app.py

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();