app.py

import gradio as gr
import cv2
import numpy as np
from ultralytics import YOLO
import pytesseract
import subprocess

def load_model(model_path):
    """
    Loads the YOLO model from the specified path.

    Parameters:
    model_path (str): Path to the YOLO model file.

    Returns:
    YOLO: Loaded YOLO model.
    """
    return YOLO(model_path)

def predict_and_plot(image, model):
    """
    Predicts and plots the bounding boxes on the given image using the trained YOLO model.
    Also performs OCR on the detected bounding boxes to extract text.

    Parameters:
    image (numpy.ndarray): Input image.
    model (YOLO): The trained YOLO model.

    Returns:
    numpy.ndarray: Image with bounding boxes drawn.
    str: Detected text from the bounding boxes.
    """
    if image is None:
        return None, "Error: No image provided."

    # Perform prediction on the input image using the model
    results = model.predict(image, device='cpu')

    detected_texts = []

    # Extract the bounding boxes and labels from the results
    for result in results:
        for box in result.boxes:
            # Get the coordinates of the bounding box
            x1, y1, x2, y2 = map(int, box.xyxy[0])
            # Get the confidence score of the prediction
            confidence = box.conf[0]

            # Draw the bounding box on the image
            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
            # Draw the confidence score near the bounding box
            cv2.putText(image, f'{confidence*100:.2f}%', (x1, y1 - 10),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 255, 0), 2)

    return image

def main(image):
    """
    Main function to handle the prediction and plotting.

    Parameters:
    image (numpy.ndarray): Input image.

    Returns:
    numpy.ndarray: Image with bounding boxes drawn.
    str: Detected text from the bounding boxes.
    """
    # Load the model
    model = load_model('best.pt')
    return predict_and_plot(image, model)

# Create the Gradio interface
iface = gr.Interface(
    fn=main,
    inputs=gr.Image(type="numpy", value="Cars418.png"),
    outputs=[
        gr.Image(type="numpy", label="Output Image with Bounding Boxes")
    ],
    title="License Plate Detection",
    description="Click 'Submit' to dodetection on current image or click 'Clear' and then Upload an image to detect license plates.",
    allow_flagging="never"  # Disable flagging
)

# Launch the Gradio app
if __name__ == "__main__":
    iface.launch()