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-0.1.54.tar.gz (11.0 kB view details)

Uploaded Source

Built Distribution

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

mimicx-0.1.54-py2.py3-none-any.whl (17.0 kB view details)

Uploaded Python 2Python 3

File details

Details for the file mimicx-0.1.54.tar.gz.

File metadata

  • Download URL: mimicx-0.1.54.tar.gz
  • Upload date:
  • Size: 11.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.9.6

File hashes

Hashes for mimicx-0.1.54.tar.gz
Algorithm Hash digest
SHA256 147c7b31a44123247601d69f906a8f8079cb00bf87f68470ee7242dd71a1eef3
MD5 e9f2670458e70e7bc14a1243ca061ac5
BLAKE2b-256 1d13fd1af2369b67b3152611590304f6ff0ab700f40c9bfaf89c21f0d967abf5

See more details on using hashes here.

File details

Details for the file mimicx-0.1.54-py2.py3-none-any.whl.

File metadata

  • Download URL: mimicx-0.1.54-py2.py3-none-any.whl
  • Upload date:
  • Size: 17.0 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-0.1.54-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 6f92a43e966a3307c27277daed71ae241edf5c67a0b7cf1198122272ec322454
MD5 7b93b50f18d869b4e5871972c8e72e38
BLAKE2b-256 65661ae0740f3486a474583c5ede61074b96229dfbdb7a35be479243035b01b9

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