📊 C-Engine Student Dashboard - Sample Project

A hybrid, lightweight analytical dashboard that bridges a low-level native C Processing Core with an interactive Streamlit (Python) Web Frontend. The application demonstrates Inter-Process Communication (IPC) by dynamically compiling and running a C program on the hosting server, intercepting its standard output (stdout), and parsing it directly into data visualizations.

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🏛️ Project Architecture Layout

├── packages.txt       # System dependency manager (Auto-installs GCC compiler)
├── requirements.txt   # Python package manager (Auto-installs Streamlit & Pandas)
├── app.py             # Streamlit frontend layout and subprocess execution controller
└── project.c          # Native C data structure processing engine

⚡ Technical Workflow

1. On-Demand Compilation: When a user interacts with the web interface, Python triggers the Linux gcc compiler to bundle project.c into an optimized machine binary (backend).
2. Data Streaming: The binary executes natively on the host server instance. It computes mathematical averages using static structures and streams out a standardized CSV formatting table to the system shell console.
3. Reactive UI Interception: Python catches the live text output stream via a secure subprocess data pipeline, deserializes it into memory as a Pandas Dataframe, and displays interactive analytical metric blocks.

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🛠️ Deployment Instructions

Cloud Deployment (Streamlit Community Cloud)

1. Commit all four project files directly into a public GitHub Repository.
2. Log into Streamlit Community Cloud using your GitHub workspace profile credentials.
3. Click New app, select your repository name, configure your deployment branch to main, and explicitly assign the main file path to app.py.
4. Click Deploy! The server container will automatically identify packages.txt to install the C development environment before loading your web interface link.

Local Machine Execution

Ensure you have a local C compiler (gcc) and Python 3.8+ properly configured on your operating system environment path variables.

# 1. Clone your project workspace down locally
git clone https://github.com
cd your-repository-name

# 2. Build the python packages stack
pip install -r requirements.txt

# 3. Spin up the Streamlit engine instance
streamlit run app.py

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📋 Source Elements Configuration Manifest

To ensure clean execution profiles on deployment, verify that your configuration infrastructure matching files are created precisely as written below:

• packages.txt

  gcc
  

• requirements.txt

  streamlit
  pandas