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Create a readme for the following code:
app.py
import gradio as gr
import os
import shutil
def process_image(input_image):
# Step 1: Create or clear the 'images' folder
images_folder = "images"
if os.path.exists(images_folder):
shutil.rmtree(images_folder) # Remove the folder if it exists
os.makedirs(images_folder) # Create a new 'images' folder
# Step 2: Save the input image into the 'images' folder
input_image_path = os.path.join(images_folder, "input_image.png")
input_image.save(input_image_path)
# # Step 3: Perform some actions (placeholder)
os.system("python run_google_lens.py")
os.system("python run_clean_images.py")
# Step 4: Zip the 'images' folder
zip_filename = "images.zip"
shutil.make_archive("images", "zip", images_folder)
shutil.rmtree(images_folder)
# Step 5: Return the path to the ZIP file
return zip_filename
# Set up the Gradio interface using Interface instead of Blocks
iface = gr.Interface(
fn=process_image,
inputs=gr.Image(type="pil", label="Upload an Image"),
outputs=gr.File(label="Download Images Folder"),
title="Image Processor",
description="Upload an image and download a folder with processed images.",
allow_flagging="auto", # Disable the flag button
)
# Launch the app
iface.launch()
---
run_clean_images.py
import os
import logging
from cleanvision.imagelab import Imagelab
# Set up logging
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s",
)
def delete_images_with_issues(directory):
# Initialize Imagelab with the directory of images
imagelab = Imagelab(directory)
# Run the inspection to identify images with issues
issues = imagelab.find_issues()
issue_columns = imagelab.issues.filter(like="issue")
# Use where to replace rows that don't have any True value with NaN
filtered_df = imagelab.issues.where(issue_columns.any(axis=1))
# Drop the rows with NaN values (i.e., rows where no issue column was True)
filtered_df = filtered_df.dropna()
# Display the filtered DataFrame
filtered_df.index.to_list()
# Iterate over the issues and delete the corresponding images
for issue in filtered_df.index.to_list():
image_path = issue
try:
os.remove(image_path)
logging.info(f"Deleted: {image_path}")
except Exception as e:
logging.error(f"Error deleting {image_path}: {e}")
if __name__ == "__main__":
# path = "/Users/andrewmayes/Project/document-type-detection/data/non_object"
path = "/home/user/app/images/"
delete_images_with_issues(path)
---
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