From 29895669cc5001bf2dacff486bf2333eabdb5956 Mon Sep 17 00:00:00 2001
From: M V Harinandan <harinandanmv11@gmail.com>
Date: Tue, 30 Dec 2025 11:28:30 +0530
Subject: [PATCH] Revise README for clarity and visual appeal

Updated README to enhance clarity and organization, added emojis for better visual appeal, and improved section headings.
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 README.md | 140 ++++++++++++++++++++++++++++++++++++++----------------
 1 file changed, 98 insertions(+), 42 deletions(-)

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-# video-processing-distributed
+# 🎥 Video Processing Distributed
 
-## Master Controller
+## 🧠 Master Controller
 
-This project provides a **distributed video processing system** using:
-- **Streamlit** for the UI  
-- **Flask** for the worker-facing API  
-- **FFmpeg** for merging  
-- **MP4Box** for video splitting  
-- **Multiple worker nodes** that pick up chunks, encode them, and return processed video parts.
+This project implements a **distributed video processing system** using a master–worker model.
+You control everything from a simple UI, while worker nodes handle video chunks in parallel.
 
-The master automatically:
-1. Accepts a large input MP4
-2. Splits it into chunks
-3. Assigns each chunk to workers
-4. Waits for all jobs to complete
-5. Merges encoded chunks into a final MP4
-6. Deletes all temporary chunk folders (chunks + results)
+### 🔧 Tech Stack
+
+* 🖥️ Streamlit for the master UI
+* 🌐 Flask for worker communication
+* 🎞️ FFmpeg for encoding and merging
+* ✂️ MP4Box for video splitting
+* 🧩 Multiple worker nodes for parallel processing
+
+---
+
+## ⚙️ How the System Works
+
+The master node performs the following steps automatically:
+
+1. 📥 Accepts a large input MP4 file
+2. ✂️ Splits the video into time-based chunks
+3. 📤 Assigns chunks to available worker nodes
+4. ⏳ Waits for all workers to finish processing
+5. 🔗 Merges processed chunks into a final MP4
+6. 🧹 Cleans up all temporary chunk and output folders
+
+---
+
+## 📦 Requirements
+
+### 🐍 Python Libraries (Master Node)
+
+Install using pip:
+
+* flask
+* werkzeug
+* streamlit
+* streamlit-autorefresh
+* pandas
+* requests
+
+### 🧑‍💻 Worker Node Requirements
+
+Only the following are required:
+
+* requests
+* ffmpeg
+
+---
+
+## 🛠️ System Dependencies
+
+### ✂️ MP4Box (Master Node Only)
+
+Used for splitting videos into chunks.
+
+* Command used:
+  MP4Box -split `<seconds>`
+* Download via official GPAC site or YouTube guides
+* Ensure MP4Box is added to system PATH
+
+### 🎞️ FFmpeg (Master + Worker Nodes)
+
+Used for encoding and merging video chunks.
+
+Install on Windows:
+
+```
+winget install ffmpeg
+```
+
+Verify both `ffmpeg` and `MP4Box` are accessible from the terminal.
 
 ---
 
-## Requirements
-
-### Python libraries (install with pip)
-- flask
-- werkzeug
-- streamlit
-- streamlit-autorefresh
-- pandas
-- requests
-
-For worker node need to install only `requests` and `ffmpeg`
-
-### System Dependencies (must be installed manually)
-- **MP4Box (GPAC)-(Only needed in master node)**  
-  Required for splitting video into chunks: MP4Box -split <seconds>
-  Refer google or youtube for downloading it
-- **FFmpeg(Needed both in master and worker node)**  
-  Required for merging processed chunks.
-  Install in windows using
-  ```
-  winget ffmpeg
-  ```
-Ensure both `ffmpeg` and `MP4Box` are available in your system PATH.
-
-Run the master node using streamlit
+## ▶️ Running the Master Node
+
+Start the master controller UI:
+
 ```
 streamlit run master.py
 ```
-Ensure that the system is configured for getting inbound through the specified port. Used here is **5000**.
-You can set up in windows by adding a new inbound tcp for port 5000 rule in firewall.
 
+---
+
+## 🔐 Network Configuration
+
+* Master node listens on port **5000**
+* Ensure inbound TCP traffic is allowed on this port
+
+### Windows Firewall Setup
+
+* Add a new inbound rule
+* Protocol: TCP
+* Port: 5000
+* Action: Allow
+
+---
+
+## 🚀 Use Case
 
+This project demonstrates:
 
+* Distributed computing fundamentals
+* Parallel video encoding
+* Master–worker architecture
+* Practical use of FFmpeg in Python systems
 
+Ideal for academic experiments, demos, and learning distributed systems through multimedia processing.