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metadata

title: Amkyaw AI NLP
emoji: 🤖
colorFrom: blue
colorTo: gray
sdk: gradio
sdk_version: 5.29.0
python_version: '3.12'
app_file: app.py
pinned: false
license: mit
tags:
  - chatbot
  - myanmar
  - burmese
  - nlp
  - llama
  - groq
  - llm
  - conversational-ai
  - local-ai

🤖 Amkyaw AI NLP

မြန်မာစာ AI Chatbot - Powered by Groq & Llama 3.1

📝 Overview

Amkyaw AI NLP is a sophisticated conversational AI chatbot specifically designed for Myanmar (Burmese) language interactions. The system combines local knowledge base matching with advanced LLM capabilities to provide intelligent, contextually appropriate responses in Myanmar Unicode text.

🎯 Core Objectives

✅ Provide natural conversational experiences in Myanmar language
✅ Leverage local dataset for accurate responses to common queries
✅ Utilize Groq-powered Llama 3.1 for complex reasoning tasks
✅ Demonstrate production-grade AI chatbot architecture

✨ Features

Feature	Description
🗣️ Myanmar Language Support	Full Unicode support for Burmese text input and output
🔍 Intelligent Matching	Fuzzy search with 85% similarity threshold
⚡ Groq Integration	High-performance inference with Llama 3.1 8B
🎨 Modern UI	Dark-themed Gradio interface
🔄 Streaming Responses	Real-time token-by-token output
📚 Extensible Knowledge	CSV-based conversation database
🛡️ Error Handling	Graceful fallback responses

🛠️ Technology Stack

Core Technologies

Technology	Version	Purpose
Python	3.12+	Runtime environment
Gradio	5.29.0	Web UI framework
Groq	Latest	LLM inference engine
Pandas	Latest	Data processing

AI/ML Libraries

Library	Purpose
Llama 3.1 8B Instant	Large language model
SequenceMatcher	Fuzzy string matching

⚙️ How It Works

Architecture Flow

User Input → Fuzzy Match Check → [Match Found?] → Yes → Return Local Response
                     ↓ No
              Groq API (Llama 3.1) → Process → Streaming Response

Response Logic

Step 1: Local Knowledge Base

System loads conversation pairs from CSV dataset
Implements fuzzy matching with SequenceMatcher
Returns matching response if similarity > 85%

Step 2: LLM Fallback

For unmatched queries, uses Groq API
Converts chat history to Llama format
Generates contextual response

📁 Project Structure

amkyawdev-nlp/
├── app.py                    # Main Gradio application
├── requirements.txt         # Python dependencies
├── README.md               # This file
├── LICENSE                # MIT License
├── .gitignore             # Git ignore rules
└── .gradioignore          # Gradio ignore rules

File Descriptions

File	Description
`app.py`	Core application with UI and AI logic
`requirements.txt`	pip dependencies
`README.md`	Documentation
`LICENSE`	MIT License

🚀 Installation

Prerequisites

Python 3.12+
pip (包管理器)

Local Setup

# Clone the repository
git clone https://huggingface.co/spaces/amkyawdev/amkyawdev-nlp.git
cd amkyawdev-nlp

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Linux/macOS
# venv\Scripts\activate  # Windows

# Install dependencies
pip install -r requirements.txt

# Set environment variable
export GROQ_API_KEY=your_api_key_here

# Run the application
python app.py

Docker Deployment

FROM python:3.12-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY app.py .
EXPOSE 7860

CMD ["python", "app.py", "--share"]

💻 Usage

Running Locally

# Set your Groq API key
export GROQ_API_KEY=your_key_here

# Launch the app
python app.py

The app will be available at http://localhost:7860

Using on HuggingFace Spaces

Visit: https://amkyawdev-amkyawdev-nlp.hf.space

API Example

import requests

response = requests.post(
    "https://amkyawdev-amkyawdev-nlp.hf.space/predict",
    json={"data": ["ဟောက်ပန်းပါ"]}
)
print(response.json())

🔐 Environment Variables

Variable	Required	Description
`GROQ_API_KEY`	Yes	Groq API authentication token

Getting Your Groq API Key

Visit console.groq.com
Create an account or sign in
Navigate to API Keys
Generate new key
Copy and set as environment variable

📦 Deployment

HuggingFace Spaces

This app is optimized for HF Spaces deployment:

# HF Spaces automatically handles:
# - Container management
# - HTTPS enforcement
# - Traffic distribution
# - Sleep scheduling (after 48h inactivity)

Environment Details

Setting	Value
Runtime	Gradio 5.29.0
Python	3.12
Hardware	CPU Basic
Sleep Time	48 hours

🔒 Security

Best Practices Implemented

✅ API keys stored as secrets (not in code)
✅ HTTPS enforced by Spaces
✅ Input validation
✅ Error message localization
✅ Rate limiting (via Spaces)

Recommendations

Never commit API keys to version control
Rotate keys periodically
Monitor usage in Groq dashboard

📜 License

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

📧 Contact

amkyawdev

🤝 Acknowledgments

Groq - LLM Inference
Gradio - UI Framework
HuggingFace - Infrastructure

⭐ If you find this project useful, please consider giving it a star!