Skip to main content

Human-like AI for everyone — vision, text, voice, and more in one simple package.

Project description

MIMICX AI Library 📚

License PyPI version

Create Human-Like AI: Perception, Reasoning, and Interaction in One Library

Mimicx AI is a lightweight Python library for giving machines human-level perception and decision-making capabilities by enabling advanced perception, contextual reasoning, autonomous decision-making, and intuitive human-machine interaction.

At its core, Mimicx structures its capabilities into specialized domains — Vision, Text, Voice, Feel, Twin, Phy, Sense, and Mind — each targeting a unique dimension of machine perception, reasoning, and interaction.

Sample: Face Recognition

from mimicx import Model

# Initialize load model
model = Model("mimicvision/face_recognition")

# Compare faces
result = model.compare_faces(image1_path, image2_path)
print(f"Face similarity: {result:.2f}")
       

Sample: Iris Recognition

from mimicx import Model

# Initialize load model
model = Model("mimicvision/iris_recognition")

# Compare faces
result = model.compare_iris(image1_path, image2_path)
print(f"Iris similarity: {result}")

Notes

  • Ensure that the images exist in the same directory as this script.

  • The MimicX package must be installed and properly configured.

  • The load('face_recognition') call initializes the face recognition model.

✨ Key Features

  • MimicVision: processes and interprets visual data, enabling advanced image and video understanding.

  • MimicText: handles natural language processing and contextual text reasoning.

  • MimicVoice: works with audio signals for speech recognition, synthesis, and voice interaction.

  • MimicFeel: captures and interprets tactile and sensory data to simulate touch and emotion.

  • MimicTwin: creates and manages digital twins, bridging physical and virtual environments.

  • MimicPhy: interfaces with physical systems for autonomous control and robotics.

  • MimicSense: integrates multisensory data streams for comprehensive environmental awareness.

  • MimicMind: enables high-level cognitive functions including planning, decision-making, and adaptive learning.

📦 Installation

The GenAI Processors library requires Python 3.10+.

Install it with:

pip install mimicx

🤝 Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines on how to contribute to this project.

📜 License

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

Mimicx Terms of Services

If you make use of Mimicx via the MimicX library, please ensure you review the Terms of Service.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mimicx-0.1.77.tar.gz (12.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

mimicx-0.1.77-py2.py3-none-any.whl (11.4 kB view details)

Uploaded Python 2Python 3

File details

Details for the file mimicx-0.1.77.tar.gz.

File metadata

  • Download URL: mimicx-0.1.77.tar.gz
  • Upload date:
  • Size: 12.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.9.6

File hashes

Hashes for mimicx-0.1.77.tar.gz
Algorithm Hash digest
SHA256 0ebf2ecb0cd7474b9f4194862fb25fe3dd63e6eeb5ee172a4c0f78844afb5217
MD5 44b02ec0b27c0c18f5548bfd80008c6a
BLAKE2b-256 b924837f881d4fa513906bb8e396e4e0415dec74b8ebb6996ff1a6111e48907a

See more details on using hashes here.

File details

Details for the file mimicx-0.1.77-py2.py3-none-any.whl.

File metadata

  • Download URL: mimicx-0.1.77-py2.py3-none-any.whl
  • Upload date:
  • Size: 11.4 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.9.6

File hashes

Hashes for mimicx-0.1.77-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 6834dde52a99332e4ad39e90b76d3f5b426c364466fb4ff0e3e74f563166c795
MD5 14de3fc86443dd12df4f99c4644920ed
BLAKE2b-256 94f7fa687f8645f2d13e3828e0ac700df6b80b923b1b4e46fabfbc18b0b575f5

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page