import streamlit as st
import cv2
import numpy as np
# Import your chosen deep learning framework (TensorFlow or PyTorch)
# ...
import tensorflow

# Load the pre-trained object detection model
model = cv2.dnn_DetectionModel("path/to/model.weights", "path/to/model.cfg")
model.setInputParams(size=(416, 416), scale=1/255)

# Optional: Load EasyOCR model if using
reader = EasyOCR("en")  # Change "en" to your desired language code

def detect_plates(image):
    # Preprocess image for model input (resizing, normalization, etc.)
    # ...
    classes, confidences, boxes = model.detect(image)

    for (class_id, confidence, box) in zip(classes.flatten(), confidences.flatten(), boxes):
        if class_id == (class_index for class_index in range(len(model.names)) if model.names[class_index] == "license_plate"):  # Adjust class index based on your model
            x_min, y_min, x_max, y_max = box
            plate_roi = image[y_min:y_max, x_min:x_max]

            # Perform character recognition (if not using EasyOCR, implement your own)
            plate_text = "..."
            if reader is not None:
                result = reader.readtext(plate_roi)
                plate_text = result[0][1]

            # Display bounding box and plate text (or confidence score if not using OCR)
            cv2.rectangle(image, (x_min, y_min), (x_max, y_max), (255, 0, 0), 2)
            if reader is not None:
                cv2.putText(image, plate_text, (x_min, y_min - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 0), 2)
            else:
                cv2.putText(image, f"Confidence: {confidence:.2f}", (x_min, y_min - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 0), 2)

    return image

def main():
    """Streamlit app"""
    st.title("Number Plate Detection App")

    uploaded_file = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"])
    if uploaded_file is not None:
        image = cv2.imdecode(np.fromstring(uploaded_file.read(), np.uint8), cv2.IMREAD_COLOR)
        results = detect_plates