LLM_Engineering_OLD/week3/community-contributions/Synthetic Dataset Generator

🤖 Synthetic Dataset Generator

AI-Powered Synthetic Data Generation with Claude 3 Haiku

📥 Installation

1️⃣ Clone the Repository

git clone https://github.com/yourusername/synthetic-dataset-generator.git
cd synthetic-dataset-generator

2️⃣ Create Virtual Environment (Recommended)

# Windows
python -m venv venv
venv\Scripts\activate

# macOS/Linux
python3 -m venv venv
source venv/bin/activate

3️⃣ Install Dependencies

pip install -r requirements.txt

Requirements file (requirements.txt):

gradio>=4.0.0
anthropic>=0.25.0
pandas>=1.5.0
python-dotenv>=1.0.0
httpx==0.27.2

4️⃣ Set Up API Key

Create a .env file in the project root:

# .env
ANTHROPIC_API_KEY=your_api_key_here

Note

: Never commit your .env file to version control. Add it to .gitignore.

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🚀 Usage

Running the Application

python app.ipynb

The Gradio interface will launch at http://localhost:7860

Basic Workflow

1. Enter API Key (if not in .env)
2. Describe Your Schema in plain English
3. Set Number of Records (1-200)
4. Add Example Format (optional, but recommended)
5. Click Generate 🎉
6. Download CSV when ready

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📝 Example Schemas

👥 Customer Data

Generate customer data with:
- customer_id (format: CUST-XXXX)
- name (full name)
- email (valid email address)
- age (between 18-80)
- city (US cities)
- purchase_amount (between $10-$1000)
- join_date (dates in 2023-2024)
- subscription_type (Free, Basic, Premium)

👨‍💼 Employee Records

Generate employee records with:
- employee_id (format: EMP001, EMP002, etc.)
- name (full name)
- department (Engineering, Sales, Marketing, HR, Finance)
- salary (between $40,000-$150,000)
- hire_date (between 2020-2024)
- performance_rating (1-5)
- is_remote (true/false)

🛒 E-commerce Products

Generate e-commerce product data with:
- product_id (format: PRD-XXXX)
- product_name (creative product names)
- category (Electronics, Clothing, Home, Books, Sports)
- price (between $5-$500)
- stock_quantity (between 0-1000)
- rating (1.0-5.0)
- num_reviews (0-500)
- in_stock (true/false)

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🎯 Advanced Usage

Batch Generation

For datasets larger than 50 records, the tool automatically:

• Splits generation into batches of 50
• Combines results into a single dataset
• Prevents API timeout issues

Custom Formats

Provide example JSON to guide the output format:

{
  "id": "USR-001",
  "name": "Jane Smith",
  "email": "jane.smith@example.com",
  "created_at": "2024-01-15T10:30:00Z"
}

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🔧 Troubleshooting

❌ Error: proxies keyword argument

Solution: Downgrade httpx to compatible version

pip install "httpx==0.27.2"

Then restart your Python kernel/terminal.

❌ API Key Not Found

Solutions:

1. Check .env file exists in project root
2. Verify ANTHROPIC_API_KEY is spelled correctly
3. Ensure no extra spaces in the .env file
4. Restart the application after creating .env

❌ JSON Parsing Error

Solutions:

1. Make your schema description more specific
2. Add an example format
3. Reduce the number of records per batch
4. Check your API key has sufficient credits

❌ Rate Limit Errors

Solutions:

1. Reduce batch size in code (change batch_size=50 to batch_size=20)
2. Add delays between batches
3. Upgrade your Anthropic API plan

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📊 Output Format

DataFrame Preview

View generated data directly in the browser with scrollable table.

CSV Download

• Automatic CSV generation
• Proper encoding (UTF-8)
• No index column
• Ready for Excel, Pandas, or any data tool

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🧑‍💻 Skill Level

Beginner Friendly ✅

• No ML/AI expertise required
• Basic Python knowledge helpful
• Simple natural language interface
• Pre-configured examples included

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💡 Tips for Best Results

1. Be Specific: Include data types, ranges, and formats
2. Use Examples: Provide sample JSON for complex schemas
3. Start Small: Test with 5-10 records before scaling up
4. Iterate: Refine your schema based on initial results
5. Validate: Check the first few records before using the entire dataset

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🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Open a Pull Request

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🙏 Acknowledgments

• Anthropic for the Claude API
• Gradio for the UI framework
• Pandas for data manipulation

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📞 Support

• 📧 Email: udayslathia16@gmail.com

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🔗 Related Projects

• Claude API Documentation
• Gradio Documentation
• Pandas Documentation

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Made with ❤️ using Claude 3 Haiku

⭐ Star this repo if you find it useful!