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💬 Asha AI Chatbot – Documentation

📘 Index

1. Overview
2. Features
3. Datasets Used
4. Repository Structure
5. How to Run
6. API Integrations
7. Frontend Overview
8. Backend Overview
9. Data Preprocessing
10. Acknowledgements

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🧠 Overview

Asha AI is an inclusive AI chatbot built to empower women by providing access to job opportunities, mentorship, and personalized career guidance. It uses real-time job APIs, resume parsing, and D&I (Diversity and Inclusion) datasets to offer helpful and relevant insights for users. It has been deployed online at- [!]https://asha-ai-bot-blue.vercel.app/

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✨ Features

• Real-time job search from Google Jobs and Indeed APIs
• Resume parsing and job matching
• Diversity & Inclusion recommendations
• Chat with historical query memory
• Dashboard for profile and job views
• Responsive frontend using React

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📊 Datasets Used in the Asha AI Chatbot Project

Overview

The Asha AI Chatbot leverages multiple datasets to provide real-time job listings, mentorship opportunities, and personalized career recommendations. Data is sourced from publicly available platforms and APIs.

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1. Google Jobs Search API

• Source: Google Jobs API
• Description: Fetches real-time job listings dynamically based on user queries, including job titles, companies, and locations.

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2. Indeed API

• Source: Indeed API
• Description: Pulls updated job postings from the Indeed database including roles, descriptions, and companies.

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3. LinkedIn Job Listings (1.3M Dataset)

• Source: Kaggle
• Description: Contains 1.3M job listings with skills, job titles, and company information for skill-job alignment.

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4. PwC Diversity & Inclusion Dashboard

• Source: PwC D&I
• Description: Promotes jobs at inclusive companies by referencing their D&I initiatives and metrics.

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5. Resume Dataset (Livecareer)

• Source: Livecareer
• Description: Resume samples used for parsing and comparing with job descriptions for match accuracy.

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6. Lightcast - Open Skills API

• Source: Lightcast
• Description: Provides emerging and in-demand skill trends, used to enhance recommendations for users to upskill.

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📁 Repository Structure

AshaAIBot/
│
├── README.md
├── allfileshas.txt
├── myenv/                  # Python virtual environment
├── data/                   # Datasets used for chatbot training and matching
│   ├── llmchatbot/
│   │   ├── jobs_chatbot.csv
│   │   ├── llm_pwc.csv
│   │   └── resume_chatbot.csv
│   └── resumejobmatch/
│       ├── jobmatch_jobs.csv
│       ├── jobmatch_linkedinpost.csv
│       ├── jobmatch_linkedinskills.csv
│       └── jobmatch_resumes.csv
│
├── datapreprocess_clean/   # Cleaned datasets and scripts
│
├── sample_models/          # Sample models (optional)
│
├── asha_ai/                # Main chatbot app
│   ├── backend/            # Node.js + Python backend
│   │   ├── server.js
│   │   ├── serverworking.js
│   │   ├── diversityinclusion.xlsx
│   │   ├── ai/
│   │   │   ├── matchjobs.js
│   │   │   ├── smartresponse.js
│   │   │   └── career_csvs/
│   │   └── routes/
│   │       ├── app.py
│   │       ├── jobsRoute.js
│   │       ├── requirements.txt
│
│   └── src/                # React frontend
│       ├── App.js
│       ├── index.js
│       ├── components/
│       │   ├── Chatbot.js
│       │   ├── Chatwithhistory.js
│       │   ├── Home.js
│       │   ├── Dashboard.js
│       │   ├── Login.js
│       │   ├── #.js
│       │   ├── Profile.js
│       │   ├── matchjob.js
│       │   ├── DiversityViewer.js
│       │   └── NavigationBar.js

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🛠️ How to Run

1. Backend (Node + Python)

cd asha_ai/backend
npm install
node server.js

Optionally run Python APIs from routes/app.py using Flask or FastAPI:

cd routes
pip install -r requirements.txt
python3 app.py

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2. Frontend (React)

cd asha_ai
npm install
npm start

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🔌 API Integrations

• Google Jobs Search API
• Indeed Jobs API
• Lightcast Open Skills API
• Custom resume and job parsing functions in ai/matchjobs.js and smartresponse.js

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💻 Frontend Overview

React components located in:

asha_ai/src/components/

Each component corresponds to pages or UI features like:

• Chatbot.js – AI assistant interface
• Login.js, #.js, Profile.js – Auth and profile pages
• matchjob.js – Resume to job matching logic
• DiversityViewer.js – D&I visualization

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⚙️ Backend Overview

Node.js with Express for job APIs and integration with Python. Key files:

• server.js – Main server logic
• ai/smartresponse.js – AI reply generation
• routes/jobsRoute.js – Routes for job search APIs

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🧼 Data Preprocessing

Cleaned datasets and scripts stored in:

datapreprocess_clean/

Used for preparing CSVs for training, matching, and chatbot response enrichment.

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🙌 Acknowledgements

• Google Cloud
• Indeed
• LinkedIn Datasets on Kaggle
• PwC Diversity & Inclusion Initiative
• Lightcast Skills
• Livecareer Resume Samples

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Let me know if you'd like me to help generate a PDF version of this or convert this into a `README.md` file directly.