app.py

# 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_plate_Detection.keras")
# plate_detector = cv2.CascadeClassifier("haarcascade_russian_plate_number.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)

#     # Ensure output image is same size as input
#     result_img = Image.fromarray(frame)
#     return result_img, 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:
#     col1, col2 = st.columns([1, 2])
#     uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
#     if uploaded_file:
#         image_input = Image.open(uploaded_file)
#         with col1:
#             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)

#             with col2:
#                 st.image(result_img, caption="Detection Result", 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:
#     col1, col2 = st.columns([1, 2])
#     with col1:
#         camera_image = st.camera_input("📷 Take a picture using your webcam")
#     if camera_image:
#         try:
#             image_input = Image.open(camera_image)
#             with st.spinner("Analyzing..."):
#                 result_img, confidence, label = detect_and_predict(image_input)
#             with col2:
#                 st.image(result_img, caption="Detection Result", 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 OCR tools
model = load_model("Vehicle_number_plate_Detection.keras")
plate_detector = cv2.CascadeClassifier("haarcascade_russian_plate_number.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"

        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)

    # Convert back to PIL and resize to 450x450 for display
    result_img = Image.fromarray(frame)
    result_img = result_img.resize((450, 450))
    return result_img, confidence, plate_text


# Streamlit 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:
    col1, col2 = st.columns([1, 2])
    with col1:
        uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"], key="uploader")
        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)

                with col2:
                    st.image(result_img, caption="Detection Result (450x450)", use_container_width=False)
                    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:
    col1, col2 = st.columns([1, 2])
    with col1:
        camera_image = st.camera_input("📷 Take a picture using your webcam")
    if camera_image:
        try:
            image_input = Image.open(camera_image)
            with st.spinner("Analyzing..."):
                result_img, confidence, label = detect_and_predict(image_input)

            with col2:
                st.image(result_img, caption="Detection Result (450x450)", use_container_width=False)
                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)}")