Update README.md

README.md

@@ -2,6 +2,51 @@
 tags:
 - image-to-text
 - image-captioning
+- endpoints-template
 license: bsd-3-clause
+library_name: generic
+---
+
+# Blip Caption 🤗 Inference Endpoints
+
+This repository implements a `custom` task for `image-captioning` for 🤗 Inference Endpoints. The code for the customized pipeline is in the [pipeline.py](https://huggingface.co/florentgbelidji/blip_captioning/blob/main/pipeline.py).
+To use deploy this model a an Inference Endpoint you have to select `Custom` as task to use the `pipeline.py` file. -> _double check if it is selected_
+### expected Request payload
+```json
+{
+  "image": "/9j/4AAQSkZJRgABAQEBLAEsAAD/2wBDAAMCAgICAgMC....", // base64 image as bytes
+}
+```
+below is an example on how to run a request using Python and `requests`.
+## Run Request 
+1. prepare an image. 
+```bash
+!wget https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
+```
+2. 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):
+    with open(path_to_image, "rb") as i:
+        b64 = base64.b64encode(i.read())
+    payload = {"inputs": {"image": b64.decode("utf-8"), "candiates": candiates}}
+    response = r.post(
+        ENDPOINT_URL, headers={"Authorization": f"Bearer {HF_TOKEN}"}, json=payload
+    )
+    return response.json()
+prediction = predict(
+    path_to_image="palace.jpg"
+)
+```
+expected output
+```python
+['buckingham palace with flower beds and red flowers']
 
----
+```