Update app.py

app.py

@@ -1,8 +1,8 @@
-import streamlit as st
 from ultralytics import YOLO
 import numpy as np
 import cv2
 from PIL import Image
+import streamlit as st
 
 # Model labels
 model1Labels = {0: 'single_number_plate', 1: 'double_number_plate'}
@@ -87,36 +87,11 @@ def prediction(image):
 st.title('License Plate Recognition 🚗')
 st.header('Upload an image of a license plate to get the License number.')
 
-# Define example images (update with actual paths)
-example_images = {
-    "Car ": "test/audiR8V10.jpg",
-    "Car 2": "test/c7.jpg",
-    "Car 3": "test/c4.jpg",
-    "CCTV B/W": "test/cctv img plate.jpg",
-    "Bike": "test/BikeNumberPlate.jpg",
-    "Bus": "test/bus.jpg",
-}
-
-# File uploader
+# File uploader for user to upload an image
 uploaded_file = st.file_uploader("Choose an image...", type="jpg")
 
-
-
-c1, c2 = st.columns(2)
-for name, path in example_images.items():
-    with c1:
-        example_img = Image.open(path)
-
-image = None
 if uploaded_file is not None:
     image = np.array(Image.open(uploaded_file))
-else:
-    st.header("Or choose an example image from below dropdown:")
-    selected_example = st.selectbox("", list(example_images.keys()))
-    if selected_example:
-        image = np.array(Image.open(example_images[selected_example]))
-
-if image is not None:
     c1, c2, c3 = st.columns(3)
 
     with c1:
@@ -133,4 +108,3 @@ if image is not None:
     with c3:
         st.success(', '.join(license_plate_text))
         st.write('License Plate Text')
-