🧠 LLM-Powered Email Extraction App

This Streamlit web app lets you upload

.eml

email files and use Anthropic Claude (via AWS Bedrock) to:

Extract structured JSON data from emails
Automatically generate cleaner, reusable prompts
Compare prompt quality side-by-side in a 4-column interface

🚀 Features

✅ .eml File Upload & Parsing — Extracts
```
text
```
,
```
html
```
, sender, subject, attachments (PDFs included)
🤖 Claude-Powered Extraction — Uses
```
user_prompt
```
to pull out JSON info from emails
✨ Improved Prompt Generator — Claude rewrites your prompt in scalable, few-shot natural language
📊 Prompt Comparison Tool — See a side-by-side markdown table comparing your prompt vs Claude’s
🖼 Fixed 4-Column UI — Horizontal layout: Email Preview | JSON | Improved Prompt | Comparison
🌙 Dark Panel Layout — Panels styled for readability in dark mode, no HTML clutter

🛠️ Technologies Used

Tool	Purpose
Streamlit	Frontend Web App
AWS Bedrock	Claude 3 Haiku (LLM prompt processing)
boto3	Python SDK for AWS
PyPDF2	PDF Attachment Parsing
BeautifulSoup	HTML to Text Conversion
Bleach	HTML Sanitization (optional)

📦 Installation

🐍 Python Environment

git clone https://github.com/rsonic17/llm-prompt-testing-utility.git
cd llm-prompt-testing-utility
python -m venv venv
source venv/bin/activate   # or venv\Scripts\activate on Windows
pip install -r requirements.txt

🔐 Setup AWS Credentials

Ensure you have:

AWS credentials in
```
~/.aws/credentials
```
AWS region and model ID set via environment:

export AWS_REGION=us-east-1
export BEDROCK_MODEL_ID=anthropic.claude-3-haiku-20240307-v1:0

You can also use a

.env

file and

python-dotenv

🧪 Usage

streamlit run app.py

Upload a
```
.eml
```
file
Paste a Claude-compatible prompt (use
```
{email_data}
```
as placeholder)

Click:

```
🧠 Extract with LLM
```
```
✨ Suggest Better Prompt
```
```
📝 Compare Prompts
```

View results in 4 scrollable panels

🔤 Prompt Format (Example)

Paste this in the "User Prompt" box:

Extract the following fields from this email:
- Buyer Name
- Sender Email
- Date
- Payment Amount

Output JSON like:

{
  "buyer": "...",
  "sender": "...",
  "amount": "...",
  "date": "..."
}

Email:
{email_data}

🖼 UI Overview

📸 You can add screenshots here later.

Panel	Purpose
📄 Email Preview	Raw email text
📦 LLM Extracted Data	Claude’s structured JSON
🌟 Improved Prompt	Claude's few-shot rewrite
📑 Prompt Comparison	Markdown table comparison

👥 Contributing

Pull requests are welcome. Please open an issue to discuss any major changes.

📄 License

🚀 Features

✅ .eml File Upload & Parsing — Extracts

text

html

, sender, subject, attachments (PDFs included)

🤖 Claude-Powered Extraction — Uses

user_prompt

to pull out JSON info from emails

✨ Improved Prompt Generator — Claude rewrites your prompt in scalable, few-shot natural language

📊 Prompt Comparison Tool — See a side-by-side markdown table comparing your prompt vs Claude’s

🖼 Fixed 4-Column UI — Horizontal layout: Email Preview | JSON | Improved Prompt | Comparison

🌙 Dark Panel Layout — Panels styled for readability in dark mode, no HTML clutter

Tool

Purpose

Streamlit

Frontend Web App

AWS Bedrock

Claude 3 Haiku (LLM prompt processing)

boto3

Python SDK for AWS

PyPDF2

PDF Attachment Parsing

BeautifulSoup

HTML to Text Conversion

Bleach

HTML Sanitization (optional)

📦 Installation

git clone https://github.com/rsonic17/llm-prompt-testing-utility.git
cd llm-prompt-testing-utility
python -m venv venv
source venv/bin/activate   # or venv\Scripts\activate on Windows
pip install -r requirements.txt

Ensure you have:

AWS credentials in

~/.aws/credentials

AWS region and model ID set via environment:

export AWS_REGION=us-east-1
export BEDROCK_MODEL_ID=anthropic.claude-3-haiku-20240307-v1:0

You can also use a

.env

python-dotenv

Extract the following fields from this email:
- Buyer Name
- Sender Email
- Date
- Payment Amount

Output JSON like:

{
  "buyer": "...",
  "sender": "...",
  "amount": "...",
  "date": "..."
}

Email:
{email_data}

Panel

Purpose

📄 Email Preview

Raw email text

📦 LLM Extracted Data

Claude’s structured JSON

🌟 Improved Prompt

Claude's few-shot rewrite

📑 Prompt Comparison

Markdown table comparison

🧠 LLM-Powered Email Extraction App

🧠 LLM-Powered Email Extraction App

🚀 Features

🛠️ Technologies Used

📦 Installation

🐍 Python Environment

🔐 Setup AWS Credentials

🧪 Usage

🔤 Prompt Format (Example)

🖼 UI Overview

👥 Contributing

📄 License

Related Skills

<h1 align="center">

- Identify gaps

2. Apply Deepthink Protocol (reason about dependencies

🧠 LLM-Powered Email Extraction App

🚀 Features

🛠️ Technologies Used

📦 Installation

🐍 Python Environment

🔐 Setup AWS Credentials

🧪 Usage

🔤 Prompt Format (Example)

🖼 UI Overview

👥 Contributing

📄 License