Code Editor for app.py

import streamlit as st
from ultralytics import YOLO
from PIL import Image
import numpy as np

st.title("Object Detection with YOLO")

# Load the YOLO model
@st.cache_resource
def load_model():
    return YOLO('yolov8n.pt')

model = load_model()

# File uploader
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.
    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
    image = Image.open(uploaded_file)
    
    st.image(image, caption='Uploaded Image', use_column_width=True)
    
    if st.button('Detect Objects'):
        st.write("Detecting objects...")
        
        # Perform prediction
        results = model.predict(image)
        
        # Display results
        for result in results:
            # Plot results image
            st.image(result.plot(), caption='Detected Objects', use_column_width=True)
            
            # Display detection information
            boxes = result.boxes
            if len(boxes) > 0:
                st.write("Detected Objects:")
                for box in boxes:
                    confidence = box.conf[0]
                    class_id = box.cls[0]
                    class_name = model.names[int(class_id)]
                    st.write(f"- {class_name} (Confidence: {confidence:.2f})")