Update app.py

app.py

@@ -3,49 +3,128 @@ import degirum as dg
 from PIL import Image
 import degirum_tools
 
-# hw_location: Where you want to run inference.
-#     Use "@cloud" to use DeGirum cloud.
-#     Use "@local" to run on local machine.
-#     Use an IP address for AI server inference.
-hw_location = "@cloud"
+# -----------------------------
+# Page config
+# -----------------------------
+st.set_page_config(
+    page_title="DeGirum License Plate Demo",
+    page_icon="🚗",
+    layout="centered",
+)
+
+# -----------------------------
+# App title & intro
+# -----------------------------
+st.title("License Plate Detection & Recognition (DeGirum Cloud)")
+
+st.markdown(
+    """
+This demo shows how to build a simple **Automatic License Plate Recognition (ALPR)** 
+pipeline using models hosted on **DeGirum Cloud**.
+
+**What this app does:**
+1. Detects license plates in an uploaded image.
+2. Crops each plate region.
+3. Runs an OCR model to read the characters on the plate.
+4. Displays the original and annotated images **side by side**.
+"""
+)
 
-# model_zoo_url: URL/path for the model zoo.
-#     Use cloud_zoo_url for @cloud, @local, and AI server inference options.
-#     Use '' for an AI server serving models from a local folder.
-#     Use a path to a JSON file for a single model zoo in case of @local inference.
+st.sidebar.header("About this demo")
+st.sidebar.markdown(
+    """
+- **Inference location:** DeGirum Cloud  
+- **Models used:**
+  - LP detection: `yolov8n_relu6_global_lp_det--640x640_quant_n2x_orca1_1`
+  - LP OCR: `yolov8s_relu6_lp_ocr_7ch--256x128_quant_n2x_orca1_1`
+- **Libraries:**
+  - `degirum`
+  - `degirum_tools`
+  - `streamlit`
+"""
+)
+
+# -----------------------------
+# Configuration
+# -----------------------------
+hw_location = "@cloud"
 model_zoo_url = "https://cs.degirum.com/degirum/degirum"
 
-# lp_det_model_name: Name of the model for license plate detection.
 lp_det_model_name = "yolov8n_relu6_global_lp_det--640x640_quant_n2x_orca1_1"
-
-# lp_ocr_model_name: Name of the model for license plate OCR.
 lp_ocr_model_name = "yolov8s_relu6_lp_ocr_7ch--256x128_quant_n2x_orca1_1"
 
-# Connect to AI inference engine
-model_zoo = dg.connect(hw_location, model_zoo_url, token=st.secrets["DG_TOKEN"])
-
-# Load models
-lp_det_model = model_zoo.load_model(lp_det_model_name,
-                                   image_backend='pil',
-                                   overlay_color=(255,0,0),
-                                   overlay_line_width=2,
-                                   overlay_font_scale=2                                
-                                   )
-lp_ocr_model= model_zoo.load_model(lp_ocr_model_name, image_backend='pil')
-
-# Create a compound cropping model with 5% crop extent
-crop_model = degirum_tools.CroppingAndClassifyingCompoundModel(
-    lp_det_model, lp_ocr_model, 5.0
+
+# -----------------------------
+# Model loading (cached)
+# -----------------------------
+@st.cache_resource(show_spinner=True)
+def load_compound_model():
+    model_zoo = dg.connect(hw_location, model_zoo_url, token=st.secrets["DG_TOKEN"])
+
+    lp_det_model = model_zoo.load_model(
+        lp_det_model_name,
+        image_backend="pil",
+        overlay_color=(255, 0, 0),
+        overlay_line_width=2,
+        overlay_font_scale=2,
+    )
+
+    lp_ocr_model = model_zoo.load_model(
+        lp_ocr_model_name,
+        image_backend="pil",
+    )
+
+    # Create a compound cropping model with 5% crop extent
+    crop_model = degirum_tools.CroppingAndClassifyingCompoundModel(
+        lp_det_model, lp_ocr_model, 5.0
     )
 
-st.title('DeGirum Cloud Platform Demo of License Plate Detection and Recognition Models')
-
-st.text('Upload an image. Then click on the submit button')
-with st.form("model_form"):
-    uploaded_file=st.file_uploader('input image')
-    submitted = st.form_submit_button("Submit")
-    if submitted:
-        image = Image.open(uploaded_file)
-        image.thumbnail((512,512), Image.Resampling.LANCZOS)
-        inference_results=crop_model(image)
-        st.image(inference_results.image_overlay,caption='Image with Bounding Boxes')        
+    return crop_model
+
+
+crop_model = load_compound_model()
+
+# -----------------------------
+# File upload UI
+# -----------------------------
+st.subheader("Upload an image and run the models")
+
+uploaded_file = st.file_uploader(
+    "Choose an image containing a vehicle / license plate",
+    type=["jpg", "jpeg", "png"],
+)
+
+run_button = st.button("Run Inference", type="primary", disabled=uploaded_file is None)
+
+# -----------------------------
+# Inference
+# -----------------------------
+if run_button and uploaded_file is not None:
+    with st.spinner("Running license plate detection and recognition..."):
+        # Load full-res image and create a display copy
+        orig_image = Image.open(uploaded_file).convert("RGB")
+        display_image = orig_image.copy()
+        display_image.thumbnail((640, 640), Image.Resampling.LANCZOS)
+
+        # Run model on the resized display image
+        inference_results = crop_model(display_image)
+
+    st.subheader("Results")
+
+    col1, col2 = st.columns(2, gap="medium")
+
+    with col1:
+        st.markdown("**Original image**")
+        st.image(display_image, use_container_width=True)
+
+    with col2:
+        st.markdown("**Detection & recognition**")
+        st.image(
+            inference_results.image_overlay,
+            caption="License plates with bounding boxes and labels",
+            use_container_width=True,
+        )
+
+    st.caption("Inference complete. Detected plates and OCR results are shown on the right.")
+elif uploaded_file is None:
+    st.info("👈 Upload an image to get started.")