Docs/renew readme

README.md

@@ -1,136 +1,76 @@
-# SentiLog AI
+# 🧠 SentiLog AI
 
 [![MIT License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)
 [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/openml-stack/SentiLog-AI/pulls)
 [![Build Status](https://img.shields.io/github/actions/workflow/status/openml-stack/SentiLog-AI/ci.yml?branch=main)](https://github.com/openml-stack/SentiLog-AI/actions)
-[![Open Issues](https://img.shields.io/github/issues/your-org/SentiLog-AI)](https://github.com/openml-stack/SentiLog-AI/issues)
+[![Open Issues](https://img.shields.io/github/issues/openml-stack/SentiLog-AI)](https://github.com/openml-stack/SentiLog-AI/issues)
 
 ---
 
-## 🧠 What is SentiLog AI?
+## 🌟 Overview
 
-SentiLog AI is an open-source platform that combines news sentiment analysis and personal mood journaling. It features a modern React frontend, a robust Node.js/Express backend, and a Python/Flask microservice for ML-powered sentiment/emotion analysis.
+**SentiLog AI** is a full-stack application that blends real-time news sentiment analysis with personal mood journaling. It helps users track their emotions and detect news bias using machine learning.
 
 ---
 
-## 🌟 Core Features
+## 🔧 Tech Stack
 
-SentiLog AI provides a unique fusion of sentiment-aware news analysis and personal mental wellness tracking through the following key features:
-
-### 📰 Real-Time News Bias Filter
-
-- Automatically analyzes current news articles and classifies them based on political bias: Left, Right, or Neutral.
-- Helps users recognize media bias and form balanced opinions.
-- Uses Natural Language Processing (NLP) models to assess sentiment and ideological leaning in real-time.
-
-### 📓 Daily Mood Journal
-
-- Users can write daily journal entries about their thoughts or experiences.
-- The system performs sentiment and emotion analysis (e.g., happy, sad, anxious).
-- Helps users track their mental state over time, identifying patterns or triggers.
-- Journal data can be cross-referenced with consumed news to understand the impact of external events on personal emotions.
+| Layer       | Technology                    |
+|-------------|-------------------------------|
+| Frontend    | React, Vite, Tailwind CSS     |
+| Backend     | Node.js, Express, MongoDB     |
+| ML API      | Python, Flask, VaderSentiment |
+| Auth        | JWT-based Authentication      |
+| DB          | MongoDB Atlas (via Mongoose)  |
 
 ---
 
-## 📁 Project Structure
+## 🗂️ Architecture
 
-```
-project-root/
-│
-├── client/ 
-|   ├── index.html/
-|   ├── public/                 
-│   └── src/                 # React frontend (Vite + Tailwind)
-│       ├── pages/           # JournalPage, NewsPage, Dashboard
-│       ├── components/      # Navbar, SentimentCard, ChartPanel
-│       ├── App.jsx
-│       └── main.jsx
-│
-├── server/                  # Node.js + Express backend
-│   ├── routes/              # journal.js, news.js
-│   ├── controllers/
-|   ├── middlewares/         # Express entry
-|   ├── uploads/
-|   ├── utils/
-│   ├── models/              # Mongo schemas
-│   └── index.js             
-│
-├── ml-api/                  # Flask-based ML service
-│   ├── app.py
-│   ├── model/
-|   ├── vader_service.py/
-│   └── requirements.txt
-|
-├── notebook/
-|    ├── varder_sentiment_demo.ipynb
-│    
-├── .gitignore
-├── README.md
-└── package.json / envs
+```mermaid
+graph LR
+  A[React Frontend] --> B[Node.js/Express API]
+  B --> C[MongoDB]
+  B --> D[Flask ML API]
+  D --> E[Sentiment Analysis]
 ```
 
 ---
 
-## 🚀 Quick Start
+## 🚀 Getting Started
 
 ### 1. Clone the Repository
 
 ```bash
-git clone https://github.com/your-org/SentiLog-AI.git
+git clone https://github.com/openml-stack/SentiLog-AI.git
 cd SentiLog-AI
 ```
 
 ---
 
-### 2. Setup the Frontend (client/)
+### 2. Install & Run Client
 
 ```bash
 cd client
 npm install
 npm run dev
 ```
 
-- Built with [Vite](https://vitejs.dev/) + [React](https://react.dev/) + [Tailwind CSS](https://tailwindcss.com/)
-- Main routes: `/journal`, `/news`, `/dashboard`
-- Placeholder components: `Navbar`, `TextInput`, `SentimentCard`, `LineChart`
-
 ---
 
-### 3. Setup the Backend (server/)
+### 3. Install & Run Server
 
 ```bash
 cd ../server
 npm install
+cp .env.example .env
+# Fill your MongoDB URI and other secrets in .env
 npm run dev
 ```
 
-- [Express.js](https://expressjs.com/) API server
-- Connects to MongoDB (see `.env.example`)
-- Routes:
-  - `POST /api/journal/analyze` → calls ML API
-  - `POST /api/news/analyze` → calls ML API
-- Uses [Mongoose](https://mongoosejs.com/) for MongoDB schemas
-
 ---
 
----
-
-### 📘 API Documentation (Swagger)
-
-Once your backend server is running, you can explore all API endpoints using **Swagger UI**:
-
-[https://sentilog-ai.onrender.com/api-docs](https://sentilog-ai.onrender.com/api-docs)
-
-This interactive documentation provides:
-
-- Available endpoints and HTTP methods
-- Required parameters and request formats
-- Sample requests and responses
-- Error status codes
-
-> **Note:** If your server runs on a different port, update the URL accordingly (e.g., `http://localhost:3000/api-docs`).
-
-### 4. Setup the ML API (ml-api/)
+### 4. Install & Run ML API
 
 ```bash
 cd ../ml-api
@@ -140,64 +80,82 @@ pip install -r requirements.txt
 python app.py
 ```
 
-- [Flask](https://flask.palletsprojects.com/) microservice
-- `/predict` POST endpoint: accepts `{"text": "..."}` and returns mock sentiment/emotion
-
 ---
 
-### 5. Environment Variables
+## 🌍 Environment Variables
 
-- Copy `.env.example` in `server/` to `.env` and fill in your MongoDB URI and other secrets.
+Update `.env` in `server/` folder with:
+
+```
+PORT=8080
+MONGODB_URI=mongodb://localhost:27017/sentilog
+ML_API_URL=http://localhost:8060
+JWT_SECRET=your_secret_key
+```
 
 ---
 
-## 🧩 Contributing
+## 📄 API Documentation
 
-1. Comment on the issue you want to work on (frontend, ml-api, express route, schema)
-2. Fork the repo & clone locally
-3. Work in the corresponding subfolder
-4. Open a PR with the title: `[Feature]: <Your Component or Route>`
+You can find detailed API information in [server/API_DOC.md](server/API_DOC.md), including:
 
----
+- All Server and ML API endpoints
+- Request/response formats
+- Sample payloads
+- Status codes & error handling
 
-## 📦 Tech Stack
+Additionally, the Swagger documentation is available at:
 
-- **Frontend:** React, Vite, Tailwind CSS
-- **Backend:** Node.js, Express, MongoDB, Mongoose
-- **ML API:** Python, Flask, transformers, vaderSentiment
+```text
+http://localhost:8080/api-docs
+```
 
 ---
 
-## 📝 License
+## ✨ Features
 
-This project is licensed under the **MIT License** — see the [LICENSE](./LICENSE) file for details.
+### 📰 News Bias Filter
+- Classifies articles as Left, Right, or Neutral.
+- Real-time NLP-based sentiment + ideological analysis.
 
-The MIT License is a permissive license that allows you to:
+### 📓 Mood Journal
+- Users log their mood and track emotional trends.
+- Analyzed using ML-based sentiment/emotion scoring.
 
-- Freely use, copy, modify, and distribute the code.
-- Use the project in commercial and non-commercial applications.
-- Attribute the original creator(s) in any reused version.
+---
 
-By contributing to this repository, you agree that your contributions will be licensed under the MIT License as well.
+## 📁 Project Structure
+
+```
+SentiLog-AI/
+├── client/           # Frontend (React)
+├── server/           # Backend (Express + MongoDB)
+├── ml-api/           # Python Flask ML service
+├── notebook/         # Research notebooks
+├── README.md
+└── server/API_DOC.md # Detailed API docs
+```
 
 ---
 
-## 🙌 Community
+## 🧩 Contributing
 
-- [Open Issues](https://github.com/openml-stack/SentiLog-AI/issues)
-- [Pull Requests](https://github.com/openml-stack/SentiLog-AI/pulls)
+1. Comment on an open issue.
+2. Fork the repository.
+3. Work in a feature branch.
+4. Open a pull request with descriptive title.
 
 ---
 
-## 📄 Acknowledgements
+## 📝 License
 
-- [Vite](https://vitejs.dev/)
-- [Tailwind CSS](https://tailwindcss.com/)
-- [Flask](https://flask.palletsprojects.com/)
-- [transformers](https://huggingface.co/transformers/)
-- [vaderSentiment](https://github.com/cjhutto/vaderSentiment)
+MIT License — see [LICENSE](./LICENSE)
 
 ---
 
-> _Let’s build the future of mood and news analysis together — one contribution at a time! _ 
+## 🙌 Community & Support
+
+- [Issues](https://github.com/openml-stack/SentiLog-AI/issues)
+- [Discussions](https://github.com/openml-stack/SentiLog-AI/discussions)
 
+> Let’s build the future of sentiment + media intelligence together.