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from pathlib import Path
from PIL import Image
import pathlib
import numpy as np
import torch
import streamlit as st
import cv2
#If you have linux (or deploying for linux) use:
pathlib.WindowsPath = pathlib.PosixPath
# Load YOLOv5 model
model = torch.hub.load('./yolov5', 'custom', path='./yolo/best.pt', source='local', force_reload=True)
st.title("YOLO Object Detection Web App")
# Upload image
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Convert the file to an OpenCV image
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image", use_column_width=True)
st.write("Processing...")
# Convert the image to a format compatible with YOLO
image_np = np.array(image)
image_cv = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
# Perform YOLO detection
results = model(image_cv)
# Render the results
detected_image = np.squeeze(results.render())
# Display result
st.image(detected_image, caption="Detected Image", use_column_width=True) |