sample.py

import streamlit as st
from PIL import Image
import cv2
import matplotlib.pyplot as plt
import torch
import numpy as np
from torchvision import transforms
from ultralytics import YOLO

# Load the YOLO model
@st.cache
def load_model():
    # Replace 'model.pt' with the path to your YOLO model file
    model = YOLO('best.pt')
    return model

# Define YOLO processing function
def process_image(image):
    # Preprocess the image
    preprocess = transforms.Compose([
        transforms.Resize((416, 416)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])
    input_tensor = preprocess(image)
    input_batch = input_tensor.unsqueeze(0)
    
    # # Perform inference
    # with torch.no_grad():
    #     output = model(input_batch)
    
    # # Post-process the output (e.g., draw bounding boxes)
    # # Replace this with your post-processing code
    
    # # Convert tensor to PIL Image
    # output_image = transforms.ToPILImage()(output[0].cpu().squeeze())

    return input_batch

# Main Streamlit code
def main():
    st.title('YOLO Image Detection')
    
    # Upload image file
    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
    
    if uploaded_file is not None:
        # Load YOLO model
        model = load_model()
        
        # Process uploaded image
        # image_l = Image.open(uploaded_file)
        image = plt.imread(uploaded_file)
        # Process uploaded image
        # image = Image.open(uploaded_file)
        st.image(image, caption='Original Image', use_column_width=True)
        
        # output_l = process_image(image)
        # output = np.asarray(output_l)
        # output_image = Image.fromarray(output)
        results = model(image)
        for result in results.pred[0]:
            box = result.xyxy[0].tolist()  # Extract bounding box coordinates as a list [x_min, y_min, x_max, y_max]
            x_min, y_min, x_max, y_max = box
            cv2.rectangle(image, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2)
        st.image(image, caption='Processed Image', use_column_width=True)


if __name__ == '__main__':
    main()