typepeek 0.1.0

Infer type hints from runtime Python data

📦 typepeek

typepeek is a lightweight Python package that infers accurate, human-readable type hints from runtime data — including nested and complex containers like lists, dictionaries, tuples, sets, and even third-party objects like PyTorch tensors.

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🚀 Quick Start

Installation

pip install typepeek

Example Usage

from typepeek import infer_type
import torch

data = [torch.tensor(1), torch.tensor(2), 3]
print(infer_type(data))
# Output: List[Union[torch.Tensor, int]]

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✨ Features

• ✅ Precise type hint inference for dynamic values
• 🔁 Supports nested containers (e.g., List[Dict[str, Tuple[int, float]]])
• 🧠 Handles heterogeneous collections using Union[...]
• 🧹 Compatible with third-party objects (like torch.Tensor, np.ndarray, ...)
• ⚙️ Extensible and easy to integrate in tooling, type-checking, or docs

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📚 Examples

infer_type([1, 2, 3])
# List[int]

infer_type(["a", 1, 3.14])
# List[Union[str, int, float]]

infer_type({"name": "Alice", "age": 30})
# Dict[str, Union[str, int]]

infer_type((1, "hello", 3.5))
# Tuple[int, str, float]

infer_type([[1, 2], [3, 4]])
# List[List[int]]

infer_type([torch.tensor(1), torch.tensor(2)])
# List[torch.Tensor]

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🛠 Use Cases

• 📦 Auto-generate type hints for untyped or runtime-generated data
• 🧪 Write better tests for dynamic outputs
• 📄 Assist in documenting APIs and functions
• 🧠 Debug and inspect complex runtime object structures

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🔮 Roadmap

• CLI support (e.g., typepeek file.json)
• String output mode (as_str=True)
• Support for Optional, TypedDict, NamedTuple, etc.
• Type alias simplifier for long Union[...] types
• JSON Schema export

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🙌 Contributing

Contributions are welcome! If you have an idea, bug, or feature request, feel free to open an issue or submit a pull request.

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📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

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👤 Author

👨‍💻 Le Hoang Viet
🐙 GitHub: Mikyx-1