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task_operations.py

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+import pandas as pd
+import os
+from datetime import datetime
+import streamlit as st
+
+class TaskManager:
+    TASKS_FILE = "tasks.csv"
+    CATEGORIES = ['Learning', 'Gym', 'Personal', 'Family', 'Work', 'Prayer']
+
+    def __init__(self):
+        if 'tasks' not in st.session_state:
+            st.session_state.tasks = self.load_tasks()
+
+    def load_tasks(self):
+        if os.path.exists(self.TASKS_FILE):
+            if os.path.getsize(self.TASKS_FILE) > 0:
+                return pd.read_csv(self.TASKS_FILE, parse_dates=['Task Time']).to_dict(orient='records')
+            else:
+                return []
+        else:
+            df = pd.DataFrame(columns=["Task Name", "Task Time", "Task Duration (hours)", "Task Duration (minutes)", "Category"])
+            df.to_csv(self.TASKS_FILE, index=False)
+            return []
+
+    def save_tasks(self, tasks):
+        df = pd.DataFrame(tasks)
+        df.to_csv(self.TASKS_FILE, index=False)
+
+    def add_task(self, task_name, task_time, task_duration_hours, task_duration_minutes, task_category):
+        task_time_full = datetime.combine(datetime.today(), task_time)
+        task_entry = {
+            "Task Name": task_name,
+            "Task Time": task_time_full,
+            "Task Duration (hours)": int(task_duration_hours),
+            "Task Duration (minutes)": int(task_duration_minutes),
+            "Category": task_category
+        }
+        st.session_state.tasks.append(task_entry)
+        self.save_tasks(st.session_state.tasks)
+
+    def delete_task_by_name(self, task_name):
+        for index, task in enumerate(st.session_state.tasks):
+            if task['Task Name'] == task_name:
+                st.session_state.tasks.pop(index)
+                self.save_tasks(st.session_state.tasks)
+                return True
+        return False
+
+    def generate_report(self, timeframe):
+        df = pd.DataFrame(st.session_state.tasks)
+        if timeframe == 'daily':
+            report = df[df['Task Time'].dt.date == pd.Timestamp.today().date()]
+        elif timeframe == 'weekly':
+            week_start = pd.Timestamp.today() - pd.DateOffset(days=pd.Timestamp.today().dayofweek)
+            report = df[(df['Task Time'] >= week_start) & (df['Task Time'] <= pd.Timestamp.today())]
+        elif timeframe == 'monthly':
+            report = df[df['Task Time'].dt.month == pd.Timestamp.today().month]
+        elif timeframe == 'yearly':
+            report = df[df['Task Time'].dt.year == pd.Timestamp.today().year]
+
+        report['Total Duration'] = report['Task Duration (hours)'] + report['Task Duration (minutes)'] / 60.0
+        return report

task_visualization.py

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+import matplotlib.pyplot as plt
+import pandas as pd
+import streamlit as st
+
+class TaskVisualizer:
+    def plot_performance(self):
+        df = pd.DataFrame(st.session_state.tasks)
+        df['Total Duration'] = df['Task Duration (hours)'] + df['Task Duration (minutes)'] / 60.0
+
+        plt.figure(figsize=(10, 5))
+        task_times = df.groupby('Task Name')['Total Duration'].sum()
+        task_times.plot(kind='bar')
+        plt.xlabel('Task')
+        plt.ylabel('Hours Spent')
+        plt.title('Overall Task Performance')
+        plt.xticks(rotation=45)
+        plt.tight_layout()
+        st.pyplot(plt)
+
+    def plot_category_performance(self, timeframe, task_manager):
+        report = task_manager.generate_report(timeframe)
+        if not report.empty:
+            category_times = report.groupby('Category')['Total Duration'].sum()
+
+            plt.figure(figsize=(10, 5))
+            category_times.plot(kind='bar', color='skyblue')
+            plt.xlabel('Category')
+            plt.ylabel('Total Hours Spent')
+            plt.title(f'Task Performance by Category - {timeframe.capitalize()} Report')
+            plt.xticks(rotation=45)
+            plt.tight_layout()
+            st.pyplot(plt)
+
+    def plot_overall_category_performance(self):
+        df = pd.DataFrame(st.session_state.tasks)
+        df['Total Duration'] = df['Task Duration (hours)'] + df['Task Duration (minutes)'] / 60.0
+
+        category_times = df.groupby('Category')['Total Duration'].sum()
+
+        plt.figure(figsize=(10, 5))
+        category_times.plot(kind='bar', color='lightgreen')
+        plt.xlabel('Category')
+        plt.ylabel('Total Hours Spent')
+        plt.title('Overall Task Performance by Category')
+        plt.xticks(rotation=45)
+        plt.tight_layout()
+        st.pyplot(plt)
+
+    def download_report(self):
+        df = pd.DataFrame(st.session_state.tasks)
+        csv = df.to_csv(index=False)
+        st.download_button("Download CSV", data=csv, file_name="task_report.csv", mime='text/csv')