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from transformers import pipeline
import streamlit as st
import torch
from PIL import Image
import requests
# Load model directly
from transformers import AutoImageProcessor, AutoModelForObjectDetection
from transformers import AutoImageProcessor, DetrForObjectDetection
def model():
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
image_processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
inputs = image_processor(images=image, return_tensors="pt")
outputs = model(**inputs)
# display image
st.image(image, caption='Animal Images')
# convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax)
target_sizes = torch.tensor([image.size[::-1]])
results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[0]
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
box = [round(i, 2) for i in box.tolist()]
st.write(f"Detected {model.config.id2label[label.item()]} with confidence ")
st.write(f"{round(score.item(), 3)} at location {box}")
def main():
st.title("Hugging Face Model Demo")
# Create an input text box
input_text = st.text_input("Enter your text", "")
# Create a button to trigger model inference
if st.button("Analyze"):
# Perform inference using the loaded model
result=model()
#st.write("Prediction:", result[0]['label'], "| Score:", result[0]['score'])
if __name__ == "__main__":
main()
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