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# from PIL import Image
from transformers import DetrFeatureExtractor
from transformers import DetrForObjectDetection
import torch
# import numpy as np
def object_count(picture):
feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-101-dc5")
encoding = feature_extractor(picture, return_tensors="pt")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-101-dc5")
outputs = model(**encoding)
# keep only predictions of queries with 0.9+ confidence (excluding no-object class)
probas = outputs.logits.softmax(-1)[0, :, :-1]
keep = probas.max(-1).values > 0.7
count = 0
for i in keep:
if i:
count=count+1
return "About " + str(count) +" common objects were detected"
# object_count("toothbrush.jpg")
import gradio as gr
interface = gr.Interface(object_count, gr.inputs.Image(shape=(640, 480)), "text", title="Common Object Counter",examples=["chairs.jpg", "empty.jpg", "bottles.jpg"], description="This App counts the common objects detected in an Image",
allow_flagging="never").launch()
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