"""
07_finetune_tracking_and_data_cards.py

Use Case: Model Fine-tuning/Training Tracking — Quality, Analytics, Data Card Reporting
--------------------------------------------------------------------------------------
Track model improvement, safety, and drift across epochs/checkpoints. Generates analytics & exportable data cards for audit.
 - Logs metric evolution, anomaly, and perf stats using BlazeMetrics analytics
 - At end, produces a markdown (or JSON) data card for full traceability
 - Plug into any real training/fine-tuning pipeline: just update pred/ref code
"""
import random
from blazemetrics import BlazeMetricsClient
import numpy as np

n_epochs = 3
samples_per_epoch = 5

client = BlazeMetricsClient(enable_analytics=True, analytics_window=5)

for epoch in range(n_epochs):
    print(f"\n=== EPOCH {epoch+1} ===")
    for _ in range(samples_per_epoch):
        # Replace with actual generation/gold in practice
        pred = f"Prediction val {random.randint(0,100)}"
        ref = f"Prediction val {random.randint(0,100)}"
        metrics = client.compute_metrics([pred], [[ref]])
        agg = client.aggregate_metrics(metrics)
        client.add_metrics(agg)
    summary = client.get_analytics_summary()
    print(f"Analytics after epoch {epoch+1}:", summary)

data_card = client.generate_data_card("Fine-tune-experiment", summary, summary)
print("\n--- Data Card ---\n", data_card)
