Create app.py

app.py

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+import streamlit as st
+from ultralytics import YOLO
+from PIL import Image, ImageDraw
+import requests
+import tempfile
+import os
+
+# Download YOLOv8 model from Hugging Face
+model_url = "https://huggingface.co/krishnamishra8848/Nepal_Vehicle_License_Plates_Detection_Version2/resolve/main/best.pt"
+model_path = "best.pt"
+
+# Download the model if not already downloaded
+if not os.path.exists(model_path):
+    st.info("Downloading model from Hugging Face...")
+    with open(model_path, "wb") as f:
+        response = requests.get(model_url)
+        f.write(response.content)
+    st.success("Model downloaded successfully!")
+
+# Load the YOLOv8 model
+model = YOLO(model_path)
+
+# App title
+st.title("Nepal Vehicle License Plate Detection")
+st.write("Upload an image to detect vehicle license plates along with their confidence scores.")
+
+# Upload image
+uploaded_image = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"])
+
+if uploaded_image is not None:
+    # Display the uploaded image
+    image = Image.open(uploaded_image)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Run YOLOv8 inference
+    with st.spinner("Running detection..."):
+        temp_file = tempfile.NamedTemporaryFile(delete=False)
+        temp_file.write(uploaded_image.read())
+        results = model(temp_file.name)
+
+    # Draw bounding boxes and confidence scores on the image
+    draw = ImageDraw.Draw(image)
+    results_table = []
+    for box in results[0].boxes:
+        # Get bounding box coordinates and confidence score
+        x_min, y_min, x_max, y_max = map(int, box.xyxy[0].tolist())
+        confidence = box.conf.item()
+        label = f"Plate: {confidence:.2f}"
+
+        # Draw rectangle and label
+        draw.rectangle([(x_min, y_min), (x_max, y_max)], outline="red", width=3)
+        draw.text((x_min, y_min - 10), label, fill="red")
+
+        # Append detection to the table
+        results_table.append({"x_min": x_min, "y_min": y_min, "x_max": x_max, "y_max": y_max, "confidence": confidence})
+
+    # Display the resulting image with bounding boxes
+    st.image(image, caption="Detected Image", use_column_width=True)
+
+    # Show individual detections in a table
+    st.write("### Detection Results")
+    st.write(results_table)