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"

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 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=img_byte_arr)
    return response.json()


inputs = gr.components.Image(type="pil", label="Upload an image")
demo = gr.Interface(fn=rb, inputs=inputs, outputs="json")
demo.launch()

# import io
# import requests
# from PIL import Image
# import torch
# import numpy

# from transformers import DetrFeatureExtractor, DetrForSegmentation
# from transformers.models.detr.feature_extraction_detr import rgb_to_id

# url = "http://images.cocodataset.org/val2017/000000039769.jpg"
# image = Image.open(requests.get(url, stream=True).raw)

# feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50-panoptic")
# model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic")

# # prepare image for the model
# inputs = feature_extractor(images=image, return_tensors="pt")

# # forward pass
# outputs = model(**inputs)

# # use the `post_process_panoptic` method of `DetrFeatureExtractor` to convert to COCO format
# processed_sizes = torch.as_tensor(inputs["pixel_values"].shape[-2:]).unsqueeze(0)
# result = feature_extractor.post_process_panoptic(outputs, processed_sizes)[0]

# # the segmentation is stored in a special-format png
# panoptic_seg = Image.open(io.BytesIO(result["png_string"]))
# panoptic_seg = numpy.array(panoptic_seg, dtype=numpy.uint8)
# # retrieve the ids corresponding to each mask
# panoptic_seg_id = rgb_to_id(panoptic_seg)