Update README.md

README.md

@@ -5,6 +5,104 @@ language:
 - en
 pipeline_tag: zero-shot-object-detection
 library_name: transformers
+base_model:
+- omlab/omdet-turbo-swin-tiny-hf
+tags:
+- endpoints-template
 ---
 
-# Fork of [omlab/omdet-turbo-swin-tiny-hf](https://huggingface.co/omlab/omdet-turbo-swin-tiny-hf) for a `zero-shot-object-detection` Inference endpoint.
+# Fork of [omlab/omdet-turbo-swin-tiny-hf](https://huggingface.co/omlab/omdet-turbo-swin-tiny-hf) for a `zero-shot-object-detection` Inference endpoint.
+
+This repository implements a `custom` task for `zero-shot-object-detection` for 🤗 Inference Endpoints. The code for the customized handler is in the [handler.py](https://huggingface.co/Blueway/inference-endpoint-for-omdet-turbo-swin-tiny-hf/blob/main/handler.py).
+
+To use deploy this model a an Inference Endpoint you have to select `Custom` as task to use the `handler.py` file. 
+
+The repository contains a requirements.txt to download the timm library.
+
+### expected Request payload
+
+```json
+{
+  "image": "/9j/4AAQSkZJRgABAQEBLAEsAAD/2wBDAAMCAgICAgMC....", // base64 image as bytes
+  "candiates":["broken curb", "broken road", "broken road sign", "broken sidewalk"]
+}
+```
+
+below is an example on how to run a request using Python and `requests`.
+
+## Run Request 
+
+``` python
+import json
+from typing import List
+import requests as r
+import base64
+
+ENDPOINT_URL = ""
+HF_TOKEN = ""
+
+def predict(path_to_image: str = None, candidates: List[str] = None):
+    with open(path_to_image, "rb") as i:
+        b64 = base64.b64encode(i.read())
+
+    payload = {"inputs": {"image": b64.decode("utf-8"), "candidates": candidates}}
+    response = r.post(
+        ENDPOINT_URL, headers={"Authorization": f"Bearer {HF_TOKEN}"}, json=payload
+    )
+    return response.json()
+
+
+prediction = predict(
+    path_to_image="image/brokencurb.jpg", candidates=["broken curb", "broken road", "broken road sign", "broken sidewalk"]
+)
+print(json.dumps(prediction, indent=2))
+```
+expected output
+
+``` python
+{
+  "boxes": [
+    [
+      1.919342041015625,
+      231.1556396484375,
+      1011.4019775390625,
+      680.3773193359375
+    ],
+    [
+      610.9949951171875,
+      397.6180419921875,
+      1019.9259033203125,
+      510.8144226074219
+    ],
+    [
+      1.919342041015625,
+      231.1556396484375,
+      1011.4019775390625,
+      680.3773193359375
+    ],
+    [
+      786.1240234375,
+      68.618896484375,
+      916.1265869140625,
+      225.0513458251953
+    ]
+  ],
+  "scores": [
+    0.4329715967178345,
+    0.4215811491012573,
+    0.3389397859573364,
+    0.3133399784564972
+  ],
+  "candidates": [
+    "broken sidewalk",
+    "broken road sign",
+    "broken road",
+    "broken road sign"
+  ]
+}
+```
+The boxes are structured like {x_min, y_min, x_max, y_max}
+
+
+## Credits
+