image = cv.imread("/content/Demo2.png")
def preprocess_image(image):
  gray_image = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
  ret, bin_image = cv.threshold(gray_image, 127, 255, cv.THRESH_OTSU)

  bin_image = cv.copyMakeBorder(bin_image, int(0.10 * image.shape[0]), int(0.05 * image.shape[0]), int(0.05 * image.shape[1]), int(0.10 * image.shape[1]), cv.BORDER_CONSTANT, value=(255, 255, 255))
  return bin_image

bin_image = preprocess_image(image)

def split_image_into_lines(image):
  lines = []
  while (image.shape[0] > 20):
    flag1 = 0
    flag2 = 0

    for i in range(image.shape[0]):
      if flag1 == 0:
        for j in range(image.shape[1]):
          pixel_value = image[i][j]
          if (pixel_value == 0) & (flag1 == 0):
            start = i
            flag1 = 1
            flag2 = 1
      if flag2 == 1:
        num_white_pixels = np.sum(image[i + 1] == 255)
        if (num_white_pixels > 0.98 * image.shape[1]):
          end = i + 1
          break


    line = image[int(start - 0.2 * (end - start + 1)): int(end + 1 + 0.2 * (end - start + 1))][:]
    if line.shape[0] > 20:
      line_rgb = cv.cvtColor(line, cv.COLOR_GRAY2RGB)
      lines.append(line_rgb)

    pads = 255 * np.ones((20, image.shape[1]), dtype='uint8')
    new_image = image[int(end + 2 -(0.2 * (end - start + 1))):][:]
    new_image = np.concatenate((pads, new_image))
    image = new_image


  return lines

lines = split_image_into_lines(bin_image)


def generate_text(line):
  pixel_values = processor(images=line, return_tensors="pt").pixel_values
  generated_ids = model.generate(pixel_values, max_new_tokens=50)
  generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
  return generated_text

with ProcessPoolExecutor() as executor:
    results = ' '.join(executor.map(generate_text, lines))
    #improve results with llm

    client = OpenAI()

    completion = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {
                "role": "user",

                "content": f"I have a string that was extracted from an image of handwritten text. The extraction process introduced minor grammatical, spelling, and punctuation errors. Please carefully review the text below and make any necessary corrections to improve readability and accuracy while preserving the original meaning. Do not change the content or style beyond necessary corrections. Return the corrected text only without adding any headings, explanations, or extra formatting. Text: {results}"
            }
        ]
    )

    improved_text = completion.choices[0].message.content



def put_text(text, font, font_scale, color, thickness, max_width, out_image_width, top_margin):
  words = text.split(" ")
  lines = []
  current_line = ""

  for word in words:
    if cv.getTextSize(current_line + " " + word, font, font_scale, thickness)[0][0] <= (max_width * out_image_width):
      current_line += " " + word
    else:
      lines.append(current_line)
      current_line = word

  lines.append(current_line)

  out_image_height = sum([cv.getTextSize(line, font, font_scale, thickness)[0][1] for line in lines]) + 2 * top_margin + 20 * (len(lines) - 1) #20 is the gap between two consecutive lines

  out_image = 255 * (np.ones((out_image_height, out_image_width, 3), dtype=np.uint8))

  top = top_margin
  for line in lines:
    cv.putText(out_image, line.strip(), (int(((1 - max_width) * out_image_width) / 2), top), font, font_scale, 0, thickness, lineType=cv.LINE_AA)
    top += cv.getTextSize(line.strip(), font, font_scale, thickness)[0][1] + 20

  return out_image

font = cv.FONT_HERSHEY_DUPLEX
font_scale = 2
color = 0
thickness = 2
max_width = 0.9
out_image_width = 1500
top_margin = 100

out_image = put_text(improved_text, font, font_scale, color, thickness, max_width, out_image_width, top_margin)