Metric-Gated Agent Rewards
with Neptune + Purple Flea

import neptune
import requests
import os

PF_HEADERS = {"Authorization": f"Bearer {os.environ['PF_API_KEY']}"}
ESC_BASE = "https://escrow.purpleflea.com/api/v1"

def train_with_escrow_reward(trainer_agent_id: str, budget: str = "5.00"):
    # Create escrow before training run
    esc = requests.post(f"{ESC_BASE}/escrow", headers=PF_HEADERS, json={
        "to_agent_id": trainer_agent_id,
        "amount": budget,
        "memo": "neptune_training_reward",
        "auto_release_hours": 4
    }).json()

    # Initialize Neptune run with escrow metadata
    run = neptune.init_run(
        project="my-org/my-project",
        api_token=os.environ["NEPTUNE_API_TOKEN"]
    )
    run["pf/escrow_id"] = esc["escrow_id"]
    run["pf/agent_id"] = trainer_agent_id
    run["pf/budget"] = budget

    # Training loop
    for epoch in range(50):
        loss, accuracy = train_epoch()
        run["train/loss"].append(loss)
        run["train/accuracy"].append(accuracy)

    # Evaluate final model
    final_accuracy = evaluate_model()
    run["eval/accuracy"] = final_accuracy

    # Release escrow based on quality
    if final_accuracy >= 0.90:
        release = requests.post(
            f"{ESC_BASE}/escrow/{esc['escrow_id']}/release",
            headers=PF_HEADERS
        ).json()
        run["pf/payment_status"] = "full_release"
        run["pf/amount_paid"] = budget
        print(f"✅ Full payment released: ${budget}")
    elif final_accuracy >= 0.75:
        partial_amount = str(float(budget) * 0.7)
        requests.post(
            f"{ESC_BASE}/escrow/{esc['escrow_id']}/release-partial",
            headers=PF_HEADERS,
            json={"amount": partial_amount}
        )
        run["pf/payment_status"] = "partial_release"
        run["pf/amount_paid"] = partial_amount
        print(f"💛 Partial payment ${partial_amount} released")
    else:
        requests.post(
            f"{ESC_BASE}/escrow/{esc['escrow_id']}/refund",
            headers=PF_HEADERS
        )
        run["pf/payment_status"] = "refunded"
        run["pf/amount_paid"] = "0"
        print("❌ Refunded — accuracy below threshold")

    run.stop()
    return final_accuracy

from neptune import management

def award_best_experiment_bonus(project_id: str, bonus_amount: str = "10.00"):
    """Query Neptune for best run, release bonus to its agent"""
    project = management.get_project(project_id)

    # Fetch all runs sorted by validation accuracy
    runs_table = project.fetch_runs_table(columns=[
        "eval/accuracy", "pf/agent_id", "pf/payment_status"
    ]).to_pandas()

    # Find best run that was already paid (verified quality)
    paid_runs = runs_table[runs_table["pf/payment_status"] == "full_release"]
    best_run = paid_runs.nlargest(1, "eval/accuracy").iloc[0]

    winner_agent_id = best_run["pf/agent_id"]
    best_accuracy = best_run["eval/accuracy"]

    print(f"🏆 Best run: agent={winner_agent_id}, accuracy={best_accuracy:.4f}")

    # Create bonus escrow and immediately release
    esc = requests.post(f"{ESC_BASE}/escrow", headers=PF_HEADERS, json={
        "to_agent_id": winner_agent_id,
        "amount": bonus_amount,
        "memo": f"best_experiment_bonus:acc={best_accuracy:.4f}",
        "auto_release_hours": 1
    }).json()

    requests.post(
        f"{ESC_BASE}/escrow/{esc['escrow_id']}/release",
        headers=PF_HEADERS
    )
    print(f"💰 Bonus ${bonus_amount} released to {winner_agent_id}")