295 lines
12 KiB
Python
295 lines
12 KiB
Python
import streamlit as st
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from streamlit_calendar import calendar
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import pandas as pd
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import database
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import datetime
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# --- Configuration ---
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st.set_page_config(page_title="Work Tracker", layout="wide")
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# --- Database Init ---
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if "db_initialized" not in st.session_state:
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database.init_db()
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st.session_state["db_initialized"] = True
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# --- UI Layout ---
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st.title("📅 Work Hours Tracker")
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col1, col2 = st.columns([2, 1])
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# --- Sidebar Controls ---
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with st.sidebar:
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st.header("Settings")
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# Global Config for Stats
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DAILY_HOURS = st.slider("Work Hours per Day (for Stats)", min_value=6.0, max_value=8.0, value=8.0, step=0.5)
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st.divider()
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mode = st.radio("Mode", ["Mark Work Day", "Mark Holiday", "Remove Day"])
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course_name = ""
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if mode == "Mark Work Day":
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course_name = st.text_input("Course Name", placeholder="e.g. Python 101")
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st.info(f"Current Mode: **{mode}**")
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if mode == "Mark Work Day":
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if course_name:
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st.caption(f"Course: **{course_name}**")
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# --- Calendar Section ---
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with col1:
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st.subheader("Select Days")
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# Fetch existing data to populate calendar
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df = database.get_all_days()
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# Format events for streamlit-calendar
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events = []
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if not df.empty:
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for _, row in df.iterrows():
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date_str = row['date']
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day_type = row.get('type', 'work')
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c_name = row.get('course_name', '')
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# Get hours for tooltip/display if needed, though simple title is usually best for calendar
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title = "Worked"
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color = "#4CAF50" # Green
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if day_type == 'holiday':
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title = "Holiday"
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color = "#FF9800" # Orange
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elif c_name:
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title = c_name
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if row.get('travel_costs'):
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title = f"🚗 {title}"
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events.append({
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"title": title,
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"start": date_str,
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"allDay": True,
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"color": color,
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# Store extra props if needed
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"extendedProps": {
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"type": day_type,
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"course": c_name,
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"hours": row.get('hours', 8.0),
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"travel_costs": bool(row.get('travel_costs'))
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}
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})
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# Calendar options
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calendar_options = {
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"headerToolbar": {
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"left": "today prev,next",
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"center": "title",
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"right": "dayGridMonth,dayGridWeek"
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},
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"initialView": "dayGridMonth",
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"selectable": True,
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"timeZone": "UTC", # Force UTC to prevent off-by-one errors from local timezone
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}
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# Render Calendar
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cal = calendar(events=events, options=calendar_options, key="calendar")
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# Handle Click Events
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# 1. Date Click (Empty cell)
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if cal.get("callback") == "dateClick":
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# Check if we already processed this specific event to avoid infinite loops/double toggles
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# The component might return the same event object on rerun until interaction changes
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current_event_str = str(cal)
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if st.session_state.get("last_processed_event") != current_event_str:
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st.session_state["last_processed_event"] = current_event_str
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# Parse date from ISO string (e.g. "2023-01-01T00:00:00.000Z")
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if "date" in cal["dateClick"]:
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clicked_date = cal["dateClick"]["date"]
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if "T" in clicked_date:
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clicked_date = clicked_date.split("T")[0]
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# Logic based on Mode
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if mode == "Remove Day":
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database.remove_day(clicked_date)
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elif mode == "Mark Holiday":
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# Holidays are standard 8h for now, or could use the slider too if user wanted "half holiday"
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database.update_day(clicked_date, day_type="holiday", course_name="Holiday", hours=DAILY_HOURS)
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else: # Mark Work Day
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database.update_day(clicked_date, day_type="work", course_name=course_name, hours=DAILY_HOURS, travel_costs=True)
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st.rerun() # Refresh to update calendar and stats
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# 2. Event Click (Existing item)
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elif cal.get("callback") == "eventClick":
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current_event_str = str(cal)
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if st.session_state.get("last_processed_event") != current_event_str:
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st.session_state["last_processed_event"] = current_event_str
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event_data = cal["eventClick"]["event"]
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extended_props = event_data.get("extendedProps", {})
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# Only toggle travel costs for "work" days
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if extended_props.get("type") == "work":
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clicked_date = event_data["start"]
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if "T" in clicked_date:
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clicked_date = clicked_date.split("T")[0]
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# Invert current travel cost status
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current_travel_costs = extended_props.get("travel_costs", False)
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new_travel_costs = not current_travel_costs
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# Update database
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database.update_day(
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clicked_date,
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day_type="work",
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course_name=extended_props.get("course"),
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hours=extended_props.get("hours", DAILY_HOURS),
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travel_costs=new_travel_costs
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)
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st.rerun()
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# --- Statistics Section ---
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with col2:
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st.subheader("Statistics")
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# Configuration: Start Date
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START_DATE = pd.Timestamp("2025-09-15")
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# Convert to datetime BEFORE filtering
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df['date'] = pd.to_datetime(df['date'])
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# Filter for data >= Start Date
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df = df[df['date'] >= START_DATE]
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df = df.sort_values('date')
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# Determine "effective" days and hours
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# User requested Holidays count as full days for simplicity
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def get_effective_days(row):
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return 1.0
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def get_effective_hours(row):
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# Use the global setting, ignoring DB value if necessary
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# (Or could prioritize DB if users want override? User asked to recalculate based on entered days)
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# "I don't want it to change the actual hours worked. I just want to... recalculate the averages"
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# This implies ignoring stored hour values and reapplying the global constant.
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return DAILY_HOURS
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# Ensure 'type' exists
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if 'type' not in df.columns:
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df['type'] = 'work'
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df['effective_days'] = df.apply(get_effective_days, axis=1)
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df['effective_hours'] = df.apply(get_effective_hours, axis=1)
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# --- Calculate Weekly Stats ---
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# Resample to weekly frequency (W-MON)
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weekly_data = df.resample('W-MON', on='date')[['effective_days', 'effective_hours']].sum()
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# Reindex to ensure we cover from START_DATE to Today (or Max Date)
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# This ensures weeks with 0 work are counted in the average
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max_date = pd.Timestamp.now() if df.empty or pd.isna(df['date'].max()) else max(df['date'].max(), pd.Timestamp.now())
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full_weeks = pd.date_range(start=START_DATE, end=max_date, freq='W-MON')
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weekly_data = weekly_data.reindex(full_weeks, fill_value=0)
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weekly_data.columns = ['Days', 'Hours']
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if not weekly_data.empty:
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avg_days = weekly_data['Days'].mean()
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avg_hours = weekly_data['Hours'].mean()
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m1, m2 = st.columns(2)
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m1.metric("Avg Days / Week", f"{avg_days:.2f}")
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m2.metric("Avg Hours / Week", f"{avg_hours:.1f}")
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# Plotting
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st.caption("Days per Week (Effective)")
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# Calculate running average (expanding mean) to show trend over time
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weekly_data['Running Average'] = weekly_data['Days'].expanding().mean()
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st.line_chart(weekly_data[['Days', 'Running Average']], use_container_width=True)
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# --- Monthly Breakdown ---
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monthly_data = df.resample('M', on='date')[['effective_days', 'effective_hours']].sum()
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# Reindex for full months too
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# (Use 'MS' for Month Start to align better, or 'M' for End)
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# But reindexing with 'M' is tricky if today is mid-month.
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# Let's stick to simple reindexing of what we have + fillna(0) for displayed months
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# Actually, standard resample handles missing months WITHIN the range.
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# We just need to make sure the range starts at START_DATE.
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full_months = pd.date_range(start=START_DATE, end=max_date, freq='M')
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monthly_data = monthly_data.reindex(full_months, fill_value=0)
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monthly_data.columns = ['Days Worked', 'Hours Worked']
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# Calculate Avg Hours/Week for the month
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# (Total Hours / (Days in Month / 7))
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monthly_data['Avg Hours/Week'] = monthly_data['Hours Worked'] / (monthly_data.index.days_in_month / 7)
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# Format index to be more readable (e.g. "January 2023")
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monthly_data.index = monthly_data.index.strftime('%B %Y')
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st.write("### Monthly Breakdown")
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st.dataframe(monthly_data.style.format({
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"Avg Hours/Week": "{:.1f}",
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"Hours Worked": "{:.1f}",
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"Days Worked": "{:.1f}"
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}))
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st.divider()
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st.write("### 🚗 Export Travel Costs")
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# Select month for export
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available_months = df.resample('M', on='date').size().index.strftime('%B %Y').tolist()
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if available_months:
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selected_export_month = st.selectbox("Select Month to Export", reversed(available_months))
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if st.button("Generate Export Text"):
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# Filter data for selected month
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month_dt = pd.to_datetime(selected_export_month, format='%B %Y')
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month_df = df[(df['date'].dt.month == month_dt.month) & (df['date'].dt.year == month_dt.year)]
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if not month_df.empty:
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export_lines = ["Hierbij ook nog even het lijstje aan werkdagen met reiskosten:"]
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# Group by course
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courses = month_df['course_name'].unique()
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total_travel_days = 0
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for course in courses:
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if not course or course == 'Holiday':
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continue
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course_df = month_df[month_df['course_name'] == course].sort_values('date')
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travel_days = course_df[course_df['travel_costs'] == 1]
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no_travel_days = course_df[course_df['travel_costs'] == 0]
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if not travel_days.empty:
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dates_str = ", ".join(travel_days['date'].dt.strftime('%d %b'))
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line = f"{course}: {dates_str}"
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# Add "skipped" days if they exist within the range
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if not no_travel_days.empty:
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skip_dates = ", ".join(no_travel_days['date'].dt.strftime('%d %b'))
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line += f" (dus niet {skip_dates} want toen was het Online)"
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export_lines.append(line)
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total_travel_days += len(travel_days)
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export_lines.append("")
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export_lines.append(f"Totaal lijkt het dus {total_travel_days} dagen geweest te zijn. Zoals eerder bekeken leek het zo'n 30km per dag.")
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export_text = "\n".join(export_lines)
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st.code(export_text, language="text")
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st.success("You can copy the text above!")
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else:
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st.warning("No data found for this month.")
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else:
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st.info("No work days logged yet. Click dates on the calendar.")
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# --- Debugging (Optional) ---
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# with st.expander("Raw Data"):
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# st.dataframe(df)
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