Update app.py

app.py

@@ -1,96 +1,108 @@
-import streamlit as st
-import cv2
-import numpy as np
-import easyocr
-from PIL import Image
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing import image as keras_image
-
-# Load model and OCR tools
-model = load_model("Vehicle Number Plates.keras")
-plate_detector = cv2.CascadeClassifier("haarcascade_license_plate_rus_16stages.xml")
-reader = easyocr.Reader(['en'])
-
-# Plate Detection Function
-def detect_and_predict(img_input):
-    img = np.array(img_input.convert("RGB"))
-    frame = img.copy()
-    gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
-    plates = plate_detector.detectMultiScale(gray, 1.1, 4)
-
-    plate_text = "Not Detected"
-    confidence = None
-
-    for x, y, w, h in plates:
-        roi = frame[y:y+h, x:x+w]
-        try:
-            test_img = cv2.resize(roi, (200, 200))
-            test_img = keras_image.img_to_array(test_img) / 255.0
-            test_img = np.expand_dims(test_img, axis=0)
-            pred = model.predict(test_img)[0][0]
-        except Exception as e:
-            print(f"Prediction error: {e}")
-            continue
-
-        if pred < 0.5:
-            result = reader.readtext(roi)
-            if result:
-                plate_text = result[0][1]
-                confidence = result[0][2]
-                label = f"Plate: {plate_text}"
-            else:
-                label = "Plate Detected (No text)"
-        else:
-            label = "Plate Not Detected"
-
-        # Draw detection
-        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
-        cv2.putText(frame, label, (x, y - 10),
-                    cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
-
-    return frame, confidence, plate_text
-
-# Streamlit App UI
-st.set_page_config(page_title="License Plate Detection", layout="wide")
-st.title("🚘 License Plate Detection App")
-
-tab1, tab2 = st.tabs(["📁 Upload Image", "📷 Webcam Capture"])
-
-# Tab 1 - Upload Image
-with tab1:
-    uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
-    if uploaded_file:
-        image_input = Image.open(uploaded_file)
-        st.image(image_input, caption="Uploaded Image", use_container_width=True)
-
-        if st.button("🔍 Detect from Upload"):
-            with st.spinner("Processing..."):
-                result_img, confidence, label = detect_and_predict(image_input)
-
-            st.image(result_img, caption="Detection Result", channels="RGB", use_container_width=True)
-            if confidence:
-                st.metric("Confidence", f"{confidence * 100:.2f}%")
-                st.success(f"Detected Text: {label}")
-            else:
-                st.warning("No plate text detected.")
-
-# Tab 2 - Webcam Input (camera snapshot)
-with tab2:
-    camera_image = st.camera_input("📷 Take a picture using your webcam")
-    if camera_image:
-        try:
-            image_input = Image.open(camera_image)
-            st.image(image_input, caption="Webcam Snapshot", use_container_width=True)
-
-            with st.spinner("Analyzing..."):
-                result_img, confidence, label = detect_and_predict(image_input)
-
-            st.image(result_img, caption="Detection Result", channels="RGB", use_container_width=True)
-            if confidence is not None:
-                st.metric("Confidence", f"{confidence*100:.2f}%")
-                st.success(f"Detected Text: {label}")
-            else:
-                st.warning("Plate detected but no readable text found.")
-
-        except Exception as e:
-            st.error(f"❌ Error: {str(e)}")
+import streamlit as st
+import cv2
+import numpy as np
+import easyocr
+from PIL import Image
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image as keras_image
+
+# Load model and tools
+model = load_model("Vehicle Number Plates.keras")
+plate_detector = cv2.CascadeClassifier("haarcascade_license_plate_rus_16stages.xml")
+reader = easyocr.Reader(['en'])
+
+# Detect and predict function
+def detect_and_predict(img_input):
+    img = np.array(img_input.convert("RGB"))
+    frame = img.copy()
+    gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
+    plates = plate_detector.detectMultiScale(gray, 1.1, 4)
+
+    plate_text = "Not Detected"
+    confidence = None
+
+    for x, y, w, h in plates:
+        roi = frame[y:y+h, x:x+w]
+        try:
+            test_img = cv2.resize(roi, (200, 200))
+            test_img = keras_image.img_to_array(test_img) / 255.0
+            test_img = np.expand_dims(test_img, axis=0)
+            pred = model.predict(test_img)[0][0]
+        except Exception as e:
+            print(f"Prediction error: {e}")
+            continue
+
+        if pred < 0.5:
+            result = reader.readtext(roi)
+            if result:
+                plate_text = result[0][1]
+                confidence = result[0][2]
+                label = f"Plate: {plate_text}"
+            else:
+                label = "Plate Detected (No text)"
+        else:
+            label = "Plate Not Detected"
+
+        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
+        cv2.putText(frame, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+
+    return frame, confidence, plate_text
+
+# App Config
+st.set_page_config(page_title="Vehicle Plate Identifier", layout="centered")
+
+# Sidebar style
+with st.sidebar:
+    st.markdown("## 🚗 License Plate Scanner")
+    st.markdown("Upload an image or take a photo to detect and read vehicle number plates using AI and OCR.")
+    st.markdown("---")
+    st.info("📌 Tip: Use clear images with visible plates for best results.")
+
+# Title
+st.markdown("<h4 style='text-align: center; color: navy;'>🔍 AI-Powered Vehicle Plate Detection & OCR</h4>", unsafe_allow_html=True)
+
+# Tabs
+tab1, tab2 = st.tabs([
+    "🖼️ **:blue[Upload Vehicle Image]**",
+    "📷 **:green[Use Live Webcam]**"
+])
+
+# Tab 1 - Upload
+with tab1:
+    st.markdown("#### :blue[Upload an image to detect number plate]", unsafe_allow_html=True)
+    uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
+    if uploaded_file:
+        image_input = Image.open(uploaded_file)
+        st.image(image_input, caption="Uploaded Image", width=250)
+
+        if st.button("🔍 Detect from Upload"):
+            with st.spinner("Processing..."):
+                result_img, confidence, label = detect_and_predict(image_input)
+
+            st.image(result_img, caption="Detection Result", channels="RGB", width=250)
+            if confidence:
+                st.metric("Confidence", f"{confidence * 100:.2f}%")
+                st.success(f"Detected Text: {label}")
+            else:
+                st.warning("No plate text detected.")
+
+# Tab 2 - Webcam
+with tab2:
+    st.markdown("#### :green[Capture from your webcam to scan a vehicle plate]", unsafe_allow_html=True)
+    camera_image = st.camera_input("📷 Take a picture using your webcam")
+    if camera_image:
+        try:
+            image_input = Image.open(camera_image)
+            st.image(image_input, caption="Webcam Snapshot", width=250)
+
+            with st.spinner("Analyzing..."):
+                result_img, confidence, label = detect_and_predict(image_input)
+
+            st.image(result_img, caption="Detection Result", channels="RGB", width=250)
+            if confidence is not None:
+                st.metric("Confidence", f"{confidence*100:.2f}%")
+                st.success(f"Detected Text: {label}")
+            else:
+                st.warning("Plate detected but no readable text found.")
+        except Exception as e:
+            st.error(f"❌ Error: {str(e)}")