NaN columns
         if df['Lab'].dropna().empty or df['Type'].dropna().empty:
-            return ["All"], ["All"], ["All"], f"{csv_info}\n\nError: Lab or Type columns are empty or contain only NaN values.", "All", "All", "All", None, None, None, None
+            return ["All"], ["All"], ["All"], f"{debug_msg}\n\nError: Lab or Type columns are empty or contain only NaN values.", "All", "All", "All", None, None, None, None
         
         # Convert Timestamp to datetime with error handling
         try:
             df['Timestamp'] = pd.to_datetime(df['Timestamp'], errors='coerce')
+            debug_msg += f"\n\nTimestamps after conversion:\n{df['Timestamp'].head().to_string()}"
             if df['Timestamp'].isna().all():
-                return ["All"], ["All"], ["All"], f"{csv_info}\n\nError: All Timestamp values are invalid or unparseable.", "All", "All", "All", None, None, None, None
+                return ["All"], ["All"], ["All"], f"{debug_msg}\n\nError: All Timestamp values are invalid or unparseable.", "All", "All", "All", None, None, None, None
         except Exception as e:
-            return ["All"], ["All"], ["All"], f"{csv_info}\n\nError: Failed to parse Timestamp column: {str(e)}", "All", "All", "All", None, None, None, None
+            return ["All"], ["All"], ["All"], f"{debug_msg}\n\nError: Failed to parse Timestamp column: {str(e)}", "All", "All", "All", None, None, None, None
         
         # Extract unique values for dropdowns
         labs = ['All'] + sorted([str(lab) for lab in df['Lab'].fillna('Unknown').unique()])
@@ -50,23 +54,23 @@ def upload_csv(file):
         max_date = df['Timestamp'].max()
         if pd.isna(min_date) or pd.isna(max_date):
             date_ranges = ['All']
-            error_msg = f"{csv_info}\n\nWarning: Could not determine date range due to invalid timestamps."
+            debug_msg += "\n\nWarning: Could not determine date range due to invalid timestamps."
         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}"]
-            error_msg = "CSV loaded successfully."
+            debug_msg += f"\n\nDate Range: {min_date} to {max_date}"
         
         # Automatically trigger filter_and_visualize after upload with default filters
         try:
             result = filter_and_visualize("All", "All", "All")
             device_cards, plot_daily, plot_uptime, anomaly_text, filter_msg = result
-            error_msg += f"\nInitial Filter Result: {filter_msg}"
+            debug_msg += f"\n\nInitial Filter Result: {filter_msg}"
         except Exception as e:
-            error_msg += f"\nInitial Filter Error: {str(e)}"
+            debug_msg += f"\n\nInitial Filter Error: {str(e)}"
             device_cards, plot_daily, plot_uptime, anomaly_text = None, None, None, None
         
-        return labs, types, date_ranges, error_msg, "All", "All", "All", device_cards, plot_daily, plot_uptime, anomaly_text
+        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"Failed to load CSV: {str(e)}", "All", "All", "All", None, None, None, None
 
@@ -82,14 +86,17 @@ def filter_and_visualize(selected_lab, selected_type, selected_date_range):
     filtered_df = df.copy()
     if selected_lab != "All":
         filtered_df = filtered_df[filtered_df["Lab"] == selected_lab]
+        error_msg += f"\nAfter Lab filter ({selected_lab}): {len(filtered_df)} rows"
     if selected_type != "All":
         filtered_df = filtered_df[filtered_df["Type"] == selected_type]
+        error_msg += f"\nAfter Type filter ({selected_type}): {len(filtered_df)} rows"
     if selected_date_range != "All" and selected_date_range != "No data available.":
         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"\nAfter Date Range filter ({start_date} to {end_date}): {len(filtered_df)} rows"
         except Exception as e:
             error_msg += f"\nError parsing date range: {str(e)}"
             return None, None, None, None, error_msg
@@ -120,17 +127,28 @@ def filter_and_visualize(selected_lab, selected_type, selected_date_range):
     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)]
-    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)
+    if weekly_df.empty:
+        error_msg += "\nWarning: No data for Weekly Uptime % (date range too narrow)."
+        buf2 = io.BytesIO()
+        plt.figure(figsize=(8, 4))
+        plt.title("Weekly Uptime % - No Data")
+        plt.xlabel("Date")
+        plt.ylabel("Uptime %")
+        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)
     
     # Anomaly Alerts (Text)
     anomalies = filtered_df[(filtered_df['UsageCount'] > 80) | (filtered_df['Status'] == 'Down')]