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import gradio as gr
from PIL import Image
import os
import numpy as np
import cv2
import time
# Directory to store the PDF files
output_directory = "base"
os.makedirs(output_directory, exist_ok=True)
# Initialize an empty list to store the uploaded images as NumPy arrays
images_list = []
# Function to add an uploaded image (NumPy array) to the list
def add_image(image):
print(image) # Display the image array
global images_list
images_list.append(image)
return f"Image added! Current number of images: {len(images_list)}"
# Function to convert NumPy array to PIL Image
def numpy_to_pil(np_img):
if np_img.ndim == 2: # Grayscale
return Image.fromarray(np_img)
elif np_img.shape[2] == 3: # RGB
return Image.fromarray(cv2.cvtColor(np_img, cv2.COLOR_BGR2RGB))
elif np_img.shape[2] == 4: # RGBA
return Image.fromarray(cv2.cvtColor(np_img, cv2.COLOR_BGRA2RGBA))
else:
raise ValueError("Unsupported image format")
# Function to merge images into a PDF and save it
def merge_images_to_pdf():
if not images_list:
return "No images to merge!"
# Convert NumPy arrays to PIL Images
image_objs = [numpy_to_pil(img) for img in images_list]
# Save the images as a PDF
pdf_output_path = os.path.join(output_directory, str(time.ctime())+'.pdf')
image_objs[0].save(pdf_output_path, save_all=True, append_images=image_objs[1:])
# Clear the images list after saving the PDF
images_list.clear()
return f"PDF saved successfully at {pdf_output_path}!"
# Create the Gradio interface
with gr.Blocks() as demo:
gr.Markdown("### Image to PDF Converter")
# Image upload component (uses webcam or upload)
image_input = gr.Image(source="webcam", streaming=True, type="numpy")
# Button to add the image to the list
add_button = gr.Button("Add Image")
# Button to merge the images into a PDF
merge_button = gr.Button("Merge Images to PDF")
# Output text to display status
output_text = gr.Textbox(label="Status")
# Event handling
add_button.click(add_image, inputs=image_input, outputs=output_text)
merge_button.click(merge_images_to_pdf, outputs=output_text)
# Launch the Gradio app
demo.launch()
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