initial commit

.gitignore

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+# PhpStorm / IDEA
+.idea
+

README.md

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+---
+tags:
+  - vision
+  - zero-shot-image-classification
+  - endpoints-template
+inference: true
+pipeline_tag: zero-shot-image-classification
+base_model: openai/clip-vit-large-patch14
+library_name: generic
+---
+
+# Fork of [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) for a `zero-sho-image-classification` Inference endpoint.
+
+This repository implements a `custom` task for `zero-shot-image-classification` for 🤗 Inference Endpoints. The code for the customized 
+pipeline is in the handler.py.
+
+To use deploy this model an Inference Endpoint you have to select `Custom` as task to use the `handler.py` file. 
+
+### expected Request payload
+
+```json
+{
+  "image": encoded_image,
+  "parameters": {
+    "candidate_labels": "green, yellow, blue, white, silver"
+  }
+}
+```
+
+`encoded_image` is a base64 encoded image. 

handler.py

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+from typing import Dict, List, Any
+from PIL import Image
+from io import BytesIO
+from transformers import pipeline
+import base64
+
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        self.pipeline=pipeline("zero-shot-image-classification",model="openai/clip-vit-large-patch14-336")
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+       data args:
+            inputs (:obj:`string`)
+            parameters (:obj:)
+      Return:
+            A :obj:`list`:. The list contains items that are dicts should be liked {"label": "XXX", "score": 0.82}
+        """
+        image_data = data.pop("inputs", data)
+        # decode base64 image to PIL
+        image = Image.open(BytesIO(base64.b64decode(image_data)))
+
+        parameters = data.pop("parameters", data)
+        candidate_labels = parameters['candidate_labels']
+
+        candidate_labels_array = list(map(str.strip, candidate_labels.split(',')))
+
+        # run prediction one image wit provided candiates
+        prediction = self.pipeline(images=[image], candidate_labels=candidate_labels_array)
+        return prediction[0]