README.md

---
license: mit
title: Customer Experience Bot Demo
sdk: gradio
colorFrom: purple
colorTo: green
short_description: CX AI LLM
---

# Internal RAG CX Data Preprocessing Demo

A robust data preprocessing pipeline for Retrieval-Augmented Generation (RAG) and Context-Augmented Generation (CAG) systems, deployed on Hugging Face as a Model repository (free tier). Built with over 5 years of AI expertise since 2020, this demo focuses on cleaning and preparing call center datasets for enterprise-grade CX applications in SaaS, HealthTech, FinTech, and eCommerce. It integrates advanced data wrangling with Pandas, ensuring high-quality FAQs for downstream RAG/CAG pipelines, and is compatible with Amazon SageMaker and Azure AI for scalable modeling.

## Technical Architecture

### Data Preprocessing Pipeline

The core of this demo is a comprehensive data preprocessing pipeline designed to clean raw call center datasets:

- **Data Ingestion**:
  - Parses CSVs with `pd.read_csv`, using `io.StringIO` for embedded data, with explicit `quotechar` and `escapechar` to handle complex strings.
  - Handles datasets with columns: `call_id`, `question`, `answer`, `language`.

- **Junk Data Cleanup**:
  - **Null Handling**: Drops rows with missing `question` or `answer` using `df.dropna()`.
  - **Duplicate Removal**: Eliminates redundant FAQs via `df[~df['question'].duplicated()]`.
  - **Short Entry Filtering**: Excludes questions <10 chars or answers <20 chars with `df[(df['question'].str.len() >= 10) & (df['answer'].str.len() >= 20)]`.
  - **Malformed Detection**: Uses regex (`[!?]{2,}|\b(Invalid|N/A)\b`) to filter invalid questions.
  - **Standardization**: Normalizes text (e.g., "mo" to "month") and fills missing `language` with "en".

- **Output**:
  - Generates `cleaned_call_center_faqs.csv` for downstream modeling.
  - Provides cleanup stats: nulls removed, duplicates removed, short entries filtered, malformed entries detected.

### Enterprise-Grade Modeling Compatibility

The cleaned dataset is optimized for:

- **Amazon SageMaker**: Ready for training BERT-based models (e.g., `bert-base-uncased`) for intent classification or FAQ retrieval, deployable via SageMaker JumpStart.
- **Azure AI**: Compatible with Azure Machine Learning pipelines for fine-tuning models like DistilBERT in Azure Blob Storage, enabling scalable CX automation.
- **LLM Integration**: Supports fine-tuning LLMs (e.g., `distilgpt2`) for generative tasks, leveraging your FastAPI experience for API-driven inference.

## Performance Monitoring and Visualization

The demo includes a performance monitoring suite:

- **Processing Time Tracking**: Measures data ingestion, cleaning, and output times using `time.perf_counter()`, reported in milliseconds.
- **Cleanup Metrics**: Tracks the number of nulls, duplicates, short entries, and malformed entries removed.
- **Visualization**: Uses Matplotlib to plot a bar chart (`cleanup_stats.png`):
  - Bars: Number of entries removed per category (Nulls, Duplicates, Short, Malformed).
  - Palette: Professional muted colors for enterprise aesthetics.

## Gradio Interface for Interactive Demo

The demo is accessible via Gradio, providing an interactive data preprocessing experience:

- **Input**: Upload a sample call center CSV or use the embedded demo dataset.
- **Outputs**:
  - **Cleaned Dataset**: Download `cleaned_call_center_faqs.csv`.
  - **Cleanup Stats**: Detailed breakdown (e.g., “Cleaned FAQs: 6; removed 4 junk entries: 2 nulls, 1 duplicates, 1 short, 0 malformed”).
  - **Performance Plot**: Visual metrics for processing time and cleanup stats.
- **Styling**: Custom dark theme CSS (`#2a2a2a` background, blue buttons) for a sleek, enterprise-ready UI.

## Setup

- Clone this repository to a Hugging Face Model repository (free tier, public).
- Add `requirements.txt` with dependencies (`gradio==4.44.0`, `pandas==2.2.3`, `matplotlib==3.9.2`, etc.).
- Upload `app.py` (includes embedded demo dataset for seamless deployment).
- Configure to run with Python 3.9+, CPU hardware (no GPU).

## Usage

- **Upload CSV**: Provide a call center CSV in the Gradio UI, or use the default demo dataset.
- **Output**:
  - **Cleaned Dataset**: Download the processed `cleaned_call_center_faqs.csv`.
  - **Cleanup Stats**: “Cleaned FAQs: 6; removed 4 junk entries: 2 nulls, 1 duplicates, 1 short, 0 malformed”.
  - **Performance Plot**: Visual metrics for processing time and cleanup stats.

**Example**:
- **Input CSV**: Sample dataset with 10 FAQs, including 2 nulls, 1 duplicate, 1 short entry.
- **Output**:
  - Cleaned Dataset: 6 FAQs in `cleaned_call_center_faqs.csv`.
  - Cleanup Stats: “Cleaned FAQs: 6; removed 4 junk entries: 2 nulls, 1 duplicates, 1 short, 0 malformed”.
  - Plot: Processing Time (Ingestion: 50ms, Cleaning: 30ms, Output: 10ms), Cleanup Stats (Nulls: 2, Duplicates: 1, Short: 1, Malformed: 0).

## Technical Details

**Stack**:
- **Pandas**: Data wrangling and preprocessing for call center CSVs.
- **Gradio**: Interactive UI for real-time data preprocessing demos.
- **Matplotlib**: Performance visualization with bar charts.
- **FastAPI Compatibility**: Designed with API-driven preprocessing in mind, leveraging your experience with FastAPI for scalable deployments.

**Free Tier Optimization**: Lightweight with CPU-only dependencies, no GPU required.

**Extensibility**: Ready for integration with RAG/CAG pipelines, and cloud deployments on AWS Lambda or Azure Functions.

## Purpose

This demo showcases expertise in data preprocessing for AI-driven CX automation, focusing on call center data quality. Built on over 5 years of experience in AI, data engineering, and enterprise-grade deployments, it demonstrates the power of Pandas-based data cleaning for RAG/CAG pipelines, making it ideal for advanced CX solutions in call center environments.

## Latest Update

**Status Update**: Placeholder update - January 01, 2025 📝  
- Placeholder update text.

## Future Enhancements

- **Real-Time Streaming**: Add support for real-time data streaming from Kafka for live preprocessing.
- **FastAPI Deployment**: Expose preprocessing pipeline via FastAPI endpoints for production-grade use.
- **Advanced Validation**: Implement stricter data validation rules using machine learning-based outlier detection.
- **Cloud Integration**: Enhance compatibility with AWS Glue or Azure Data Factory for enterprise data pipelines.

**Website**: https://ghostainews.com/  
**Discord**: https://discord.gg/BfA23aYz