Update app.py

app.py

@@ -3,6 +3,10 @@ from open_image_models import LicensePlateDetector
 from PIL import Image
 import cv2
 import numpy as np
+from rich.console import Console
+
+# Set up the rich console for better terminal output
+console = Console()
 
 # Define the available models
 PlateDetectorModel = ['yolo-v9-t-640-license-plate-end2end', 
@@ -11,23 +15,25 @@ PlateDetectorModel = ['yolo-v9-t-640-license-plate-end2end',
                       'yolo-v9-t-256-license-plate-end2end']
 
 # Streamlit interface
-st.title("License Plate Detection with Open Image Models")
+st.title("🚗 License Plate Detection with Open Image Models 🚓")
 st.write("Select a model and upload an image to perform license plate detection.")
+st.markdown("---")
 
 # Model selection dropdown
-selected_model = st.selectbox("Select a License Plate Detection Model", PlateDetectorModel)
+selected_model = st.selectbox("🔍 Select a License Plate Detection Model", PlateDetectorModel)
 
 # File uploader for images
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg", "webp"])
+uploaded_file = st.file_uploader("📂 Upload an image...", type=["jpg", "png", "jpeg", "webp"])
 
 if uploaded_file is not None:
-    # Load the image
+    # Load the image using PIL
     image = Image.open(uploaded_file)
     st.image(image, caption='Uploaded Image', use_column_width=True)
+
     st.write("")
-    st.write("Detecting license plates...")
+    st.write("🔍 **Detecting license plates...**")
 
-    # Convert the image to an OpenCV format
+    # Convert the PIL image to an OpenCV format (NumPy array)
     image_np = np.array(image)
     image_cv2 = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
 
@@ -37,9 +43,32 @@ if uploaded_file is not None:
     # Perform license plate detection
     detections = lp_detector.predict(image_cv2)
 
-    # Display the detected plates
-    st.write(f"Detections: {detections}")
+    # Display the detected plates using `rich` for colorful output in the console
+    console.print(f"[bold green]Detections: [/bold green] {detections}")
+
+    # Streamlit display for detections
+    if detections:
+        st.success(f"✅ {len(detections)} License Plates Detected!")
+        for i, detection in enumerate(detections):
+            st.write(f"**Plate {i+1}:** {detection}")
+    else:
+        st.warning("⚠️ No license plates detected!")
 
     # Annotate and display the image with detected plates
     annotated_image = lp_detector.display_predictions(image_cv2)
-    st.image(annotated_image, caption='Annotated Image with Detections', use_column_width=True)
+
+    # Convert the annotated image from BGR to RGB for Streamlit display
+    annotated_image_rgb = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
+    st.image(annotated_image_rgb, caption='Annotated Image with Detections', use_column_width=True)
+
+# Add some additional style or layout to make the app more attractive
+st.markdown("""
+<style>
+    .stButton>button {
+        font-size: 16px;
+        background-color: #4CAF50;
+        color: white;
+        border-radius: 8px;
+    }
+</style>
+""", unsafe_allow_html=True)