Python Quickstart

pip install pangram-sdk

export PANGRAM_API_KEY=<your API key>

from pangram import Pangram

pangram_client = Pangram(api_key="your-api-key")

available_models = pangram_client.list_models()
print(available_models)  # e.g., ["default", "pangram-4"]

from pangram import Pangram

pangram_client = Pangram()
result = pangram_client.predict(text, model="pangram-4")
stage = result['stage']  # "STAGE_SUCCESS" after predict() completes.
version = result['version']  # "4.0" for Pangram 4.

# Analysis with AI-assistance detection.
fraction_ai = result['fraction_ai']
fraction_ai_assisted = result['fraction_ai_assisted']
fraction_human = result['fraction_human']
num_ai_segments = result['num_ai_segments']

# Access individual window classifications
for window in result['windows']:
    label = window['label']
    ai_assistance_score = window['ai_assistance_score']
    confidence = window['confidence']
    is_humanized = window['is_humanized']
    humanizer_score = window['humanizer_score']

from pangram import Pangram

pangram_client = Pangram()
result = pangram_client.predict(
    text,
    public_dashboard_link=True,
    model="pangram-4",
)
dashboard_link = result['dashboard_link']

from pangram import Pangram

pangram_client = Pangram()

bulk = pangram_client.submit_bulk(
    items=[
        {"id": "row-001", "text": "First text to analyze"},
        {"id": "row-002", "text": "Second text to analyze"},
    ],
    model="pangram-4",
)

bulk_id = bulk["bulk_id"]
status = pangram_client.wait_for_bulk(bulk_id, poll_interval=2)
results = pangram_client.get_bulk_results(bulk_id)

for item in results["items"]:
    if item["result"] is not None:
        print(item["id"], item["result"]["prediction_short"])

for failed in results["failed_items"]:
    print(failed["id"], failed["error"])

offset = 0
limit = 1000

while True:
    page = pangram_client.get_bulk_results_page(bulk_id, offset=offset, limit=limit)
    for item in page["items"]:
        process(item)
    for failed in page["failed_items"]:
        handle_failure(failed)

    offset += limit
    if offset >= page["total_items"]:
        break

from pangram import Pangram

pangram_client = Pangram()

text = "Text to check for plagiarism"
result = pangram_client.check_plagiarism(text)

if result['plagiarism_detected']:
    print(f"Plagiarism detected! {result['percent_plagiarized']}% of the text may be plagiarized.")
    for content in result['plagiarized_content']:
        print(f"Found match at {content['source_url']}")
        print(f"Matched text: {content['matched_text']}")