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-0.1.69.tar.gz (11.8 kB view details)

Uploaded Source

Built Distribution

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

mimicx-0.1.69-py2.py3-none-any.whl (17.7 kB view details)

Uploaded Python 2Python 3

File details

Details for the file mimicx-0.1.69.tar.gz.

File metadata

  • Download URL: mimicx-0.1.69.tar.gz
  • Upload date:
  • Size: 11.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.9.6

File hashes

Hashes for mimicx-0.1.69.tar.gz
Algorithm Hash digest
SHA256 815f64211ef2b8f6a30d6850c41666dd5c16eb4b9b0ce8e0f63b7f99ccae11a7
MD5 5271f9d126cf087733a74efee6b76659
BLAKE2b-256 52efe4c448f9b93b383a1f3b02e705da75593bc902a920dd193efef112737671

See more details on using hashes here.

File details

Details for the file mimicx-0.1.69-py2.py3-none-any.whl.

File metadata

  • Download URL: mimicx-0.1.69-py2.py3-none-any.whl
  • Upload date:
  • Size: 17.7 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-0.1.69-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 89c56cee8f534a628ba1330a3c837de70a6e251c989cb388bd5485cfe63b5241
MD5 a0110b4ecf15935e25f46ae6376905b2
BLAKE2b-256 dc83125fa5114f98e837ab04faffb4cdb9cd01151898867b2a428dd19ac88e2a

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