Project Overview

DataWizzy is an open-source Python package designed to assist data scientists and analysts by transforming natural language questions or requests into detailed, step-by-step instructional guides. Leveraging Large Language Models (LLMs), the tool interprets user queries related to data science and analytics and generates comprehensive guides that include explanations and code snippets using libraries like pandas, NumPy, Matplotlib, and Seaborn.

Key Features

1. Natural Language Understanding

Query Interpretation: Accepts natural language questions or requests such as "How do I perform a linear regression analysis on my dataset?" or "What are the steps to clean missing data in pandas?"

Contextual Understanding: Understands the context and intent behind the queries to provide relevant instructions.

2. Step-by-Step Instructional Guides

Detailed Explanations: Provides comprehensive explanations for each step involved in accomplishing the task.

Code Snippets: Includes executable code examples that users can copy and run in their own environments.

Best Practices: Recommends industry best practices and common pitfalls to avoid.

3. Integration with Data Science Libraries

pandas, NumPy, Matplotlib, Seaborn: Generates instructions and code using these libraries for data manipulation and visualization.

Scikit-learn: Provides guides on machine learning tasks such as regression, classification, and clustering.

4. Interactive Interface

Command-Line Interface (CLI): Users can input their questions directly into the CLI and receive instructional guides.

Jupyter Notebook Extension: An extension that allows users to generate guides within their notebooks.

Web-Based Interface: A user-friendly web app where users can input queries and receive formatted guides.

5. LLM Integration with Safety Measures

Large Language Models: Utilizes models like OpenAI's GPT-4 or open-source alternatives for generating content.

Safety Checks: Implements content filtering and code safety checks to prevent harmful instructions.

Verification Step: Allows users to review the generated instructions and code before use.

Development Plan

Phase 1: Project Setup

Repository Initialization

Create GitHub Repository: Initialize a repository named DataWizzy.

Documentation: Add a detailed README.md explaining the project's purpose, features, and contribution guidelines.

Environment Setup

Python Environment: Set up a virtual environment and install necessary packages.

Dependencies

pandas

numpy

matplotlib

seaborn

scikit-learn

jupyter

transformers (for LLM integration)

openai (if using OpenAI's API)

Phase 2: Core Functionality Development

1. Natural Language Processing Module

LLM Integration: Integrate an LLM to interpret user queries.

Prompt Engineering: Craft prompts that guide the LLM to generate step-by-step guides.

2. Instruction Generation

Content Structuring: Develop a system to structure the LLM's output into clear, logical steps.

Code Generation: Extract and format code snippets from the LLM's response.

3. Safety and Compliance

Content Filtering: Implement mechanisms to filter out inappropriate content.

Code Safety: Analyze generated code for potential security risks.

Phase 3: Interface Development

1. Command-Line Interface (CLI)

Interactive CLI: Build a CLI tool where users can input questions and receive guides.

Formatting: Ensure the output is well-formatted with clear steps and code blocks.

2. Jupyter Notebook Extension

Magic Commands: Create magic commands (e.g., %datawizzy) to generate guides within notebooks.

Inline Display: Show the instructional guides directly in the notebook cells.

3. Web-Based Interface

Web App Development: Use Streamlit or Flask to develop a web app.

User Input: Provide an input field for questions.

Output Display: Present the guides with proper formatting and syntax highlighting.

Phase 4: Testing and Validation

1. Use Case Scenarios

Test Queries: Develop a diverse set of test queries covering various topics.

Validation: Ensure the generated guides are accurate and helpful.

2. User Feedback Loop

Feedback Mechanism: Implement a way for users to provide feedback on the guides.

Iterative Improvement: Use feedback to refine the system continuously.

Phase 5: Documentation and Examples

1. Comprehensive Documentation

User Guides: Write detailed documentation on how to install and use the tool.

API Documentation: Provide documentation for developers.

2. Examples and Tutorials

Sample Queries: Include examples of queries and the resulting guides.

Tutorial Videos: Create videos demonstrating how to use the tool effectively.

Implementation Details

1. Environment Setup

a. Install Required Packages

Use pip to install necessary packages

pip install pandas numpy matplotlib seaborn scikit-learn jupyter transformers openai

OR

poetry add pandas numpy matplotlib seaborn scikit-learn jupy
ter transformers openai

b. Set Up Virtual Environment

bash

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python -m venv venv

source venv/bin/activate # On Windows use venv\Scripts\activate

2. Project Structure

Your project directory should look like this

arduino

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DataWizzy/

├── datawizzy/

│ ├── init.py

│ ├── nlp_processor.py

│ ├── instruction_generator.py

│ ├── safety.py

│ └── interfaces/

│ ├── cli.py

│ ├── jupyter_extension.py

│ └── web_app.py

├── tests/

│ ├── test_nlp_processor.py

│ ├── test_instruction_generator.py

│ └── test_safety.py

├── examples/

├── docs/

├── README.md

├── requirements.txt

├── setup.py

└── LICENSE

3. Core Modules

a. NLP Processor (nlp_processor.py)

Handles the interaction with the LLM.

python

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import openai

class NLPProcessor

def init(self, api_key)

openai.api_key = api_key

def generate_instructions(self, query)

prompt = f"Provide a detailed, step-by-step guide on how to {query} using Python."

response = openai.Completion.create(

engine="text-davinci-003",

prompt=prompt,

max_tokens=1000,

temperature=0.5,

)

return response.choices[0].text.strip()

b. Safety Module (safety.py)

Ensures that generated content is appropriate and safe.

python

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class SafetyChecker

def check_content(self, content)

Implement checks for disallowed content

Return True if safe, False otherwise

pass

def check_code(self, code)

Analyze code for potential security risks

Return True if safe, False otherwise

pass

c. Instruction Generator (instruction_generator.py)

Processes the LLM output and formats it.

python

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class InstructionGenerator

def format_instructions(self, raw_text)

Process the raw text to improve formatting

Split into steps, highlight code blocks, etc.

return formatted_text

4. Interfaces

a. Command-Line Interface (cli.py)

python

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import argparse

from datawizzy.nlp_processor import NLPProcessor

from datawizzy.instruction_generator import InstructionGenerator

from datawizzy.safety import SafetyChecker

def main()

parser = argparse.ArgumentParser(description='DataWizzy CLI')

parser.add_argument('query', type=str, help='Your data science question')

args = parser.parse_args()

nlp = NLPProcessor(api_key='your-api-key')

safety = SafetyChecker()

generator = InstructionGenerator()

raw_instructions = nlp.generate_instructions(args.query)

if safety.check_content(raw_instructions)

instructions = generator.format_instructions(raw_instructions)

print(instructions)

else

print("The generated content was deemed unsafe.")

if name == 'main'

main()

b. Jupyter Notebook Extension (jupyter_extension.py)

python

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from IPython.core.magic import register_line_magic

from datawizzy.nlp_processor import NLPProcessor

from datawizzy.instruction_generator import InstructionGenerator

from datawizzy.safety import SafetyChecker

from IPython.display import display, Markdown

@register_line_magic

def datawizzy(line)

query = line.strip()

nlp = NLPProcessor(api_key='your-api-key')

safety = SafetyChecker()

generator = InstructionGenerator()

raw_instructions = nlp.generate_instructions(query)

if safety.check_content(raw_instructions)

instructions = generator.format_instructions(raw_instructions)

display(Markdown(instructions))

else

print("The generated content was deemed unsafe.")

c. Web-Based Interface (web_app.py)

python

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import streamlit as st

from datawizzy.nlp_processor import NLPProcessor

from datawizzy.instruction_generator import InstructionGenerator

from datawizzy.safety import SafetyChecker

st.title("DataWizzy")

query = st.text_input("Enter your data science question:")

if st.button("Generate Guide")

nlp = NLPProcessor(api_key='your-api-key')

safety = SafetyChecker()

generator = InstructionGenerator()

raw_instructions = nlp.generate_instructions(query)

if safety.check_content(raw_instructions)

instructions = generator.format_instructions(raw_instructions)

st.markdown(instructions)

else

st.error("The generated content was deemed unsafe.")

5. Testing

a. Write Unit Tests (tests/)

Test NLP Processor: Ensure that queries are properly sent and responses are received.

Test Instruction Generator: Check that formatting is applied correctly.

Test Safety Module: Verify that unsafe content is correctly identified.

python

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import unittest

from datawizzy.safety import SafetyChecker

class TestSafetyChecker(unittest.TestCase)

def test_safe_content(self)

safety = SafetyChecker()

content = "This is a safe instruction."

self.assertTrue(safety.check_content(content))

def test_unsafe_content(self)

safety = SafetyChecker()

content = "This is unsafe content."

self.assertFalse(safety.check_content(content))

if name == 'main'

unittest.main()

6. Documentation

API Reference: Use docstrings and tools like Sphinx to generate documentation.

User Guides: Provide step-by-step instructions on installation and usage.

Examples: Include practical examples in the examples/ directory.

Example Usage

1. Command-Line Interface

bash

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python cli.py "clean a dataset with missing values using pandas"

Output

kotlin

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Step 1: Import pandas library

import pandas as pd

Step 2: Load your dataset



python

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df = pd.read_csv('your_dataset.csv')

Step 3: Identify missing values



python

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print(df.isnull().sum())

Step 4: Drop rows with missing values



python

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df_cleaned = df.dropna()

Step 5: Alternatively, fill missing values



python

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df_filled = df.fillna(method='ffill')

...



shell

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### **2. Jupyter Notebook**



```python

%load_ext datawizzy



%datawizzy visualize the distribution of 'Age' column using a histogram

## Output



(Displays a step-by-step guide with code snippets on how to create a histogram of the 'Age' column.)



3. Web App

Step 1: Run the web app

bash

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streamlit run web_app.py

Step 2: Open the provided URL in your browser.

Step 3: Enter your question in the input field.

Step 4: Click "Generate Guide" to receive the instructional guide.