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app.py

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+import streamlit as st
+from ultralytics import YOLO
+import cv2
+from PIL import Image
+import numpy as np
+
+# Load the pre-trained YOLOv8 model
+model = YOLO("YOLOv8\yolov8x.pt")  # Replace with the path to your model
+
+# Title for the web app
+st.title("YOLOv8 Object Detection - Image Upload")
+
+# Instructions
+st.write("Upload an image, and YOLOv8 will predict the objects in the image with bounding boxes.")
+
+# File uploader widget
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Read the uploaded image file and display it
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Convert the image to a numpy array for YOLO processing
+    img_array = np.array(image)
+
+    # Make predictions using the model
+    results = model.predict(img_array, conf=0.5, iou=0.4)
+
+    # Display the results
+    st.write(f"Detected {len(results)} objects.")
+
+    # Annotate the image with bounding boxes
+    annotated_img = results[0].plot()
+
+    # Convert the annotated image to a format suitable for Streamlit
+    annotated_img_pil = Image.fromarray(annotated_img)
+
+    # Display the annotated image
+    st.image(annotated_img_pil, caption="Processed Image with Bounding Boxes", use_column_width=True)

requirements.txt

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+ultralytics
+streamlit 
+opencv-python 
+pillow 
+numpy