Study Tracker

A streamlined, data-driven application designed to help candidates track, analyze, and optimize their study routines for public exams (concursos). Built with Python and Streamlit, this tool provides a visual dashboard to monitor progress against goals, manage study sessions, and visualize performance metrics.

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Purpose

Preparing for public exams requires discipline and consistent tracking. This project serves as a personal "Study Command Center," allowing users to:

• Quantify Effort: Move beyond "feeling" productive to seeing actual hours spent on specific subjects.
• Track Goals: Set weekly targets and visualize progress in real-time.
• Data-Driven Decisions: Identify which subjects need more attention based on historical data.

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Key Features

• Real-time Dashboard: Visual metrics for total hours, daily averages, and session intensity.
• Interactive Timer: Start and stop study sessions directly from the interface.
• Weekly Goal Management: Set study hour targets for each week and track fulfillment percentages.
• Detailed History: Review past sessions and manage records.
• Exercise Tracking: Log quantity and accuracy of solved questions.
• Data Persistence: Local SQLite database ensuring your data stays private and accessible.

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Tech Stack

• Frontend: Streamlit (for a fast, interactive UI)
• Data Analysis: Pandas
• Visualizations: Matplotlib
• Database: SQLite
• Containerization: Docker & Docker Compose

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Running with Docker

This is the recommended way to run the project, as it handles all dependencies automatically.

Prerequisites

• Docker installed.
• Docker Compose installed.

Steps

1. Clone the repository:

   git clone https://github.com/mello-pedro/webapp_concursos.git
   cd app_concursos

2. Spin up the container:

   docker-compose up -d

3. Access the app: Open your browser and go to: http://localhost:8501

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Local Setup (Alternative)

If you prefer to run it directly with Python:

1. Create a virtual environment:

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate

2. Install dependencies:

   pip install -r requirements.txt

3. Run the application:

   streamlit run src/home.py

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Project Structure

├── src/               # Application source code
│   ├── home.py        # Landing page & Main metrics
│   ├── db.py          # Database operations
│   └── pages/         # Specialized views (Stats, Goals, etc.)
├── db/                # Local SQLite database (persisted via Docker volumes)
├── data/              # Reference files and external data
├── Dockerfile         # Docker build instructions
└── docker-compose.yml # Service orchestration