app.py

import streamlit as st
from ultralytics import YOLO
from PIL import Image
import os

# Directly load the model using full path if needed
model_path = os.path.join(os.getcwd(), 'best.pt')

# Load the YOLO model
model = YOLO(model_path)

# Define the prediction function
def predict(image):
    results = model(image)  # Run YOLOv8 model on the uploaded image
    results_img = results[0].plot()  # Get image with bounding boxes
    return Image.fromarray(results_img)

# Streamlit UI for Object Detection
st.title("Object Detection with YOLOv8")
st.markdown("Upload an image for detection.")

# Allow the user to upload an image
uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

if uploaded_image is not None:
    # Open the uploaded image using PIL
    image = Image.open(uploaded_image)
    
    # Display the uploaded image
    st.image(image, caption="Uploaded Image", use_column_width=True)
    
    # Run the model prediction
    st.subheader("Prediction Results:")
    result_image = predict(image)
    
    # Display the result image with bounding boxes
    st.image(result_image, caption="Detected Image", use_column_width=True)