import mlcroissant._src.operation_graph.operations.download as dl_mod
import requests
import os
# Make sure the HF token is loaded
HF_TOKEN = os.environ.get("HF_TOKEN")
# Set the environment variables Croissant expects
os.environ["CROISSANT_BASIC_AUTH_USERNAME"] = "hf_user"
os.environ["CROISSANT_BASIC_AUTH_PASSWORD"] = HF_TOKEN or ""
print("[DEBUG] HF_TOKEN is", "set" if HF_TOKEN else "missing")
print("[DEBUG] Basic auth env set for Croissant")
import gradio as gr
import json
import time
import traceback
from validation import validate_json, validate_croissant, validate_records, generate_validation_report
def process_file(file):
results = []
json_data = None
filename = file.name.split("/")[-1]
# Check 1: JSON validation
json_valid, json_message, json_data = validate_json(file.name)
json_message = json_message.replace("\nā\n", "\n")
results.append(("JSON Format Validation", json_valid, json_message, "pass" if json_valid else "error"))
if not json_valid:
return results, None
# Check 2: Croissant validation
croissant_valid, croissant_message = validate_croissant(json_data)
croissant_message = croissant_message.replace("\nā\n", "\n")
results.append(("Croissant Schema Validation", croissant_valid, croissant_message, "pass" if croissant_valid else "error"))
if not croissant_valid:
return results, None
# Check 3: Records validation (with timeout-safe and error-specific logic)
records_valid, records_message, records_status = validate_records(json_data)
records_message = records_message.replace("\nā\n", "\n")
results.append(("Records Generation Test", records_valid, records_message, records_status))
# Generate final report
report = generate_validation_report(filename, json_data, results)
return results, report
def create_ui():
with gr.Blocks(theme=gr.themes.Soft()) as app:
gr.HTML("

")
gr.Markdown("# š„ Croissant Validator for NeurIPS")
gr.Markdown("""
Upload your Croissant JSON-LD file or enter a URL to validate if it meets the requirements for NeurIPS submission. Read more about why this is required..
*It doesn't matter where your dataset is hosted. It can be Hugging Face, Kaggle, OpenML, Harvard Dataverse, self-hosted, or elsewhere.*
""")
gr.Markdown("""
The validator will check:
1. If the file is valid JSON
2. If it passes Croissant schema validation
3. If records can be generated within a reasonable time
""")
# Track the active tab for conditional UI updates
active_tab = gr.State("upload") # Default to upload tab
# Create a container for the entire input section
with gr.Group():
# Input tabs
with gr.Tabs() as tabs:
with gr.TabItem("Upload File", id="upload_tab"):
file_input = gr.File(label="Upload Croissant JSON-LD File", file_types=[".json", ".jsonld"])
validate_btn = gr.Button("Validate Uploaded File", variant="primary")
with gr.TabItem("URL Input", id="url_tab"):
url_input = gr.Textbox(
label="Enter Croissant JSON-LD URL",
placeholder="e.g. https://huggingface.co/api/datasets/facebook/natural_reasoning/croissant"
)
fetch_btn = gr.Button("Fetch and Validate", variant="primary")
# Change initial message to match upload tab
upload_progress = gr.HTML(
"""Ready for upload
""",
visible=True)
# Now create the validation results section in a separate group
with gr.Group():
# Validation results
validation_results = gr.HTML(visible=False)
validation_progress = gr.HTML(visible=False)
# Collapsible report section
with gr.Accordion("Download full validation report", visible=False, open=False) as report_group:
with gr.Column():
report_md = gr.File(
label="Download Report",
visible=True,
file_types=[".md"]
)
report_text = gr.Textbox(
label="Report Content",
visible=True,
show_copy_button=True,
lines=10
)
# Define CSS for the validation UI
gr.HTML("""
""")
# Update helper messages based on tab changes
def on_tab_change(evt: gr.SelectData):
tab_id = evt.value
if tab_id == "Upload File":
return [
"upload",
"""Ready for upload
""",
gr.update(visible=False),
gr.update(visible=False), # Hide report group
None, # Clear report text
None, # Clear report file
None, # Clear file input
gr.update(value="") # Clear URL input
]
else:
return [
"url",
"""Enter a URL to fetch
""",
gr.update(visible=False),
gr.update(visible=False), # Hide report group
None, # Clear report text
None, # Clear report file
None, # Clear file input
gr.update(value="") # Clear URL input
]
def on_copy_click(report):
return report
def on_download_click(report, file_name):
report_file = f"report_{file_name}.md"
with open(report_file, "w") as f:
f.write(report)
return report_file
def on_file_upload(file):
if file is None:
return [
"""Ready for upload
""",
gr.update(visible=False),
gr.update(visible=False), # Hide report group
None, # Clear report text
None # Clear report file
]
return [
"""ā
File uploaded successfully
""",
gr.update(visible=False),
gr.update(visible=False), # Hide report group
None, # Clear report text
None # Clear report file
]
def fetch_from_url(url):
if not url:
return [
"""Please enter a URL
""",
gr.update(visible=False),
gr.update(visible=False),
None,
None
]
try:
# Fetch JSON from URL
response = requests.get(url, timeout=10)
response.raise_for_status()
json_data = response.json()
# Process validation
progress_html = """ā
JSON fetched successfully from URL
"""
# Validate the fetched JSON
results = []
results.append(("JSON Format Validation", True, "The URL returned valid JSON."))
croissant_valid, croissant_message = validate_croissant(json_data)
results.append(("Croissant Schema Validation", croissant_valid, croissant_message))
if not croissant_valid:
return [
"""ā
JSON fetched successfully from URL
""",
build_results_html(results),
gr.update(visible=False),
None,
None
]
records_valid, records_message, records_status = validate_records(json_data)
results.append(("Records Generation Test (Optional)", records_valid, records_message, records_status))
# Generate report
report = generate_validation_report(url.split("/")[-1], json_data, results)
report_filename = f"report_croissant-validation_{json_data.get('name', 'unnamed')}.md"
if report:
with open(report_filename, "w") as f:
f.write(report)
return [
"""ā
JSON fetched successfully from URL
""",
build_results_html(results),
gr.update(visible=True),
report,
report_filename
]
except requests.exceptions.RequestException as e:
error_message = f"Error fetching URL: {str(e)}"
return [
f"""{error_message}
""",
gr.update(visible=False),
gr.update(visible=False),
None,
None
]
except json.JSONDecodeError as e:
error_message = f"URL did not return valid JSON: {str(e)}"
return [
f"""{error_message}
""",
gr.update(visible=False),
gr.update(visible=False),
None,
None
]
except Exception as e:
error_message = f"Unexpected error: {str(e)}"
return [
f"""{error_message}
""",
gr.update(visible=False),
gr.update(visible=False),
None,
None
]
def build_results_html(results):
html = ''
for i, result in enumerate(results):
if len(result) == 4:
test_name, passed, message, status = result
else:
test_name, passed, message = result
status = "pass" if passed else "error"
if status == "pass":
status_class = "status-success"
status_icon = "ā"
message_with_emoji = "ā
" + message
elif status == "warning":
status_class = "status-warning"
status_icon = "?"
message_with_emoji = "ā ļø Could not automatically generate records. This is oftentimes not an issue (e.g. datasets could be too large or too complex), and it's not required to pass this test to submit to NeurIPS.\n\n" + message
else: # error
status_class = "status-error"
status_icon = "ā"
message_with_emoji = "ā " + message
message_with_emoji = message_with_emoji.replace("\n", "
")
html += f'''
'''
html += '
'
return gr.update(value=html, visible=True)
def on_validate(file):
if file is None:
return [
gr.update(visible=False), # validation_results
gr.update(visible=False), # validation_progress
gr.update(visible=False), # report_group
None, # report_text
None # report_md
]
# Process the file and get results
results, report = process_file(file)
# Extract dataset name from the JSON for the report filename
try:
with open(file.name, 'r') as f:
json_data = json.load(f)
dataset_name = json_data.get('name', 'unnamed')
except:
dataset_name = 'unnamed'
# Save report to file with new naming convention
report_filename = f"report_croissant-validation_{dataset_name}.md"
if report:
with open(report_filename, "w") as f:
f.write(report)
# Return final state
return [
build_results_html(results), # validation_results
gr.update(visible=False), # validation_progress
gr.update(visible=True) if report else gr.update(visible=False), # report_group
report if report else None, # report_text
report_filename if report else None # report_md
]
# Connect UI events to functions with updated outputs
tabs.select(
on_tab_change,
None,
[active_tab, upload_progress, validation_results, report_group, report_text, report_md, file_input, url_input]
)
file_input.change(
on_file_upload,
inputs=file_input,
outputs=[upload_progress, validation_results, report_group, report_text, report_md]
)
# Add progress state handling
def show_progress():
progress_html = """
"""
return [
gr.update(visible=False), # validation_results
gr.update(visible=True, value=progress_html), # validation_progress
gr.update(visible=False), # report_group
None, # report_text
None # report_md
]
validate_btn.click(
fn=show_progress,
inputs=None,
outputs=[validation_results, validation_progress, report_group, report_text, report_md],
queue=False
).then(
fn=on_validate,
inputs=file_input,
outputs=[validation_results, validation_progress, report_group, report_text, report_md]
)
fetch_btn.click(
fetch_from_url,
inputs=url_input,
outputs=[upload_progress, validation_results, report_group, report_text, report_md]
)
# Footer
gr.HTML("""
""")
gr.HTML("""
ā ļø It is possible that this validator is currently being used by a lot of people at the same time, which may trigger rate limiting by the platform hosting your data.
The app will then try again and may get into a very long loop. If it takes too long to run, we recommend using any of the following options:
- š Click the button with the three dots (āÆ) above and select "Duplicate this Space" to run this app in your own Hugging Face space.
- š» Click the button with the three dots (āÆ) above and select "Run Locally" and then "Clone (git)" to get instructions to run the checker locally. You can also use docker option (you don't need the tokens).
- š„ Run the Croissant validation code yourself (GitHub), e.g. with these scripts (validate and load).
""")
return app
if __name__ == "__main__":
app = create_ui()
app.launch()