Update README.md

README.md

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-# HRMS Email Automation & Legal Assistant Chatbot
-
-## Overview
-This repository contains three separate applications:
-1. **HRMS Email Automation** - A Streamlit-based application for automating HR interview invitation emails.
-2. **Legal Assistant Chatbot** - A Streamlit-based chatbot leveraging Retrieval-Augmented Generation (RAG) for legal assistance.
-3. **Employee Performance & Retention Analytics Dashboard** - A Streamlit-based analytics dashboard providing insights into employee attrition, performance, and retention risk.
-
----
-
-## 1️⃣ HRMS Email Automation
-
-### Description
-The **HRMS Email Automation** tool simplifies the process of sending interview invitations via email. It provides a user-friendly form to collect candidate details and automatically sends emails for both **Online** and **Face-to-Face** interviews.
-
-### Features
-- Select interview type: **Online** or **Face-to-Face**.
-- Input relevant details like candidate name, interview time, PIC (Person in Charge), and contact details.
-- Validate form fields to ensure required details are entered.
-- Send email automatically using the `EmailAuto` class.
-- User-friendly interface built with **Streamlit**.
-
-### Installation & Usage
-```sh
-# Clone the repository
-git clone https://github.com/your-repo.git
-cd your-repo
-
-# Install dependencies
-pip install -r requirements.txt
-
-# Run the Streamlit app
-streamlit run email_ui.py
-```
-
-### File Structure
-```
-├── automation/
-│   ├── emailAuto.py  # Handles email automation
-├── email_ui.py  # Streamlit UI for HRMS Email Automation
-```
-
----
-
-## 2️⃣ Legal Assistant Chatbot
-
-### Description
-The **Legal Assistant Chatbot** is a chatbot designed to provide legal assistance by retrieving relevant legal context from **Pinecone** and responding based on a fine-tuned **TinyLlama** model.
-
-### Features
-- Uses **Retrieval-Augmented Generation (RAG)** for context-aware legal responses.
-- Retrieves legal documents from **Pinecone** to enhance responses.
-- Fine-tuned **TinyLlama-1.1B-Chat** model for legal domain understanding.
-- Interactive chat interface built with **Gradio**.
-- Supports file uploads (optional).
-
-### Installation & Usage
-```sh
-# Install dependencies
-pip install -r requirements.txt
-
-# Run the chatbot
-python app.py
-```
-
-### File Structure
-```
-├── backend/
-│   ├── train.py  # Handles model training
-│   ├── rag.py  # Handles Pinecone-based retrieval
-├── app.py  # Gradio UI for the Legal Assistant Chatbot
-```
-
----
-
-## 3️⃣ Employee Performance & Retention Analytics Dashboard
-
-### Description
-The **Employee Performance & Retention Analytics Dashboard** provides insights into employee attrition, performance, and retention risk. It includes interactive visualizations, ML-based attrition predictions, and employee-specific evaluations.
-
-### Features
-- **Attrition Prediction:** Uses a RandomForest model to predict employee attrition likelihood.
-- **Performance Analysis:** Visualizations of performance rating, salary, and tenure.
-- **Retention Analysis:** Highlights risks based on job satisfaction, work-life balance, and promotion history.
-- **Employee Evaluation:** Individual employee performance and retention insights.
-- **AI-Powered Insights:** Generates department-specific insights based on filtered employee data.
-
-### Installation & Usage
-```sh
-# Install dependencies
-pip install -r requirements.txt
-
-# Run the Streamlit dashboard
-streamlit run dashboard.py
-```
-
-### File Structure
-```
-├── dashboard.py  # Streamlit UI for HR analytics
-├── HR-Employee-Attrition.csv  # Employee dataset
-```
-
----
-
-## Environment Variables
-Both applications require environment variables to function correctly. Create a `.env` file and add the following:
-```
-PINECONE_API_KEY=your_pinecone_api_key
-PINECONE_INDEX=your_pinecone_index
-PINECONE_NAMESPACE=your_pinecone_namespace
-```
-
----
-
-## Contribution
-Feel free to contribute to this project by submitting issues, pull requests, or feature suggestions!
-
----
-
-## License
-This project is licensed under the MIT License.
-
+---
+license: apache-2.0
+title: Chatbotv2
+sdk: gradio
+colorTo: blue
+---
+# HRMS Email Automation & Legal Assistant Chatbot
+
+## Overview
+This repository contains three separate applications:
+1. **HRMS Email Automation** - A Streamlit-based application for automating HR interview invitation emails.
+2. **Legal Assistant Chatbot** - A Streamlit-based chatbot leveraging Retrieval-Augmented Generation (RAG) for legal assistance.
+3. **Employee Performance & Retention Analytics Dashboard** - A Streamlit-based analytics dashboard providing insights into employee attrition, performance, and retention risk.
+
+---
+
+## 1️⃣ HRMS Email Automation
+
+### Description
+The **HRMS Email Automation** tool simplifies the process of sending interview invitations via email. It provides a user-friendly form to collect candidate details and automatically sends emails for both **Online** and **Face-to-Face** interviews.
+
+### Features
+- Select interview type: **Online** or **Face-to-Face**.
+- Input relevant details like candidate name, interview time, PIC (Person in Charge), and contact details.
+- Validate form fields to ensure required details are entered.
+- Send email automatically using the `EmailAuto` class.
+- User-friendly interface built with **Streamlit**.
+
+### Installation & Usage
+```sh
+# Clone the repository
+git clone https://github.com/your-repo.git
+cd your-repo
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Run the Streamlit app
+streamlit run email_ui.py
+```
+
+### File Structure
+```
+├── automation/
+│   ├── emailAuto.py  # Handles email automation
+├── email_ui.py  # Streamlit UI for HRMS Email Automation
+```
+
+---
+
+## 2️⃣ Legal Assistant Chatbot
+
+### Description
+The **Legal Assistant Chatbot** is a chatbot designed to provide legal assistance by retrieving relevant legal context from **Pinecone** and responding based on a fine-tuned **TinyLlama** model.
+
+### Features
+- Uses **Retrieval-Augmented Generation (RAG)** for context-aware legal responses.
+- Retrieves legal documents from **Pinecone** to enhance responses.
+- Fine-tuned **TinyLlama-1.1B-Chat** model for legal domain understanding.
+- Interactive chat interface built with **Gradio**.
+- Supports file uploads (optional).
+
+### Installation & Usage
+```sh
+# Install dependencies
+pip install -r requirements.txt
+
+# Run the chatbot
+python app.py
+```
+
+### File Structure
+```
+├── backend/
+│   ├── train.py  # Handles model training
+│   ├── rag.py  # Handles Pinecone-based retrieval
+├── app.py  # Gradio UI for the Legal Assistant Chatbot
+```
+
+---
+
+## 3️⃣ Employee Performance & Retention Analytics Dashboard
+
+### Description
+The **Employee Performance & Retention Analytics Dashboard** provides insights into employee attrition, performance, and retention risk. It includes interactive visualizations, ML-based attrition predictions, and employee-specific evaluations.
+
+### Features
+- **Attrition Prediction:** Uses a RandomForest model to predict employee attrition likelihood.
+- **Performance Analysis:** Visualizations of performance rating, salary, and tenure.
+- **Retention Analysis:** Highlights risks based on job satisfaction, work-life balance, and promotion history.
+- **Employee Evaluation:** Individual employee performance and retention insights.
+- **AI-Powered Insights:** Generates department-specific insights based on filtered employee data.
+
+### Installation & Usage
+```sh
+# Install dependencies
+pip install -r requirements.txt
+
+# Run the Streamlit dashboard
+streamlit run dashboard.py
+```
+
+### File Structure
+```
+├── dashboard.py  # Streamlit UI for HR analytics
+├── HR-Employee-Attrition.csv  # Employee dataset
+```
+
+---
+
+## Environment Variables
+Both applications require environment variables to function correctly. Create a `.env` file and add the following:
+```
+PINECONE_API_KEY=your_pinecone_api_key
+PINECONE_INDEX=your_pinecone_index
+PINECONE_NAMESPACE=your_pinecone_namespace
+```
+
+---
+
+## Contribution
+Feel free to contribute to this project by submitting issues, pull requests, or feature suggestions!
+
+---
+
+## License
+This project is licensed under the MIT License.