Metadata-Version: 2.4
Name: FastWrite
Version: 1.2.1
Summary: A module for generating AI-based code documentation and data flow diagrams.
Author-email: R-G-KJSIT <r.giridhar@somaiya.edu>
License: MIT License
        
        Copyright (c) 2025 R
        
        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.
        
Project-URL: Homepage, https://fastwrite-py.vercel.app/
Project-URL: Repository, https://github.com/R-G-KJSIT/FastWrite
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: groq
Requires-Dist: google-genai
Requires-Dist: requests
Requires-Dist: nltk
Requires-Dist: python-dotenv
Requires-Dist: openai
Requires-Dist: rouge_metric
Dynamic: license-file

# FastWrite
Python Module for AI-Assisted Documentation 

## Current Statistics:
- [![PyPI Downloads](https://static.pepy.tech/badge/fastwrite)](https://pepy.tech/projects/fastwrite)
- [![PyPI](https://badge.fury.io/py/fastwrite.svg)](https://badge.fury.io/py/fastwrite)
- [Official Website](https://fastwrite-py.vercel.app)

## Overview
This module provides functionality to:
- **Process Code Files**: Extract and list Python files from a ZIP archive.
- **Generate Data Flow Diagrams**: Create a data flow chart (in Graphviz format) by analyzing Python code using the AST module.
- **Generate Documentation**: Produce detailed documentation for Python code using multiple AI models:
  - Groq-based models (remote)
  - Gemini-based models (remote)
  - OpenAI-based models (remote)
  - OpenRouter-based models (remote)
  - Ollama-based models (local)
- **Evaluate Documentation Quality**: Compute BLEU/ROUGE scores to compare generated documentation against a reference document.


## Installation

### Requirements
- Python 3.11
- [groq](https://pypi.org/project/groq/)
- [google-genai](https://pypi.org/project/google-genai/)
- [openai](https://pypi.org/project/openai/)
- [requests](https://pypi.org/project/requests/)
- [nltk](https://pypi.org/project/nltk/)
- [python-dotenv](https://pypi.org/project/python-dotenv/)
- [rouge_metric](https://pypi.org/project/rouge-metric/)

### Install Dependencies
```bash
pip install groq google-genai requests nltk python-dotenv openai rouge_metric
```

## Usage

### Processing Files:
```
from FastWrite import extract_zip, list_python_files, read_file
import tempfile
import os

# Specify the path to your ZIP file containing Python code
zip_file_path = "path/to/your/code.zip"

with tempfile.TemporaryDirectory() as tmp_dir:
    # Extract the ZIP file
    extract_zip(zip_file_path, tmp_dir)
    
    # List Python files in the extracted directory
    py_files = list_python_files(tmp_dir)
    
    if py_files:
        # For example, choose the first Python file as the main file
        main_file_path = os.path.join(tmp_dir, py_files[0])
        code_content = read_file(main_file_path)

```

### Generating Data Flow Diagrams:

```
from FastWrite import generate_data_flow

# Generate Graphviz code for the data flow diagram
graphviz_code = generate_data_flow(code_content)
print(graphviz_code)

```

### Generating Documentation (Express Mode):
```
# Normal Mode:
py -m FastWrite --LLM_NAME

# With custom code file
py -m FastWrite code_filename.py --LLM_NAME

# Simplified (Novice Friendly) Mode:
py -m FastWrite code_filename.py --LLM_NAME --Simplify

# Formal (Precise and to the Point) Mode:
py -m FastWrite code_filename.py --LLM_NAME --Formal

# Research (In-Depth and Elaborate Documentation):
py -m FastWrite code_filename.py --LLM_NAME --Research

# Custom (Allows you to feed a custom prompt of your own):
py -m FastWrite code_filename.py --LLM_NAME --Custom-Prompt "Enter Custom Prompt here"
```


### Generating Documentation (Groq):

```
from FastWrite import generate_documentation_groq

custom_prompt = """
Objective:
Generate high-quality, developer-friendly documentation for the following Python code Ensure you include Detailed function-level and file-level documentation and a high level slightly less technical documentation at the start to make it friendly. Do not print full code snippets of existing code, just explain them:
"""

groq_api_key = "your_groq_api_key"
groq_model = "deepseek-r1-distill-llama-70b"  # Replace with your desired model

doc_groq = generate_documentation_groq(code_content, custom_prompt, groq_api_key, groq_model)
print(doc_groq)

```

### Generating Documentation (Gemini):

```
from FastWrite import generate_documentation_gemini

custom_prompt = """
Objective:
Generate high-quality, developer-friendly documentation for the following Python code Ensure you include Detailed function-level and file-level documentation and a high level slightly less technical documentation at the start to make it friendly. Do not print full code snippets of existing code, just explain them:
"""

gemini_api_key = "your_gemini_api_key"
gemini_model = "gemini-2.0-flash"  # Replace with your desired model

doc_gemini = generate_documentation_gemini(code_content, custom_prompt, gemini_api_key, gemini_model)
print(doc_gemini)

```

### Generating Documentation (OpenAI):

```
from FastWrite import generate_documentation_openai

custom_prompt = """
Objective:
Generate high-quality, developer-friendly documentation for the following Python code Ensure you include Detailed function-level and file-level documentation and a high level slightly less technical documentation at the start to make it friendly. Do not print full code snippets of existing code, just explain them:
"""
doc_openai = generate_documentation_openai(code_content, custom_prompt)
print(doc_openai)

```

### Generating Documentation (Ollama):

```
from FastWrite import generate_documentation_ollama

custom_prompt = """
Objective:
Generate high-quality, developer-friendly documentation for the following Python code Ensure you include Detailed function-level and file-level documentation and a high level slightly less technical documentation at the start to make it friendly. Do not print full code snippets of existing code, just explain them:
"""

# Replace with your local Ollama model name (e.g., "ollama-llama-70b")
ollama_model = "ollama-llama-70b"

doc_ollama = generate_documentation_ollama(code_content, custom_prompt, ollama_model)
print(doc_ollama)

```

### Generating Documentation (OpenRouter):

```
from FastWrite import generate_documentation_openrouter

custom_prompt = """
Objective:
Generate high-quality, developer-friendly documentation for the following Python code Ensure you include Detailed function-level and file-level documentation and a high level slightly less technical documentation at the start to make it friendly. Do not print full code snippets of existing code, just explain them:
"""
doc_openrouter = generate_documentation_openrouter(code_content, custom_prompt)
print(doc_openrouter)

```

### Calculating Bleu Score:

```
from FastWrite import calculate_bleu

# Provide a reference documentation string for comparison
reference_doc = "Your reference documentation text here..."

# Basic BLEU score calculation
bleu_score = calculate_bleu(doc_llm_host, reference_doc) ##LLM host may include Groq,Gemini,OpenAI or Ollama
print("BLEU Score:", bleu_score)

# BLEU score with smoothing
# Available smoothing methods: 'method0', 'method1', 'method2', 'method3', 'method4', 'method5', 'method6', 'method7'
bleu_score_smoothed = calculate_bleu(doc_llm_host, reference_doc, smoothing_method='method1')
print("BLEU Score with Smoothing:", bleu_score_smoothed)

# Calculate BLEU score with multiple reference documents
from FastWrite import calculate_bleu_multi_reference
reference_docs = ["Reference doc 1...", "Reference doc 2..."]
bleu_score_multi = calculate_bleu_multi_reference(doc_llm_host, reference_docs, smoothing_method='method1')
print("BLEU Score with Multiple References:", bleu_score_multi)

# Calculate BLEU score directly from files
from FastWrite import calculate_bleu_from_files
bleu_score_from_files = calculate_bleu_from_files("candidate_doc.txt", "reference_doc.txt", smoothing_method='method1')
print("BLEU Score from Files:", bleu_score_from_files)

```


### Calculating Rouge Score:
```
from FastWrite.rouge import calculate_rouge

# Provide a reference documentation string for comparison
reference_doc = "Your reference documentation text here..."

# Calculate Rouge
rouge = calculate_rouge(doc_llm_host,reference_doc)
```

### Generating README File:

```
from FastWrite.print import readmegen

readmegen(doc_llm,llm_used)
```
