updated chart midnight issue

weekly.py

@@ -2,6 +2,7 @@ import pandas as pd
 import streamlit as st
 import plotly.graph_objects as go
 from pre import preprocess_uploaded_file
+from datetime import datetime
 
 def generate_weekly_report(uploaded_files):
     if not uploaded_files:
@@ -11,6 +12,21 @@ def generate_weekly_report(uploaded_files):
     combined_data = pd.DataFrame()
     for uploaded_file in uploaded_files:
         data = preprocess_uploaded_file(uploaded_file)
+        # Extract date and time from filename
+        filename_parts = uploaded_file.name.split('_')
+        if len(filename_parts) >= 4:
+            file_datetime_str = f"{filename_parts[-2]}_{filename_parts[-1].split('.')[0]}"
+            try:
+                file_datetime = datetime.strptime(file_datetime_str, '%Y%m%d_%H%M%S')
+                file_date = file_datetime.date()
+            except ValueError:
+                st.error(f"Invalid date format in filename: {uploaded_file.name}")
+                return
+        else:
+            st.error(f"Filename does not contain expected date format: {uploaded_file.name}")
+            return
+        
+        data['File Date'] = file_date
         combined_data = pd.concat([combined_data, data], ignore_index=True)
 
     if combined_data.empty:
@@ -23,7 +39,8 @@ def generate_weekly_report(uploaded_files):
         st.warning("No failed scenarios found in the uploaded data.")
         return
 
-    failed_data['Date'] = pd.to_datetime(failed_data['Start datetime']).dt.date
+    # Use 'File Date' for grouping
+    failed_data['Date'] = failed_data['File Date']
 
     # UI for selecting environments and functional areas
     environments = combined_data['Environment'].unique()
@@ -31,7 +48,7 @@ def generate_weekly_report(uploaded_files):
 
     all_functional_areas = failed_data['Functional area'].unique()
     area_choice = st.radio("Choose Functional Areas to Display", ['All', 'Select Functional Areas'])
-    
+
     if area_choice == 'Select Functional Areas':
         selected_functional_areas = st.multiselect("Select Functional Areas", options=all_functional_areas)
         if not selected_functional_areas:
@@ -61,6 +78,10 @@ def generate_weekly_report(uploaded_files):
     # Group by Date, Environment, and Functional area
     daily_failures = filtered_data.groupby(['Date', 'Environment', 'Functional area']).size().unstack(level=[1, 2], fill_value=0)
 
+    # Ensure we have a continuous date range
+    date_range = pd.date_range(start=start_date, end=end_date)
+    daily_failures = daily_failures.reindex(date_range, fill_value=0)
+
     # Y-axis scaling option
     y_axis_scale = st.radio("Y-axis Scaling", ["Fixed", "Dynamic"])
 
@@ -98,16 +119,16 @@ def generate_weekly_report(uploaded_files):
 
     # Add interactivity for scenario details
     st.write("Select a date and environment to see detailed scenario information:")
-    
+
     selected_date = st.date_input("Select a date", min_value=start_date, max_value=end_date, value=start_date)
     selected_env = st.selectbox("Select an environment", options=selected_environments)
 
     if selected_date and selected_env:
         st.write(f"### Detailed Scenarios for {selected_date} - {selected_env}")
-        
+
         day_scenarios = filtered_data[(filtered_data['Date'] == selected_date) & 
                                       (filtered_data['Environment'] == selected_env)]
-        
+
         if not day_scenarios.empty:
             st.dataframe(day_scenarios[['Functional area', 'Scenario name', 'Error message', 'Time spent(m:s)']])
         else:
@@ -118,7 +139,7 @@ def generate_weekly_report(uploaded_files):
     for env in selected_environments:
         env_data = filtered_data[filtered_data['Environment'] == env]
         total_failures = len(env_data)
-        
+
         if len(daily_failures) > 0:
             avg_daily_failures = total_failures / len(daily_failures)
             if env in daily_failures.columns.levels[0]: