app.py

import gradio as gr
import logging
from roboflow import Roboflow
from PIL import Image, ImageDraw
import cv2
import numpy as np
import os
from math import atan2, degrees
import asyncio
from pyppeteer import launch
import multiprocessing

# Configure logging
logging.basicConfig(
    level=logging.DEBUG,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler("debug.log"),
        logging.StreamHandler()
    ]
)

# Roboflow and model configuration
ROBOFLOW_API_KEY = "KUP9w62eUcD5PrrRMJsV"  # Replace with your API key
PROJECT_NAME = "model_verification_project"
VERSION_NUMBER = 2

# ----------------------------
# New: Run Pyppeteer code in a separate process
# ----------------------------
def generate_handwriting_image_process(text_prompt, screenshot_path, return_dict):
    """
    This function runs in a separate process so that the Pyppeteer code
    runs in the main thread of that process.
    """
    import asyncio
    from pyppeteer import launch

    async def _generate():
        browser = await launch(headless=True, args=['--no-sandbox', '--disable-setuid-sandbox'])
        page = await browser.newPage()
        await page.goto('https://www.calligraphr.com/en/font/', {'waitUntil': 'networkidle2'})
        await page.waitForSelector('#text-input')
        await page.type('#text-input', text_prompt)
        await asyncio.sleep(2)  # Wait for the handwriting preview to render

        # Adjust these clip dimensions as needed for the correct area
        await page.screenshot({
            'path': screenshot_path,
            'clip': {'x': 100, 'y': 200, 'width': 600, 'height': 150}
        })
        await browser.close()
        return screenshot_path

    # Create a new event loop for this process
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)
    result = loop.run_until_complete(_generate())
    return_dict['result'] = result

def get_handwriting_image(text_prompt, screenshot_path="/tmp/handwriting.png"):
    manager = multiprocessing.Manager()
    return_dict = manager.dict()
    process = multiprocessing.Process(target=generate_handwriting_image_process, args=(text_prompt, screenshot_path, return_dict))
    process.start()
    process.join()
    return return_dict.get('result', None)

# ----------------------------
# Helper: Detect paper angle within bounding box
# ----------------------------
def detect_paper_angle(image, bounding_box):
    x1, y1, x2, y2 = bounding_box
    roi = np.array(image)[y1:y2, x1:x2]
    gray = cv2.cvtColor(roi, cv2.COLOR_RGBA2GRAY)
    edges = cv2.Canny(gray, 50, 150)
    lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=100, minLineLength=50, maxLineGap=10)
    if lines is not None:
        longest_line = max(lines, key=lambda line: np.linalg.norm((line[0][2] - line[0][0], line[0][3] - line[0][1])))
        x1_line, y1_line, x2_line, y2_line = longest_line[0]
        dx = x2_line - x1_line
        dy = y2_line - y1_line
        angle = degrees(atan2(dy, dx))
        return angle
    else:
        return 0

# ----------------------------
# Main processing function
# ----------------------------
def process_image(image, text):
    try:
        # Initialize Roboflow
        rf = Roboflow(api_key=ROBOFLOW_API_KEY)
        logging.debug("Initialized Roboflow API.")
        project = rf.workspace().project(PROJECT_NAME)
        logging.debug("Accessed project in Roboflow.")
        model = project.version(VERSION_NUMBER).model
        logging.debug("Loaded model from Roboflow.")

        # Save input image temporarily
        input_image_path = "/tmp/input_image.jpg"
        image.save(input_image_path)
        logging.debug(f"Input image saved to {input_image_path}.")

        # Perform inference
        logging.debug("Performing inference on the image...")
        prediction = model.predict(input_image_path, confidence=70, overlap=50).json()
        logging.debug(f"Inference result: {prediction}")

        pil_image = image.convert("RGBA")
        logging.debug("Converted image to RGBA mode.")

        # Process each detected object (assumed to be white paper)
        for obj in prediction['predictions']:
            white_paper_width = obj['width']
            white_paper_height = obj['height']
            padding_x = int(white_paper_width * 0.1)
            padding_y = int(white_paper_height * 0.1)
            box_width = white_paper_width - 2 * padding_x
            box_height = white_paper_height - 2 * padding_y
            logging.debug(f"Padded white paper dimensions: width={box_width}, height={box_height}.")

            x1_padded = int(obj['x'] - white_paper_width / 2 + padding_x)
            y1_padded = int(obj['y'] - white_paper_height / 2 + padding_y)
            x2_padded = int(obj['x'] + white_paper_width / 2 - padding_x)
            y2_padded = int(obj['y'] + white_paper_height / 2 - padding_y)

            angle = detect_paper_angle(np.array(image), (x1_padded, y1_padded, x2_padded, y2_padded))
            logging.debug(f"Detected paper angle: {angle} degrees.")

            # For debugging: draw bounding box (optional)
            debug_layer = pil_image.copy()
            debug_draw = ImageDraw.Draw(debug_layer)
            debug_draw.rectangle([(x1_padded, y1_padded), (x2_padded, y2_padded)], outline="red", width=3)
            debug_layer.save("/tmp/debug_bounding_box.png")
            logging.debug("Saved bounding box debug image to /tmp/debug_bounding_box.png.")

            # Generate handwriting image using the separate process
            handwriting_path = get_handwriting_image(text, "/tmp/handwriting.png")
            if not handwriting_path:
                logging.error("Handwriting image generation failed.")
                continue

            handwriting_img = Image.open(handwriting_path).convert("RGBA")
            handwriting_img = handwriting_img.resize((box_width, box_height), Image.ANTIALIAS)
            rotated_handwriting = handwriting_img.rotate(-angle, resample=Image.BICUBIC, expand=True)

            text_layer = Image.new("RGBA", pil_image.size, (255, 255, 255, 0))
            paste_x = int(obj['x'] - rotated_handwriting.size[0] / 2)
            paste_y = int(obj['y'] - rotated_handwriting.size[1] / 2)
            text_layer.paste(rotated_handwriting, (paste_x, paste_y), rotated_handwriting)
            pil_image = Image.alpha_composite(pil_image, text_layer)
            logging.debug("Handwriting layer composited onto the original image.")

        output_image_path = "/tmp/output_image.png"
        pil_image.convert("RGB").save(output_image_path)
        logging.debug(f"Output image saved to {output_image_path}.")
        return output_image_path

    except Exception as e:
        logging.error(f"Error during image processing: {e}")
        return None

# ----------------------------
# Gradio interface function
# ----------------------------
def gradio_inference(image, text):
    logging.debug("Starting Gradio inference.")
    result_path = process_image(image, text)
    if result_path:
        logging.debug("Gradio inference successful.")
        return result_path, result_path, "Processing complete! Download the image below."
    logging.error("Gradio inference failed.")
    return None, None, "An error occurred while processing the image. Please check the logs."

# ----------------------------
# Gradio interface definition
# ----------------------------
interface = gr.Interface(
    fn=gradio_inference,
    inputs=[
        gr.Image(type="pil", label="Upload an Image"),
        gr.Textbox(label="Enter Text to Overlay")
    ],
    outputs=[
        gr.Image(label="Processed Image Preview"),
        gr.File(label="Download Processed Image"),
        gr.Textbox(label="Status")
    ],
    title="Roboflow Detection with Handwriting Overlay",
    description="Upload an image and enter text to overlay. The Roboflow model detects the white paper area, and a handwriting image is generated via Calligraphr using Pyppeteer. The output image is composited accordingly.",
    allow_flagging="never"
)

if __name__ == "__main__":
    logging.debug("Launching Gradio interface.")
    interface.launch(share=True)