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import streamlit as st
from transformers import DetrImageProcessor, DetrForObjectDetection
import torch
from PIL import Image
import requests
st.set_page_config(page_title="SnapSpot", page_icon="📸", layout="wide", initial_sidebar_state="collapsed")
# Function to perform object detection
def detect_objects(image):
# Load DETR model and processor
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
# Preprocess the image
inputs = processor(images=image, return_tensors="pt")
# Perform object detection
outputs = model(**inputs)
# Convert outputs to COCO API format
target_sizes = torch.tensor([image.size[::-1]])
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
return results
# Main Streamlit app
def main():
st.title("SnapSpot")
st.markdown(
"""
<style>
.reportview-container {
background: #0e1117;
color: #f0f6fc;
}
.st-bq {
background-color: #0e1117;
}
.st-bm {
padding-top: 2rem;
}
</style>
""",
unsafe_allow_html=True,
)
# Upload image
uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
if uploaded_image is not None:
# Display uploaded image
image = Image.open(uploaded_image)
st.image(image, caption="Uploaded Image", use_column_width=True)
# Perform object detection
results = detect_objects(image)
# Display detection results
st.subheader("Detection Results:")
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 "
f"{round(score.item(), 3)} at location {box}"
)
if __name__ == "__main__":
main() |