add ui

app.py

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+import streamlit as st
+import cv2
+from PIL import Image
+import numpy as np
+import torch
+from yolov5 import YOLOv5
+
+# Load YOLOv5 model (you can load YOLOv8 similarly)
+model = YOLOv5('yolo/best.pt')
+
+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)

unet/checkpoint_epoch5.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d14da734ef43353dd0295e2cf8c439dbde772a2a8c75b1089e0ae109a40cf46b
+size 124249546

yolo/best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:46b9db9a6deed93cb7b2322ddf61d268872b9a58911f0bde9e238218fd59da17
+size 93125376