import gradio as gr
import cv2
import numpy as np

# Function to process the image and extract contours
def extract_contours(image, min_contour_area=100):
    # Convert the uploaded image from RGB to BGR format for OpenCV processing
    image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

    # Step 1: Convert to grayscale
    gray = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2GRAY)

    # Step 2: Apply Gaussian blur to reduce noise
    blurred = cv2.GaussianBlur(gray, (5, 5), 0)

    # Step 3: Apply Canny edge detection with low thresholds for finer edges
    edges = cv2.Canny(blurred, 30, 100)  # Adjust thresholds as needed

    # Step 4: Apply morphological operations to refine edges
    kernel = np.ones((3, 3), np.uint8)
    edges_dilated = cv2.dilate(edges, kernel, iterations=1)  # Dilation to emphasize edges
    edges_eroded = cv2.erode(edges_dilated, kernel, iterations=1)  # Erosion to refine

    # Step 5: Find contours
    contours, _ = cv2.findContours(edges_eroded, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    # Step 6: Create a blank white background
    white_background = np.ones_like(image_bgr) * 255  # White background

    # Step 7: Draw contours on the white background, excluding small contours
    for contour in contours:
        if cv2.contourArea(contour) > min_contour_area:
            cv2.drawContours(white_background, [contour], -1, (0, 0, 0), thickness=1)  # Thinner lines

    # Convert the result back to RGB for displaying
    result_rgb = cv2.cvtColor(white_background, cv2.COLOR_BGR2RGB)

    return result_rgb

# Gradio interface
interface = gr.Interface(
    fn=extract_contours,
    inputs=[
        gr.Image(type="numpy", label="Upload Image"),
        gr.Slider(50, 500, step=10, value=100, label="Minimum Contour Area")  # Use 'value' instead of 'default'
    ],
    outputs=gr.Image(type="numpy", label="Processed Image"),
    title="Edge Detection and Contour Extraction",
    description="Upload an image to extract contours, excluding small areas like text labels. Adjust the minimum contour area using the slider."
)

# Launch the Gradio app
interface.launch()