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app.py

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+import os, io
+import gradio as gr
+from transformers import pipeline
+# from PIL import Image
+
+# API_URL = "https://api-inference.huggingface.co/models/facebook/detr-resnet-50-panoptic"
+
+SECRET_TOKEN = os.getenv("SECRET_TOKEN")
+API_URL = "https://api-inference.huggingface.co/models/facebook/detr-resnet-50-dc5-panoptic"
+headers = {"Authorization": f'Bearer {SECRET_TOKEN}'}
+
+
+def rb(img):
+    # initialiaze io to_bytes converter
+    img_byte_arr = io.BytesIO()
+    # define quality of saved array
+    img.save(img_byte_arr, format='JPEG', subsampling=0, quality=100)
+    # converts image array to bytesarray
+    img_byte_arr = img_byte_arr.getvalue()
+    return img_byte_arr
+
+estimator = pipeline("depth-estimation")
+result = estimator("http://images.cocodataset.org/val2017/000000039769.jpg")
+
+
+inputs = gr.inputs.Image(type="pil", label="Upload an image")
+
+demo = gr.Interface(fn=rb, inputs=inputs, outputs=result)
+#demo = gr.Interface(fn=rb, inputs=inputs, outputs=result["depth"])
+demo.launch()

requirements.txt

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+gradio
+transformers