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import gradio as gr
import os
import zipfile
import json
from io import BytesIO
import base64
from PIL import Image
import uuid
import tempfile
def save_dataset_to_zip(dataset_name, dataset):
# Create a temporary directory
temp_dir = tempfile.mkdtemp()
dataset_path = os.path.join(temp_dir, dataset_name)
os.makedirs(dataset_path, exist_ok=True)
images_dir = os.path.join(dataset_path, 'images')
os.makedirs(images_dir, exist_ok=True)
annotations = []
for idx, entry in enumerate(dataset):
image_data = entry['image']
prompt = entry['prompt']
# Save image to images directory
image_filename = f"{uuid.uuid4().hex}.png"
image_path = os.path.join(images_dir, image_filename)
image = Image.open(BytesIO(base64.b64decode(image_data.split(",")[1])))
image.save(image_path)
# Add annotation
annotations.append({
'file_name': os.path.join('images', image_filename),
'text': prompt
})
# Save annotations to JSONL file
annotations_path = os.path.join(dataset_path, 'annotations.jsonl')
with open(annotations_path, 'w') as f:
for ann in annotations:
f.write(json.dumps(ann) + '\n')
# Create a zip file
zip_buffer = BytesIO()
with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zipf:
for root, dirs, files in os.walk(dataset_path):
for file in files:
abs_file = os.path.join(root, file)
rel_file = os.path.relpath(abs_file, dataset_path)
zipf.write(abs_file, rel_file)
zip_buffer.seek(0)
return zip_buffer
def load_dataset_from_zip(zip_file):
temp_dir = tempfile.mkdtemp()
with zipfile.ZipFile(zip_file.name, 'r') as zip_ref:
zip_ref.extractall(temp_dir)
# Assuming the dataset folder is the first folder in the zip
dataset_name = os.listdir(temp_dir)[0]
dataset_path = os.path.join(temp_dir, dataset_name)
dataset = []
images_dir = os.path.join(dataset_path, 'images')
annotations_path = os.path.join(dataset_path, 'annotations.jsonl')
if os.path.exists(annotations_path):
with open(annotations_path, 'r') as f:
for line in f:
ann = json.loads(line)
file_name = ann['file_name']
prompt = ann['text']
image_path = os.path.join(dataset_path, file_name)
# Read image and convert to base64
with open(image_path, 'rb') as img_f:
image_bytes = img_f.read()
encoded = base64.b64encode(image_bytes).decode()
mime_type = "image/png"
image_data = f"data:{mime_type};base64,{encoded}"
dataset.append({
'image': image_data,
'prompt': prompt
})
return dataset_name, dataset
def display_dataset_html(dataset):
if dataset:
html_content = ""
for idx, entry in enumerate(dataset):
image_data = entry['image']
prompt = entry['prompt']
html_content += f"""
<div style="display: flex; align-items: center; margin-bottom: 10px;">
<div style="width: 50px;">{idx}</div>
<img src="{image_data}" alt="Image {idx}" style="max-height: 100px; margin-right: 10px;"/>
<div>{prompt}</div>
</div>
"""
return html_content
else:
return "<div>No entries in dataset.</div>"
with gr.Blocks() as demo:
gr.Markdown("<h1 style='text-align: center; margin-bottom: 20px;'>Dataset Builder</h1>")
datasets = gr.State({})
current_dataset_name = gr.State("")
dataset_selector = gr.Dropdown(label="Select Dataset", interactive=True)
dataset_html = gr.HTML()
message_box = gr.Textbox(interactive=False, label="Message")
with gr.Tab("Create / Upload Dataset"):
with gr.Row():
with gr.Column():
gr.Markdown("### Create a New Dataset")
dataset_name_input = gr.Textbox(label="New Dataset Name")
create_button = gr.Button("Create Dataset")
with gr.Column():
gr.Markdown("### Upload Existing Dataset")
upload_input = gr.File(label="Upload Dataset Zip", file_types=['.zip'])
upload_button = gr.Button("Upload Dataset")
def create_dataset(name, datasets):
if not name:
return gr.update(), "Please enter a dataset name."
if name in datasets:
return gr.update(), f"Dataset '{name}' already exists."
datasets[name] = []
return gr.Dropdown.update(choices=list(datasets.keys()), value=name), f"Dataset '{name}' created."
create_button.click(
create_dataset,
inputs=[dataset_name_input, datasets],
outputs=[dataset_selector, message_box]
)
def upload_dataset(zip_file, datasets):
if zip_file is None:
return gr.update(), "Please upload a zip file."
dataset_name, dataset = load_dataset_from_zip(zip_file)
if dataset_name in datasets:
return gr.update(), f"Dataset '{dataset_name}' already exists."
datasets[dataset_name] = dataset
return gr.Dropdown.update(choices=list(datasets.keys()), value=dataset_name), f"Dataset '{dataset_name}' uploaded."
upload_button.click(
upload_dataset,
inputs=[upload_input, datasets],
outputs=[dataset_selector, message_box]
)
def select_dataset(dataset_name, datasets):
if dataset_name in datasets:
dataset = datasets[dataset_name]
html_content = display_dataset_html(dataset)
return current_dataset_name.update(value=dataset_name), dataset_html.update(value=html_content), ""
else:
return current_dataset_name.update(value=""), dataset_html.update(value="<div>Select a dataset.</div>"), ""
dataset_selector.change(
select_dataset,
inputs=[dataset_selector, datasets],
outputs=[current_dataset_name, dataset_html, message_box]
)
with gr.Tab("Add Entry"):
with gr.Row():
image_input = gr.Image(label="Upload Image")
prompt_input = gr.Textbox(label="Prompt")
add_button = gr.Button("Add Entry")
def add_entry(image_data, prompt, current_dataset_name, datasets):
if not current_dataset_name:
return datasets, dataset_html.update(), "No dataset selected."
if image_data is None or not prompt:
return datasets, dataset_html.update(), "Please provide both an image and a prompt."
datasets[current_dataset_name].append({'image': image_data, 'prompt': prompt})
html_content = display_dataset_html(datasets[current_dataset_name])
return datasets, dataset_html.update(value=html_content), f"Entry added to dataset '{current_dataset_name}'."
add_button.click(
add_entry,
inputs=[image_input, prompt_input, current_dataset_name, datasets],
outputs=[datasets, dataset_html, message_box]
)
with gr.Tab("Edit / Delete Entry"):
index_input = gr.Number(label="Entry Index", precision=0)
new_prompt_input = gr.Textbox(label="New Prompt (for Edit)")
with gr.Row():
edit_button = gr.Button("Edit Entry")
delete_button = gr.Button("Delete Entry")
def edit_entry(index, new_prompt, current_dataset_name, datasets):
if not current_dataset_name:
return datasets, dataset_html.update(), "No dataset selected."
if index is None or not new_prompt:
return datasets, dataset_html.update(), "Please provide both index and new prompt."
index = int(index)
if 0 <= index < len(datasets[current_dataset_name]):
datasets[current_dataset_name][index]['prompt'] = new_prompt
html_content = display_dataset_html(datasets[current_dataset_name])
return datasets, dataset_html.update(value=html_content), f"Entry {index} updated."
else:
return datasets, dataset_html.update(), "Invalid index."
edit_button.click(
edit_entry,
inputs=[index_input, new_prompt_input, current_dataset_name, datasets],
outputs=[datasets, dataset_html, message_box]
)
def delete_entry(index, current_dataset_name, datasets):
if not current_dataset_name:
return datasets, dataset_html.update(), "No dataset selected."
if index is None:
return datasets, dataset_html.update(), "Please provide an index."
index = int(index)
if 0 <= index < len(datasets[current_dataset_name]):
del datasets[current_dataset_name][index]
html_content = display_dataset_html(datasets[current_dataset_name])
return datasets, dataset_html.update(value=html_content), f"Entry {index} deleted."
else:
return datasets, dataset_html.update(), "Invalid index."
delete_button.click(
delete_entry,
inputs=[index_input, current_dataset_name, datasets],
outputs=[datasets, dataset_html, message_box]
)
with gr.Tab("Download Dataset"):
download_button = gr.Button("Download Dataset")
download_output = gr.File(label="Download Zip")
def download_dataset(current_dataset_name, datasets):
if not current_dataset_name:
return None, "No dataset selected."
zip_buffer = save_dataset_to_zip(current_dataset_name, datasets[current_dataset_name])
return zip_buffer.getvalue(), f"Dataset '{current_dataset_name}' is ready for download."
download_button.click(
download_dataset,
inputs=[current_dataset_name, datasets],
outputs=[download_output, message_box]
)
# Initially update dataset_html
demo.load(
lambda datasets: gr.Dropdown.update(choices=list(datasets.keys())),
inputs=None,
outputs=dataset_selector
)
demo.launch() |