Metadata-Version: 2.1
Name: clip_cpp
Version: 0.4.1
Summary: CLIP inference with no big dependencies as PyTorch, TensorFlow, Numpy
Home-page: https://github.com/monatis
Keywords: ggml,clip,clip.cpp,image embeddings,image search
Author: Yusuf Sarıgöz
Author-email: yusufsarigoz@gmail.com
Requires-Python: >=3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Project-URL: Repository, https://github.com/monatis/clip.cpp
Description-Content-Type: text/markdown

# Python bindings for clip.cpp

This package provides basic Python bindings for [clip.cpp](https://github.com/monatis/clip.cpp).

It requires no third-party libraries and no big dependencies such as PyTorch, TensorFlow, Numpy, ONNX etc.

## Install

If you are on a X64 Linux distribution, you can simply Pip-install it:

```sh
pip install clip_cpp
```

> Colab Notebook available for quick experiment :
>
> <a href="https://colab.research.google.com/github/Yossef-Dawoad/clip.cpp/blob/add_colab_notebook_example/examples/python_bindings/notebooks/clipcpp_demo.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

If you are on another operating system or architecture,
or if you want to make use of support for instruction sets other than AVX2 (e.g., AVX512),
you can build it from source.
Se [clip.cpp](https://github.com/monatis/clip.cpp) for more info.

All you need to do is to compile with the `-DBUILD_SHARED_LIBS=ON` option and copy `libclip.so` to `examples/python_bindings/clip_cpp`.

## Usage

```python
from clip_cpp import Clip

## you can either pass repo_id or .bin file
## you can type `clip-cpp-models` in your terminal to see what models are available for download
## in case you pass repo_id and it has more than .bin file
## it's recommended to specify which file to download with `model_file`
repo_id = 'Green-Sky/ggml_laion_clip-vit-b-32-laion2b-s34b-b79k'
model_file = 'laion_clip-vit-b-32-laion2b-s34b-b79k.ggmlv0.f16.bin'

model = Clip(
    model_path_or_repo_id=repo_id,
    model_file=model_file,
    verbosity=2
)

text_2encode = 'cat on a Turtle'

tokens = model.tokenize(text_2encode)
text_embed = model.encode_text(tokens)

## load and extract embedings of an image from the disk
image_2encode = '/path/to/cat.jpg'
image_embed = model.load_preprocess_encode_image(image_2encode)

## perform similarity search between the image and the text
score = model.calculate_similarity(text_embed, image_embed)

# Alternatively, you can just do:
# score = model.compare_text_and_image(text, image_path)

print(f"Similarity score: {score}")

```

## Clip Class

The `Clip` class provides a Python interface to clip.cpp, allowing you to perform various tasks such as text and image encoding, similarity scoring, and text-image comparison. Below are the constructor and public methods of the `Clip` class:

### Constructor

```python
def __init__(
    self, model_path_or_repo_id: str,
    model_file: Optional[str] = None,
    revision: Optional[str] = None,
    verbosity: int = 0):
```

-   **Description**: Initializes a `Clip` instance with the specified CLIP model file and optional verbosity level.
-   `model_path_or_repo_id` (str): The path to the CLIP model file `file` | HF `repo_id`.
-   `model_file` (str, optional): if model_path_or_repo_id is **repo_id** that has multiple `.bin` files you can sapcify which `.bin` file to download
-   `verbosity` (int, optional): An integer specifying the verbosity level (default is 0).

### Public Methods

#### 1. `vision_config`

```python
@property
def vision_config(self) -> Dict[str, Any]:
```

-   **Description**: Retrieves the configuration parameters related to the vision component of the CLIP model.

#### 2. `text_config`

```python
@property
def text_config(self) -> Dict[str, Any]:
```

-   **Description**: Retrieves the configuration parameters related to the text component of the CLIP model.

#### 3. `tokenize`

```python
def tokenize(self, text: str) -> List[int]:
```

-   **Description**: Tokenizes a text input into a list of token IDs.
-   `text` (str): The input text to be tokenized.

#### 4. `encode_text`

```python
def encode_text(
    self, tokens: List[int], n_threads: int = os.cpu_count(), normalize: bool = True
) -> List[float]:
```

-   **Description**: Encodes a list of token IDs into a text embedding.
-   `tokens` (List[int]): A list of token IDs obtained through tokenization.
-   `n_threads` (int, optional): The number of CPU threads to use for encoding (default is the number of CPU cores).
-   `normalize` (bool, optional): Whether or not to normalize the output vector (default is `True`).

#### 5. `load_preprocess_encode_image`

```python
def load_preprocess_encode_image(
    self, image_path: str, n_threads: int = os.cpu_count(), normalize: bool = True
) -> List[float]:
```

-   **Description**: Loads an image, preprocesses it, and encodes it into an image embedding.
-   `image_path` (str): The path to the image file to be encoded.
-   `n_threads` (int, optional): The number of CPU threads to use for encoding (default is the number of CPU cores).
-   `normalize` (bool, optional): Whether or not to normalize the output vector (default is `True`).

#### 6. `calculate_similarity`

```python
def calculate_similarity(
    self, text_embedding: List[float], image_embedding: List[float]
) -> float:
```

-   **Description**: Calculates the similarity score between a text embedding and an image embedding.
-   `text_embedding` (List[float]): The text embedding obtained from `encode_text`.
-   `image_embedding` (List[float]): The image embedding obtained from `load_preprocess_encode_image`.

#### 7. `compare_text_and_image`

```python
def compare_text_and_image(
    self, text: str, image_path: str, n_threads: int = os.cpu_count()
) -> float:
```

-   **Description**: Compares a text input and an image file, returning a similarity score.
-   `text` (str): The input text.
-   `image_path` (str): The path to the image file for comparison.
-   `n_threads` (int, optional): The number of CPU threads to use for encoding (default is the number of CPU cores).

#### 8. `__del__`

```python
def __del__(self):
```

-   **Description**: Destructor that frees resources associated with the `Clip` instance.

With the `Clip` class, you can easily work with the CLIP model for various natural language understanding and computer vision tasks.

## Example

A basic example can be found in the [clip.cpp examples](https://github.com/monatis/clip.cpp/blob/main/examples/python_bindings/example_main.py).

```
python example_main.py --help
usage: clip [-h] -m MODEL [-v VERBOSITY] -t TEXT -i IMAGE

optional arguments:
  -h, --help            show this help message and exit
  -m MODEL, --model MODEL
                        path to GGML file
  -v VERBOSITY, --verbosity VERBOSITY
                        Level of verbosity. 0 = minimum, 2 = maximum
  -t TEXT, --text TEXT  text to encode
  -i IMAGE, --image IMAGE
                        path to an image file
```

Bindings to the DLL are implemented in `clip_cpp/clip.py` and

