usrbinenv python codingutf8 2023 the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license usr bin env python coding utf 8 2023 the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVision2Seq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
    from PIL import Image


class ImageCaptioningTool(PipelineTool):
    default_checkpoint = "Salesforce/blip-image-captioning-base"
    description = (
        "This is a tool that generates a description of an image. It takes an input named `image` which should be the "
        "image to caption, and returns a text that contains the description in English."
    )
    name = "image_captioner"
    model_class = AutoModelForVision2Seq

    inputs = ["image"]
    outputs = ["text"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["vision"])
        super().__init__(*args, **kwargs)

    def encode(self, image: "Image"):
        return self.pre_processor(images=image, return_tensors="pt")

    def forward(self, inputs):
        return self.model.generate(**inputs)

    def decode(self, outputs):
        return self.pre_processor.batch_decode(outputs, skip_special_tokens=True)[0].strip()