---
license: mit
title: Customer Experience Bot Demo
sdk: gradio
colorFrom: purple
colorTo: green
short_description: CX AI LLM
---
title: Customer Experience Bot Demo emoji: 🤖 colorFrom: blue colorTo: purple sdk: gradio sdk_version: "4.44.0" app_file: app.py pinned: false

Customer Experience Bot Demo

A Retrieval-Augmented Generation (RAG) based customer experience (CX) bot deployed on Hugging Face Spaces (free tier). Demonstrates robust data cleanup and query validation to deliver high-quality, multilingual CX solutions for enterprise applications in SaaS, HealthTech, FinTech, and eCommerce.

Features





RAG Pipeline: Retrieves FAQs using all-MiniLM-L6-v2 and FAISS for accurate, context-aware responses.



Data Cleanup: Filters nulls, duplicates, and low-quality FAQs (e.g., short answers) to ensure reliable outputs.



Performance Visualization: Displays latency and accuracy metrics with Matplotlib/Seaborn to monitor data quality.



Gradio Interface: User-friendly UI for querying, viewing FAQs, and checking cleanup statistics.

Setup





Clone this repository to a Hugging Face Space (free tier, public).



Create requirements.txt with the listed dependencies.



Upload app.py (includes embedded sample FAQs for simplicity).



Configure the Space to run with Python 3.9+ and no GPU.

Usage





Enter a query (e.g., “How do I reset my password?”) in the Gradio UI.



View the bot’s response, retrieved FAQs, data cleanup statistics, and RAG pipeline plot.



Example output:





Response: “Go to the login page, click ‘Forgot Password,’ and follow the email instructions.”



Cleanup Stats: “Cleaned FAQs: 3 (removed 2 junk entries)”

Data Cleanup





FAQ Preprocessing: Removes nulls, duplicates, and answers shorter than 20 characters to ensure high-quality data.



Query Validation: Rejects empty or overly short queries (<5 characters) for reliable input processing.



Impact: Clean data is essential for accurate, scalable CX solutions, ensuring robust performance for enterprise Partners.

Technical Details





Stack: Python, Hugging Face (all-MiniLM-L6-v2), FAISS (CPU), Gradio, Pandas, Matplotlib, Seaborn.



Free Tier Compatibility: Lightweight design with no GPU requirements, optimized for Hugging Face Spaces.



Extensibility: Easily adaptable for CRM integrations (e.g., Salesforce) and cloud deployments (e.g., AWS Lambda).

Purpose

Developed to showcase expertise in designing, building, and deploying CX bots with a strong focus on data quality, tailored for AI-driven customer experience platforms.