moondream 0.1.1

Python client library for moondream

Moondream Python Client Library

Official Python client library for Moondream, a fast multi-function VLM. This client can target either the Moondream Cloud or a Moondream Server. Both are free, though the cloud has a limits on the free tier.

Capabilities

Moondream goes beyond the typical VLM "query" ability to include more visual functions. These include:

• caption: Generate descriptive captions for images
• query: Ask questions about image content
• detect: Find bounding boxes around objects in images
• point: Identify the center location of specified objects in images

You can try this out anytime on Moondream's playground.

Installation

Install the package from PyPI:

pip install moondream

Quick Start

Choose how you want to run it:

1. Moondream Cloud: (with 5,000 free requests/day): get a free API key from the Moondream cloud console.
2. Moondream Server: Run it locally by installing and running the Moondream server.

Once you've done at least one of these, try running this code:

import moondream as md
from PIL import Image

# Initialize for Moondream Cloud
model = md.vl(api_key="<your-api-key>")

# ...or initialize for a local Moondream Server
model = md.vl(endpoint="http://localhost:2020/v1")

# Load an image
image = Image.open("path/to/image.jpg")

# Generate a caption
caption = model.caption(image)["caption"]
print("Caption:", caption)

# Ask a question
answer = model.query(image, "What's in this image?")["answer"]
print("Answer:", answer)

# Stream the response
for chunk in model.caption(image, stream=True)["caption"]:
    print(chunk, end="", flush=True)

API Reference

Constructor

# Cloud inference
model = md.vl(api_key="<your-api-key>")

# Local inference
model = md.vl(endpoint="http://localhost:2020/v1")

Methods

caption(self, image: Union[Image.Image, EncodedImage], length: Literal["normal", "short", "long"] = "normal", stream: bool = False) -> CaptionOutput

Generate a caption for an image.

caption = model.caption(image, length="short")["caption"]
print(caption)

# Generate a caption with streaming (default: False)
for chunk in model.caption(image, length="short", stream=True)["caption"]:
    print(chunk, end="", flush=True)

query(self, image: Union[Image.Image, EncodedImage], question: str, stream: bool = False) -> QueryOutput

Ask a question about an image.

answer = model.query(image, question="What's in this image?")["answer"]
print("Answer:", answer)

# Ask a question with streaming (default: False)
for chunk in model.query(image, question="What's in this image?", stream=True)["answer"]:
    print(chunk, end="", flush=True)

detect(self, image: Union[Image.Image, EncodedImage], object: str) -> DetectOutput

Detect specific objects in an image.

detect_output = model.detect(image, "item")["objects"]
print(detect_output)

point(self, image: Union[Image.Image, EncodedImage], object: str) -> PointOutput

Get coordinates of specific objects in an image.

point_output = model.point(image, "person")
print(point_output)

encode_image(self, image: Union[Image.Image, EncodedImage]) -> Base64EncodedImage

Produce Base64EncodedImage.

encoded_image = model.encode_image(image)

Image Types

• Image.Image: PIL Image object
• Base64EncodedImage: Object produced by model.encode_image(image), subtype of EncodedImage

Response Types

• CaptionOutput: {"caption": str | Generator}
• QueryOutput: {"answer": str | Generator}
• DetectOutput: {"objects": List[Region]}
• PointOutput: {"points": List[Point]}
• Region: Bounding box with coordinates (x_min, y_min, x_max, y_max)
• Point: Coordinates (x, y) indicating the object center

Links

• Website
• Try it out on the free playground
• GitHub