updates

README.md

@@ -0,0 +1,25 @@
+---
+tags:
+- vision
+- zero-shot-image-classification
+- endpoints-template
+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"
+  }
+}
+```

handler.py

@@ -17,17 +17,12 @@ class EndpointHandler():
       Return:
             A :obj:`list`:. The list contains items that are dicts should be liked {"label": "XXX", "score": 0.82}
         """
-        #inputs = data.pop("inputs", data)
-        #image_data = inputs['image']
-
         image_data = data.pop("image", data)
         # decode base64 image to PIL
         image = Image.open(BytesIO(base64.b64decode(image_data)))
 
         parameters = data.pop("parameters", data)
-        #parameters = inputs['parameters']
         candidate_labels = parameters['candidate_labels']
-        print(candidate_labels)
 
         candidate_labels_array = list(map(str.strip, candidate_labels.split(',')))