semvec/pypi-packages huggingface dataset:
hatch-django-collectstatic 0.0.4
Build, collect and distribute django static files
Requires Python: >=3.11
Keywords: collectstatic, django, hatch, hatchling

# hatch-django-collectstatic

Collect a django project's static files into its binary distribution
archive.

Example usage in `pyproject.toml`:

```toml
[build-system]
requires = ["hatchling >= 1.26", "hatch-django-collectstatic"]
build-backend = "hatchling.build"

[tool.hatch.build.targets.wheel.hooks.django-collectstatic]
settings = "my_django_package.project.settings"

# If you have installed Django apps from packages besides Django,
# you'll need to enable the require-runtime-dependencies feature of
# hatch in order to make these packages available when collecting
# static files.
# Default value of require-runtime-dependencies = false
# require-runtime-dependencies = true

# Output directory where static files are collected in the target.
# Default value of output-directory = "static"
# output-directory = "custom-static-directory"

[tool.hatch.build.targets.wheel.shared-data]
# Mapping from output directory to the distribution path.  The
# following configuration will place the files in the .data directory
# of the wheel, under "data/share/static".
# See https://packaging.python.org/en/latest/specifications/binary-distribution-format/#the-data-directory
"static" = "share/static"
```
gpt-pydantic-tools 0.3
Define GPT tool schemas using Pydantic.
Requires Python: >=3.12
Keywords: gpt, ai, tool, schemas, pydantic, json

# GPT Pydantic Tools

The `gpt_pydantic_tools` repository offers a Python module designed to integrate Pydantic models with GPT-style tool schemas. It facilitates the transformation of Pydantic models into a format that is suitable for use with GPT-4's tools functionality, ensuring structured data and tool interaction consistency.

## Features

- **Model Conversion:** Convert Pydantic models into GPT-4 tool schemas.
- **Flexible Tool Choice Handling:** Provides several strategies for tool invocation such as auto, required, and none, based on the context or specific requirements.

## Installation

Install directly using uv:
```bash
uv add gpt-pydantic-tools
```

## Usage

1. **Define Pydantic Models:** Create your Pydantic models as per your requirements.
2. **Convert to GPT Tool Schema:** Use the `ToolSchemaManager` to convert your Pydantic models into GPT tool schemas.
3. **Tool Choice Management:** Utilize the `get_tool_choice_dict` to manage how tools are chosen for execution based on the defined strategies.

Example usage:
```python
from pydantic import BaseModel
from gpt_pydantic_tools import ToolSchemaManager, get_tool_choice_dict, ToolChoiceEnum

class MyModel(BaseModel):
    name: str
    age: int

# Convert Pydantic model to GPT tool schema
schema_manager = ToolSchemaManager(pydantic_obj=MyModel)
tool_schema = schema_manager.tools_schema

# Determine tool invocation strategy
tool_choice = get_tool_choice_dict(ToolChoiceEnum.AUTO, schema_manager)
```

If you want to do the Pydantic Schema conversion yourself, and directly provide the JSON instead, you can do:
```
schema_manager = ToolSchemaManager(pydantic_obj_json_schema=my_model_json_schema)
```

## Contributing

Contributions are welcome! Please feel free to submit pull requests, create issues, and suggest improvements to the repository.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
dagviz 0.5.0
Visualization for DAGs
Requires Python: <4,>=3.10

# DAGVIZ
DAGVIZ offers a "git log"-like visualization for DAGs represented in networkx.

## Installation

DAGVIZ can be installed with `pip`:

```bash
pip install dagviz
```

## Documentation

Documentation is available at https://wimyedema.github.io/dagviz/.
## Usage

DAGVIZ relies on networkx for the representation and manipulation of the
DAG. An SVG file can be generated as follows:

```py
    import dagviz
    import networkx as nx
    # Create the DAG
    G = nx.DiGraph()
    for i in range(5):
        G.add_node(f"n{i}")
    G.add_edge(f"n0", f"n1")
    G.add_edge(f"n0", f"n2")
    G.add_edge(f"n0", f"n4")
    G.add_edge(f"n1", f"n3")
    G.add_edge(f"n2", f"n3")
    G.add_edge(f"n3", f"n4")
    # Create an SVG as a string
    r = dagviz.render_svg(G)
    with open("simple.svg", "wt") as fs:
        fs.write(r)
```
iflow-mcp_officehub-tech-llc-server 0.1.1
Add your description here
Requires Python: >=3.11

MCP Server Integration with Zoho CRM

This guide walks you through setting up an MCP (Modular Command Platform) server using Python and Node.js, integrating with Claude (via the Pro plan), and preparing your development environment in Visual Studio Code (VS Code). This setup can later be extended to communicate with Zoho CRM for conversational AI capabilities.

🚀 Prerequisites
Make sure you have the following tools installed:
- Python – A Programming language used for the server.
- Node.js – Required for backend tooling.
- Visual Studio Code (VS Code) – Code editor.
- Claude Pro Plan – Required for AI integration.
- 
📦 Installation & Project Setup
Step 1: Create a New Project Folder
1. Open Visual Studio Code.
2. Create a new folder for your MCP project.
3. Open a terminal in VS Code by navigating to: Menu > Terminal > New Terminal.
4. 
Step 2: Install uv and Initialize the Project
Install uv, a Python package manager, by running the following in PowerShell:
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
Then, initialize your MCP project:
uv init mcp-server-demo
cd mcp-server-demo

Step 3: Add MCP to Project Dependencies
Add MCP to your project’s dependencies using:
uv add "mcp[cli]"

Step 4: Install MCP Server Script
Install the MCP server with your main script (e.g., main.py):
uv run mcp install main.py

⚡ Quickstart Example: Create a Simple MCP Server
Create a file named server.py and add the following code:

# Import MCP server
from mcp.server.fastmcp import FastMCP

# Create an MCP server
mcp = FastMCP("Demo")

# Add an addition tool
@mcp.tool()
def add(a: int, b: int) -> int:
    """Add two numbers"""
    return a + b

# Add a dynamic greeting resource
@mcp.resource("greeting://{name}")
def get_greeting(name: str) -> str:
    """Get a personalized greeting"""
    return f"Hello, {name}!"

if __name__ == "__main__":
    mcp.run()


Run the server:
python server.py

⚠️ Disclaimer
Use this code at your own risk. Officehub Tech is not responsible for any issues, data loss, or damages that may arise from its use.