Update app.py

app.py

@@ -12,13 +12,13 @@ def upload_csv(file):
     global df
     try:
         if file is None:
-            return ["All"], ["All"], ["All"], "No file uploaded. Please upload a CSV file.", "All", "All", "All"
+            return ["All"], ["All"], ["All"], "No file uploaded. Please upload a CSV file.", "All", "All", "All", None, None, None, None
         
         # Read the CSV file
         df = pd.read_csv(file)
         
         if df.empty:
-            return ["All"], ["All"], ["All"], "The uploaded CSV file is empty.", "All", "All", "All"
+            return ["All"], ["All"], ["All"], "The uploaded CSV file is empty.", "All", "All", "All", None, None, None, None
         
         # Debug: Show the CSV content and column names
         csv_info = f"CSV Columns: {', '.join(df.columns)}\nRaw CSV Content:\n{df.to_string()}"
@@ -27,19 +27,19 @@ def upload_csv(file):
         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"{csv_info}\n\nError: CSV is missing required columns: {', '.join(missing_cols)}", "All", "All", "All"
+            return ["All"], ["All"], ["All"], f"{csv_info}\n\nError: CSV is missing required columns: {', '.join(missing_cols)}", "All", "All", "All", None, None, None, None
         
         # 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"
+            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
         
         # Convert Timestamp to datetime with error handling
         try:
             df['Timestamp'] = pd.to_datetime(df['Timestamp'], errors='coerce')
             if df['Timestamp'].isna().all():
-                return ["All"], ["All"], ["All"], f"{csv_info}\n\nError: All Timestamp values are invalid or unparseable.", "All", "All", "All"
+                return ["All"], ["All"], ["All"], f"{csv_info}\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"
+            return ["All"], ["All"], ["All"], f"{csv_info}\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()])
@@ -50,15 +50,21 @@ def upload_csv(file):
         max_date = df['Timestamp'].max().strftime('%Y-%m-%d')
         date_ranges = ['All', f"{min_date} to {max_date}"]
         
-        return labs, types, date_ranges, "CSV loaded successfully.", "All", "All", "All"
+        # Automatically trigger filter_and_visualize after upload with default filters
+        device_cards, plot_daily, plot_uptime, anomaly_text = filter_and_visualize("All", "All", "All")[:4]
+        
+        return labs, types, date_ranges, "CSV loaded successfully.", "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"
+        return ["All"], ["All"], ["All"], f"Failed to load CSV: {str(e)}", "All", "All", "All", None, None, None, None
 
 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}"
+    
     # Filter the DataFrame
     filtered_df = df.copy()
     if selected_lab != "All":
@@ -72,7 +78,10 @@ def filter_and_visualize(selected_lab, selected_type, selected_date_range):
         filtered_df = filtered_df[(filtered_df["Timestamp"] >= start_date) & (filtered_df["Timestamp"] < end_date)]
     
     if filtered_df.empty:
-        return None, None, None, None, "No data matches the selected filters."
+        return None, None, None, None, f"{error_msg}\nNo data matches the selected filters."
+    
+    # Debug: Log the filtered DataFrame
+    error_msg += f"\nFiltered DataFrame:\n{filtered_df.to_string()}"
     
     # Device Cards (as a table)
     device_cards = filtered_df[['DeviceID', 'Lab', 'Type', 'UsageCount', 'Timestamp']].sort_values(by='Timestamp', ascending=False)
@@ -113,7 +122,7 @@ def filter_and_visualize(selected_lab, selected_type, selected_date_range):
     else:
         anomaly_text = "Anomalies Detected:\n" + anomalies[['DeviceID', 'Lab', 'Type', 'Status', 'UsageCount']].to_string(index=False)
     
-    return device_cards, buf1, buf2, anomaly_text, "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
@@ -162,7 +171,7 @@ with gr.Blocks() as demo:
         date_dropdown = gr.Dropdown(label="Filter by Date Range", choices=["All"], value="All")
 
     with gr.Row():
-        submit_btn = gr.Button("Submit")  # Added Submit button
+        submit_btn = gr.Button("Submit Filters")  # Renamed for clarity
 
     with gr.Row():
         device_table = gr.DataFrame(label="Device Cards (Usage, Last Log)")
@@ -170,17 +179,19 @@ with gr.Blocks() as demo:
         plot_uptime = gr.Image(label="Weekly Uptime %")
 
     anomaly_output = gr.Textbox(label="Anomaly Alerts")
-    download_btn = gr.Button("Download PDF Report")
+    
+    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]
+        outputs=[lab_dropdown, type_dropdown, date_dropdown, error_box, lab_dropdown, type_dropdown, date_dropdown, device_table, plot_daily, plot_uptime, anomaly_output]
     )
     
-    # Bind filter_and_visualize to the Submit button
     submit_btn.click(
         fn=filter_and_visualize,
         inputs=[lab_dropdown, type_dropdown, date_dropdown],