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import requests
import os, io
import gradio as gr
# from PIL import Image
# API_URL = "https://api-inference.huggingface.co/models/facebook/detr-resnet-50-panoptic"
# headers = {"Authorization": "Bearer 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"}
API_URL = "https://api-inference.huggingface.co/models/facebook/detr-resnet-50-dc5-panoptic"
headers = {"Authorization": "Bearer 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"}
def image_classifier(inp):
return {'cat': 0.3, 'dog': 0.7}
def query(filename):
with open(filename, "rb") as f:
data = f.read()
response = requests.post(API_URL, headers=headers, data=data)
return response.json()
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()
# response = requests.post(API_URL, headers=headers, data=bytes(img.tobytes("raw")))
response = requests.post(API_URL, headers=headers, data=img_byte_arr)
logits = response.score
# bboxes = response.pred_boxes
# masks = response.pred_masks
return response.json()
# train = os.listdir("./")
# print(train)
# inputs = gr.inputs.Image(type="pil", label="Upload an image")
# demo = gr.Interface(fn=rb, inputs=inputs, outputs="json")
# demo.launch()
gr.Interface.load("spaces/eugenesiow/remove-bg").launch();
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