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-0.1.27.tar.gz (4.5 kB view details)

Uploaded Source

Built Distribution

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

mimicx-0.1.27-py2.py3-none-any.whl (5.7 kB view details)

Uploaded Python 2Python 3

File details

Details for the file mimicx-0.1.27.tar.gz.

File metadata

  • Download URL: mimicx-0.1.27.tar.gz
  • Upload date:
  • Size: 4.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.9.6

File hashes

Hashes for mimicx-0.1.27.tar.gz
Algorithm Hash digest
SHA256 b5da2a9159a53a57ae6131884dbed17a63290f30c98424ac035258f91274f558
MD5 dc4fafd28f8b8793bbd87d8418fe8641
BLAKE2b-256 f25e61be26244e7b8a2dae9189e7098ecd46fe680cfc956c396b5b4370dff25c

See more details on using hashes here.

File details

Details for the file mimicx-0.1.27-py2.py3-none-any.whl.

File metadata

  • Download URL: mimicx-0.1.27-py2.py3-none-any.whl
  • Upload date:
  • Size: 5.7 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-0.1.27-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 b2c6bf059a59d7dfa6e223a2e69daee4218a074fb29de102cfaa6b9734ac86b4
MD5 1ab6b681d35241725c4d1b416662eb6e
BLAKE2b-256 82e3635c1b6532402c5c7c5503310e085d2de93a463363c09793795f2ede2381

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