Updated the ReadMe file (#4)

- Updated the ReadMe file (8e96e9299197e1698cd81562640c6a6ba52296c5)


Co-authored-by: Bismark Bantar <[email protected]>

README.md

@@ -1,14 +1,153 @@
----
-title: SocialMediaFoci
-emoji: 📈
-colorFrom: green
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.44.1
-app_file: app.py
-pinned: false
-license: mit
-short_description: Whatsappchat analyser
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# WhatsApp Chat Analyzer
+
+A comprehensive tool for analyzing WhatsApp chat exports with sentiment analysis capabilities.
+
+## Table of Contents
+1. [System Overview](#system-overview)
+2. [Architecture](#architecture)
+3. [Components](#components)
+4. [Data Flow](#data-flow)
+5. [Installation](#installation)
+6. [Usage](#usage)
+7. [Analysis Capabilities](#analysis-capabilities)
+8. 
+
+## System Overview
+
+The WhatsApp Chat Analyzer is a Python-based application that processes exported WhatsApp chat data to provide:
+- Message statistics and metrics
+- Temporal activity patterns
+- User engagement analysis
+- Content analysis (words, emojis, links)
+- Sentiment analysis capabilities
+- Topics analysis in the group chats
+
+Built with Streamlit for the web interface, it offers an interactive way to explore chat dynamics and analyze sentiment.
+
+## Architecture
+
+The system follows a modular architecture with clear separation of concerns:
+
+```
+Raw WhatsApp Chat → Preprocessing → Analysis → Visualization
+```
+
+Key architectural decisions:
+- **Modular Design**: Components are separated by functionality
+- **Pipeline Processing**: Data flows through discrete processing stages
+- **Interactive UI**: Streamlit enables real-time exploration
+
+## Components
+
+### 1. App Module (`app.py`)
+- **Responsibility**: User interface and visualization
+- **Key Features**:
+  - File upload handling
+  - User selection interface
+  - Visualization rendering
+  - Interactive controls
+
+### 2. Preprocessor (`preprocessor.py`)
+- **Responsibility**: Data cleaning and structuring
+- **Key Features**:
+  - Handles multiple date/time formats
+  - Extracts messages and metadata
+  - Filters system messages
+  - Creates structured DataFrame
+
+### 3. Helper Module (`helper.py`)
+- **Responsibility**: Analytical computations
+- **Key Features**:
+  - Statistical metrics
+  - Temporal analysis
+  - Content analysis
+  - Visualization data preparation
+
+### 4. Notebook (`whatsAppAnalyzer.ipynb`)
+- **Responsibility**: Prototyping and experimentation
+- **Key Features**:
+  - Initial pattern development
+  - Data exploration
+  - Algorithm testing
+
+## Data Flow
+
+1. **Input**: User uploads WhatsApp chat export (.txt)
+2. **Preprocessing**:
+   - Raw text is parsed using regex patterns
+   - Messages are categorized and timestamped
+   - Structured DataFrame is created
+3. **Analysis**:
+   - Selected metrics are computed
+   - Temporal patterns are identified
+   - Content features are extracted
+4. **Visualization**:
+   - Results are displayed in interactive charts
+   - User can explore different views
+
+## Installation
+
+### Prerequisites
+- Python 3.8+
+- pip package manager
+
+### Steps
+1. Clone the repository:
+   ```bash
+   git clone [repository-url]
+   cd whatsapp-analyzer
+   ```
+
+2. Install dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. Run the application:
+   ```bash
+   streamlit run srcs/app.py
+   ```
+
+## Usage
+
+1. Launch the application
+2. Upload a WhatsApp chat export file
+3. Select a user or "Overall" for group analysis
+4. Explore the various analysis tabs:
+   - Statistics
+   - Timelines
+   - Activity Maps
+   - Word Clouds
+   - Emoji Analysis
+
+## Analysis Capabilities
+
+### 1. Basic Statistics
+- Message counts
+- Word counts
+- Media shared
+- Links shared
+
+### 2. Temporal Analysis
+- Daily activity patterns
+- Monthly trends
+- Hourly distributions
+
+### 3. User Engagement
+- Most active users
+- User participation rates
+- Message distribution
+
+### 4. Content Analysis
+- Most common words
+- Emoji usage
+
+### 5. Sentiment Analysis
+- Message sentiment scoring
+- Sentiment trends over time
+- User sentiment comparison
+## 5. Topics Analysis
+- Topic modeling
+- Common topics over time
+- User interests
+