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1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 | import sys
import os
# Add parent directory to path so we can import 'envs' and 'inference'
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from fastapi import FastAPI, HTTPException, Body, Query
from fastapi.responses import HTMLResponse
from pydantic import BaseModel, Field
from typing import Optional, Dict, Any, List
from enum import Enum
from envs.social_stream_moderation.environment import SocialStreamModerationEnv
from envs.social_stream_moderation.models import State, ModerationAction
from envs.social_stream_moderation.graders import list_graders as _list_graders, get_grader, grade_episode
from envs.social_stream_moderation.tasks import TASKS, TASK_ALIASES, resolve_task
# Reverse mapping: canonical name -> legacy task ID (for openenv.yaml alignment)
CANONICAL_TO_LEGACY = {v: k for k, v in TASK_ALIASES.items()}
# Enums for Swagger Dropdowns
class TaskName(str, Enum):
TASK_1 = "Task 1: Basic Safety"
TASK_2 = "Task 2: Context & Nuance"
TASK_3 = "Task 3: Fairness & Bias"
class PolicyModeChoice(str, Enum):
NORMAL = "Standard Moderation"
STRICT = "Strict Enforcement"
LENIENT = "Lenient Privacy"
class UserHistoryChoice(str, Enum):
CLEAN = "Clean History"
REPEATED = "Repeat Offender"
class ContextTypeChoice(str, Enum):
ROOT = "Main Post"
COMMENT = "Comment"
# Mapping UI labels back to backend IDs
TASK_MAP = {
TaskName.TASK_1: "Task 1: Basic Safety",
TaskName.TASK_2: "Task 2: Context & Nuance",
TaskName.TASK_3: "Task 3: Fairness & Bias"
}
POLICY_MAP = {
PolicyModeChoice.NORMAL: "normal",
PolicyModeChoice.STRICT: "strict",
PolicyModeChoice.LENIENT: "lenient"
}
HISTORY_MAP = {
UserHistoryChoice.CLEAN: "no_prior_violations",
UserHistoryChoice.REPEATED: "prior_violations"
}
CONTEXT_MAP = {
ContextTypeChoice.ROOT: "root_post",
ContextTypeChoice.COMMENT: "comment"
}
# API Metadata for Swagger
TAGS_METADATA = [
{
"name": "🤖 Automated Benchmarking",
"description": "Autonomous evaluation loop. Sequence: **Reset** -> **Predict & Step** (Repeat). This tracks the official hackathon metrics.",
},
{
"name": "🧪 Interactive Lab",
"description": "Manual testing endpoints. Perfect for testing specific edge cases with custom inputs and human overrides.",
},
{
"name": "📊 System Monitoring",
"description": "Real-time state and status tracking for the moderation engine.",
}
]
app = FastAPI(
title="🛡️ PolicyPulse AI | Intelligence Center",
description="""
### Evaluation Guide for Hackathon Judges:
1. **Automated Testing:** Use `[POST] /reset` then `[POST] /predict_and_step`.
2. **Fairness Testing (Task 3):** Start an episode with `task_name='policy_fairness'`.
3. **Internal Logic:** Use `[POST] /evaluate` to see the model's reasoning without advancing the environment.
""",
version="1.2.0",
openapi_tags=TAGS_METADATA
)
env = SocialStreamModerationEnv()
class ResetRequest(BaseModel):
task_name: Optional[TaskName] = Field(None, description="Select the benchmark level to initialize (Swagger UI).")
task_id: Optional[str] = Field(None, description="Machine-readable task ID (e.g. 'clear_cut_moderation'). Used by the validator.")
seed: Optional[int] = Field(42, description="Reproducibility seed for dataset sampling.")
class EvaluateRequest(BaseModel):
text: str = Field("I will kill you", description="The user content string to analyze.")
api_base_url: Optional[str] = Field(None, description="Optional override. If blank, defaults to server's API_BASE_URL config.")
model_name: Optional[str] = Field(None, description="Optional override. If blank, defaults to server's MODEL_NAME config.")
api_key: Optional[str] = Field(None, description="Optional override. If blank, defaults to server's HF_TOKEN config.")
class LLMConfigRequest(BaseModel):
api_base_url: Optional[str] = Field(None, description="Optional override. If blank, defaults to server's API_BASE_URL config.")
model_name: Optional[str] = Field(None, description="Optional override. If blank, defaults to server's MODEL_NAME config.")
api_key: Optional[str] = Field(None, description="Optional override. If blank, defaults to server's HF_TOKEN config.")
class StepRequest(BaseModel):
action: ModerationAction = Field(ModerationAction.ALLOW, description="The action to apply to the current post.")
class FeedbackRequest(BaseModel):
text: str
corrected_action: ModerationAction
reason: str
@app.get("/", response_class=HTMLResponse)
def read_root():
return r"""
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>PolicyPulse AI | Intelligence Center</title>
<link href="https://fonts.googleapis.com/css2?family=Outfit:wght@300;400;600;800&family=JetBrains+Mono&display=swap" rel="stylesheet">
<style>
:root {
--bg: #030712;
--sidebar: rgba(15, 23, 42, 0.6);
--accent: #38bdf8;
--danger: #f472b6;
--success: #4ade80;
--text: #f8fafc;
--muted: #94a3b8;
}
* { margin:0; padding:0; box-sizing:border-box; }
body {
font-family:'Outfit', sans-serif; background: #030712; color:var(--text);
height:100vh; overflow:hidden; display:flex; flex-direction:column;
transition: 0.3s cubic-bezier(0.4, 0, 0.2, 1);
}
/* Custom Scrollbars */
::-webkit-scrollbar { width: 6px; height: 6px; }
::-webkit-scrollbar-track { background: transparent; }
::-webkit-scrollbar-thumb { background: rgba(56, 189, 248, 0.2); border-radius: 10px; }
::-webkit-scrollbar-thumb:hover { background: var(--accent); }
main {
flex:1;
display:grid;
grid-template-columns: 320px 1fr 0px;
gap:20px;
padding:20px;
max-height:calc(100vh - 60px);
transition: 0.4s cubic-bezier(0.4, 0, 0.2, 1);
}
body.audit-active main {
grid-template-columns: 320px 1fr 420px;
}
header { height:60px; display:flex; align-items:center; justify-content:space-between; padding:0 30px; border-bottom:1px solid rgba(255,255,255,0.05); background:rgba(15, 23, 42, 0.4); }
.logo { font-weight:800; font-size:1.4rem; letter-spacing:-0.03em; color:var(--accent); }
.version { font-size:0.7rem; background:rgba(56, 189, 248, 0.1); padding:4px 10px; border-radius:6px; color:var(--accent); font-weight:600; }
/* Panel Styling */
.panel { background:var(--sidebar); backdrop-filter:blur(20px); border-radius:24px; border:1px solid rgba(255,255,255,0.06); display:flex; flex-direction:column; overflow:hidden; }
.panel-header { padding:25px; border-bottom:1px solid rgba(255,255,255,0.05); }
.panel-title { font-size:0.9rem; font-weight:800; text-transform:uppercase; letter-spacing:0.05em; display:flex; align-items:center; gap:10px; }
.panel-title::before { content:''; width:3px; height:14px; background:var(--accent); border-radius:10px; }
.panel-content { padding:25px; flex:1; overflow-y:auto; }
/* Tabs */
.mode-switch { display:flex; background:rgba(0,0,0,0.3); padding:4px; border-radius:12px; margin-bottom:25px; }
.tab { flex:1; padding:10px; text-align:center; cursor:pointer; font-size:0.8rem; font-weight:700; border-radius:8px; transition:0.3s; color:var(--muted); }
.tab.active { background:var(--accent); color:#020617; }
/* Forms */
.field { margin-bottom:20px; }
label { display:block; font-size:0.65rem; font-weight:700; color:var(--muted); text-transform:uppercase; margin-bottom:8px; }
select, textarea { width:100%; background:rgba(0,0,0,0.4); border:1px solid rgba(255,255,255,0.1); border-radius:12px; padding:12px; color:#fff; font-family:'Outfit'; font-size:0.9rem; transition:0.3s; }
textarea { resize:none; min-height:100px; }
select:focus, textarea:focus { outline:none; border-color:var(--accent); }
/* Buttons */
.btn { width:100%; padding:16px; border-radius:14px; border:none; font-weight:700; cursor:pointer; transition:0.3s; font-size:0.95rem; display:flex; align-items:center; justify-content:center; gap:10px; }
.btn-primary { background:var(--accent); color:#020617; }
.btn-primary:hover { background:#7dd3fc; transform:translateY(-2px); }
.btn-secondary { background:rgba(255,255,255,0.05); color:#fff; border:1px solid rgba(255,255,255,0.1); margin-top:10px; }
.btn-secondary:hover { background:rgba(255,255,255,0.08); }
.btn:disabled { opacity:0.3; cursor:not-allowed; transform:none !important; }
/* Right Column */
.stats-bar { display:grid; grid-template-columns: repeat(3, 1fr); gap:15px; margin-bottom:20px; }
.stat-card { background:rgba(255,255,255,0.03); padding:15px; border-radius:16px; border:1px solid rgba(255,255,255,0.05); }
.stat-label { font-size:0.6rem; color:var(--muted); font-weight:700; text-transform:uppercase; }
.stat-value { font-size:1.1rem; font-weight:800; font-family:'JetBrains Mono'; margin-top:5px; color:var(--accent); }
.log-container { background:rgba(0,0,0,0.2); border-radius:20px; border:1px solid rgba(255,255,255,0.05); flex:1; overflow-y:auto; padding:20px; display:flex; flex-direction:column; gap:12px; }
.log-entry {
background:rgba(255,255,255,0.02); padding:18px; border-radius:14px;
border-left:3px solid var(--accent); animation:fadeIn 0.3s;
transition:0.3s; cursor:default;
}
.log-entry.active-audit { background:rgba(56,189,248,0.08); border-color:var(--accent); box-shadow:0 10px 30px rgba(0,0,0,0.3); }
@keyframes fadeIn { from { opacity:0; transform:translateY(5px); } to { opacity:1; transform:translateY(0); } }
.log-meta { display:flex; justify-content:space-between; font-size:0.7rem; color:var(--muted); margin-bottom:8px; font-weight:600; }
.log-text { font-size:0.95rem; line-height:1.4; color:#e2e8f0; }
.log-badge { font-size:0.6rem; font-weight:800; padding:2px 8px; border-radius:4px; text-transform:uppercase; margin-top:10px; display:inline-block; }
.audit-btn { background:rgba(255,255,255,0.05); border:1px solid rgba(255,255,255,0.1); color:var(--muted); font-size:0.6rem; padding:4px 12px; border-radius:6px; cursor:pointer; font-weight:800; transition:0.2s; }
.audit-btn:hover { background:var(--danger); color:#000; border-color:var(--danger); }
.verify-btn { background:rgba(74,222,128,0.05); border:1px solid var(--success); color:var(--success); font-size:0.6rem; padding:4px 12px; border-radius:6px; cursor:pointer; font-weight:800; transition:0.2s; }
.verify-btn:hover { background:var(--success); color:#000; }
.grid-btn { background:rgba(255,255,255,0.05); border:1px solid rgba(255,255,255,0.1); color:white; font-size:0.7rem; padding:12px; border-radius:8px; cursor:pointer; font-weight:700; transition:0.2s; }
.grid-btn:hover { background:var(--accent); color:#020617; border-color:var(--accent); }
/* Skeleton Shimmer */
.skeleton {
height: 200px;
background: linear-gradient(90deg, rgba(255,255,255,0.03) 25%, rgba(255,255,255,0.08) 50%, rgba(255,255,255,0.03) 75%);
background-size: 200% 100%;
animation: shimmer 1.5s infinite;
border-radius: 14px;
margin-bottom: 12px;
border: 1px solid rgba(255,255,255,0.05);
}
@keyframes shimmer {
0% { background-position: 200% 0; }
100% { background-position: -200% 0; }
}
.empty-state { margin:auto; text-align:center; color:var(--muted); font-weight:300; }
/* Header Nav */
.nav-links { display:flex; gap:25px; align-items:center; }
.nav-links a {
font-size:0.75rem;
color:var(--muted);
text-decoration:none;
font-weight:700;
letter-spacing:0.05em;
transition:0.3s;
position:relative;
padding-bottom: 4px;
}
.nav-links a:hover { color:var(--accent); }
.nav-links a::after {
content: '';
position: absolute;
bottom: 0; left: 0;
width: 0; height: 1px;
background: var(--accent);
transition: 0.3s;
}
.nav-links a:hover::after { width: 100%; }
</style>
</head>
<body>
<header>
<div class="logo">POLICYPULSE <span style="font-weight:300">AI</span></div>
<div style="display:flex; align-items:center; gap:20px;">
<div class="nav-links">
<a href="/docs">API REFERENCE</a>
<a href="/state">SYSTEM STATUS</a>
</div>
<div class="version">REVISION 1.0</div>
</div>
</header>
<main>
<!-- Left Panel: Orchestration -->
<div class="panel">
<div class="panel-header">
<div class="panel-title">Operation Center</div>
</div>
<div class="panel-content">
<div class="mode-switch">
<div class="tab active" id="tab-lab">LIVE MODE</div>
<div class="tab" id="tab-auto">GRADER MODE</div>
</div>
<!-- Lab Mode Form -->
<div id="section-lab">
<div class="field">
<label>User Content</label>
<textarea id="lab-input" placeholder="Type or paste text to test our agent's moderation logic..."></textarea>
</div>
<div class="field">
<label>Safety Policy</label>
<select id="lab-policy">
<option value="NORMAL">Standard Moderation</option>
<option value="STRICT">Strict Enforcement</option>
<option value="LENIENT">Lenient Privacy</option>
</select>
</div>
<div class="field" style="display:grid; grid-template-columns:1fr 1fr; gap:10px;">
<div>
<label>User History</label>
<select id="lab-history" style="font-size:0.75rem;">
<option value="no_prior_violations">Clean History</option>
<option value="prior_violations">Repeat Offender</option>
</select>
</div>
<div>
<label>Context Type</label>
<select id="lab-context" style="font-size:0.75rem;">
<option value="root_post">Main Post</option>
<option value="comment">Comment</option>
</select>
</div>
</div>
</div>
<!-- Auto Mode Form -->
<div id="section-auto" style="display:none;">
<div class="field">
<label>Benchmark Level</label>
<select id="auto-task">
<option value="Task 1: Basic Safety">Task 1: Basic Safety</option>
<option value="Task 2: Context & Nuance">Task 2: Context & Nuance</option>
<option value="Task 3: Fairness & Bias">Task 3: Fairness & Bias</option>
</select>
</div>
<button class="btn btn-primary" id="btn-auto-reset">START BENCHMARK</button>
<button class="btn btn-secondary" id="btn-auto-step" disabled>PROCESS NEXT ITEM</button>
</div>
<div style="margin-top:20px; padding-top:20px; border-top:1px solid rgba(255,255,255,0.05);">
<div style="font-size:0.65rem; font-weight:700; color:var(--accent); text-transform:uppercase; margin-bottom:10px;">Optional: Custom LLM Override</div>
<div class="field" style="margin-bottom:15px;">
<input type="text" id="config-base-url" placeholder="API Base URL (e.g., https://api.openai.com/v1)" style="width:100%; background:rgba(0,0,0,0.4); border:1px solid rgba(255,255,255,0.1); border-radius:8px; padding:10px; color:#fff; font-family:'Outfit'; font-size:0.8rem; margin-bottom:8px;">
<input type="text" id="config-model" placeholder="Model Name (e.g., gpt-4o-mini)" style="width:100%; background:rgba(0,0,0,0.4); border:1px solid rgba(255,255,255,0.1); border-radius:8px; padding:10px; color:#fff; font-family:'Outfit'; font-size:0.8rem; margin-bottom:8px;">
<input type="password" id="config-key" placeholder="API Key" style="width:100%; background:rgba(0,0,0,0.4); border:1px solid rgba(255,255,255,0.1); border-radius:8px; padding:10px; color:#fff; font-family:'Outfit'; font-size:0.8rem;">
</div>
</div>
<button class="btn btn-primary" id="btn-lab-run" style="margin-top:20px" disabled>RUN MODERATION</button>
<button class="btn btn-secondary" id="btn-global-clear" style="margin-top:10px">PURGE LOGS</button>
</div>
</div>
<!-- Right Panel: Intelligence Stream -->
<div class="panel" style="background:transparent; border:none; backdrop-filter:none;">
<div class="stats-bar">
<div class="stat-card">
<div class="stat-label">Model Accuracy</div>
<div class="stat-value" id="val-accuracy">--</div>
</div>
<div class="stat-card">
<div class="stat-label">Aggregate Reward</div>
<div class="stat-value" id="val-reward">0.000</div>
</div>
<div class="stat-card">
<div class="stat-label">System State</div>
<div class="stat-value" id="val-state" style="color:var(--muted)">IDLE</div>
</div>
</div>
<div class="log-container" id="log-viewport">
<div class="empty-state" id="empty-hint">
<div style="font-size:3rem; margin-bottom:20px; opacity:0.2;">📉</div>
<div style="font-weight:600; font-size:0.9rem;">Intelligence Stream Idle</div>
<p style="font-size:0.75rem; opacity:0.5; margin-top:10px;">Configure your parameters and click 'RUN MODERATION' to begin ingestion.</p>
</div>
</div>
</div>
<!-- Audit Inspector Sidepanel (Now correctly part of the grid) -->
<div id="inspector-pane" class="panel" style="border-left:1px solid rgba(255,255,255,0.1); background:rgba(15,23,42,0.6); overflow:hidden; visibility:hidden; opacity:0; transition:0.4s;">
<div class="panel-header" style="display:flex; justify-content:space-between; align-items:center;">
<div class="panel-title">Audit Inspector</div>
<button onclick="closeInspector()" style="background:none; border:none; color:var(--muted); cursor:pointer; font-size:1.2rem;">×</button>
</div>
<div class="panel-content" id="inspector-content" style="padding:20px;">
<!-- Content injected by JS -->
</div>
</div>
</main>
<script>
// Elements
const tabs = { lab: document.getElementById('tab-lab'), auto: document.getElementById('tab-auto') };
const sections = { lab: document.getElementById('section-lab'), auto: document.getElementById('section-auto') };
const btnLabRun = document.getElementById('btn-lab-run');
const btnAutoReset = document.getElementById('btn-auto-reset');
const btnAutoStep = document.getElementById('btn-auto-step');
const btnGlobalClear = document.getElementById('btn-global-clear');
const logViewport = document.getElementById('log-viewport');
// HUD
const valReward = document.getElementById('val-reward');
const valAccuracy = document.getElementById('val-accuracy');
const valState = document.getElementById('val-state');
let totalReward = 0;
let counter = 0;
let currentMode = 'lab';
// Tab Switching
tabs.lab.onclick = () => setMode('lab');
tabs.auto.onclick = () => setMode('auto');
// Mode Switch Logic
function setMode(m) {
currentMode = m;
sections.lab.style.display = m === 'lab' ? 'block' : 'none';
sections.auto.style.display = m === 'auto' ? 'block' : 'none';
tabs.lab.classList.toggle('active', m === 'lab');
tabs.auto.classList.toggle('active', m === 'auto');
// Clear state UI
valState.textContent = 'READY';
valState.style.color = 'var(--accent)';
}
// Lab Input Validation
document.getElementById('lab-input').oninput = (e) => {
btnLabRun.disabled = !e.target.value.trim();
};
// Task Change Re-enables Start
document.getElementById('auto-task').onchange = () => {
btnAutoReset.disabled = false;
btnAutoStep.disabled = true;
};
// Global Reset
btnGlobalClear.onclick = () => {
logViewport.innerHTML = '<div class="empty-state">System purged. Waiting for new data.</div>';
totalReward = 0;
counter = 0;
valReward.textContent = '0.000';
valAccuracy.textContent = '--';
valState.textContent = 'IDLE';
valState.style.color = 'var(--muted)';
btnAutoStep.disabled = true;
btnAutoReset.disabled = false; // Re-enable benchmark
if (currentMode === 'auto') valState.textContent = 'SYSTEM RESET';
};
// Lab Evaluation
btnLabRun.onclick = async () => {
const text = document.getElementById('lab-input').value.trim();
const policy = document.getElementById('lab-policy').value;
const history = document.getElementById('lab-history').value;
const context = document.getElementById('lab-context').value;
if (!text) return;
btnLabRun.disabled = true;
// Show Skeleton Loading State
const skeleton = document.createElement('div');
skeleton.id = 'lab-shimmer';
skeleton.innerHTML = `<div class="skeleton"></div>`;
logViewport.prepend(skeleton);
try {
const resp = await fetch('/evaluate', {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({
text: text,
policy_mode: policy.toLowerCase(),
user_history: history,
context_type: context,
api_base_url: document.getElementById('config-base-url').value.trim() || undefined,
model_name: document.getElementById('config-model').value.trim() || undefined,
api_key: document.getElementById('config-key').value.trim() || undefined
})
});
if (skeleton) skeleton.remove();
if (!resp.ok) {
const errData = await resp.json();
throw new Error(errData.detail || "Neural Evaluation Failed");
}
const data = await resp.json();
renderEntry(text, data.action, data.reward, policy, data.reason, {history, context});
updateHUD(data.reward);
document.getElementById('lab-input').value = '';
btnLabRun.disabled = true; // Auto-disable after clear
} catch (e) {
if (skeleton) skeleton.remove();
const errorEntry = document.createElement('div');
errorEntry.className = 'log-entry';
errorEntry.style.borderLeftColor = 'var(--danger)';
errorEntry.style.background = 'rgba(244,114,182,0.05)';
errorEntry.innerHTML = `
<div class="log-meta"><span style="color:var(--danger)">⚠️ SYSTEM EXCEPTION</span></div>
<div class="log-text">The neural bridge was interrupted. Check your API key or model availability.</div>
<div style="font-size:0.6rem; color:var(--danger); margin-top:10px; opacity:0.7; font-family:'JetBrains Mono'">${e.toString()}</div>
`;
logViewport.prepend(errorEntry);
} finally {
btnLabRun.disabled = !document.getElementById('lab-input').value.trim();
}
};
// Auto Benchmark
btnAutoReset.onclick = async () => {
btnAutoReset.disabled = true; // Lock Benchmark
btnAutoStep.disabled = false;
const task = document.getElementById('auto-task').value;
valState.textContent = 'RESETTING...';
const resp = await fetch('/reset', {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({task_name: task})
});
const state = await resp.json();
logViewport.innerHTML = `<div class="log-entry" style="border-color:var(--muted)">
<div class="log-meta"><span>SYSTEM EVENT</span><span>SESSION START</span></div>
<div class="log-text">Environment reset complete. Target: <b>${task}</b>. Dataset contains ${state.total_steps} items. Ready for sequential evaluation.</div>
</div>`;
valState.textContent = `SEQ: 1/${state.total_steps}`;
btnAutoStep.disabled = false;
};
btnAutoStep.onclick = async () => {
if (btnAutoStep.disabled) return;
btnAutoStep.disabled = true;
// Show Skeleton Loading State
const logViewport = document.getElementById('log-viewport');
const skeleton = document.createElement('div');
skeleton.id = 'shimmer-loading';
skeleton.innerHTML = `<div class="skeleton"></div>`;
logViewport.prepend(skeleton);
try {
const stateResp = await fetch('/state');
const state = await stateResp.json();
const evalResp = await fetch('/evaluate', {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({
text: state.text,
policy_mode: state.platform_policy_mode,
api_base_url: document.getElementById('config-base-url').value.trim() || undefined,
model_name: document.getElementById('config-model').value.trim() || undefined,
api_key: document.getElementById('config-key').value.trim() || undefined
})
});
const evalData = await evalResp.json();
const stepResp = await fetch('/step', {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({action: evalData.action})
});
const stepResult = await stepResp.json();
// Remove Skeleton
if (skeleton) skeleton.remove();
renderEntry(state.text, evalData.action, stepResult.reward, state.platform_policy_mode.toUpperCase(), evalData.reason, {history: state.user_history_summary, context: state.context_type});
updateHUD(stepResult.reward);
if (stepResult.done) {
valState.textContent = 'COMPLETE';
valState.style.color = 'var(--success)';
btnAutoStep.disabled = true;
logViewport.innerHTML = `<div class="log-entry" style="border-color:var(--success); background:rgba(74,222,128,0.05)">
<div class="log-meta"><span>EPISODE COMPLETE</span><span>FINAL GRADE</span></div>
<div class="log-text">The environment has finalized this sequence. Total episodes rewards calculated with active fairness parity checks.</div>
<div style="font-size:1.4rem; font-weight:800; color:var(--success); margin-top:15px; font-family:'JetBrains Mono'">SCORE: ${stepResult.final_score.toFixed(4)}</div>
</div>` + logViewport.innerHTML;
} else {
valState.textContent = `SEQ: ${state.step_index + 1}/${state.total_steps}`;
btnAutoStep.disabled = false;
}
} catch (e) {
if (skeleton) skeleton.remove();
btnAutoStep.disabled = false;
const errorEntry = document.createElement('div');
errorEntry.className = 'log-entry';
errorEntry.style.borderLeftColor = 'var(--danger)';
errorEntry.style.background = 'rgba(244,114,182,0.05)';
errorEntry.innerHTML = `
<div class="log-meta"><span style="color:var(--danger)">⚠️ SYSTEM EXCEPTION</span></div>
<div class="log-text">An intelligence bypass occurred or the connection was interrupted. Please check your Operation Center configuration or API availability.</div>
<div style="font-size:0.6rem; color:var(--danger); margin-top:10px; opacity:0.7; font-family:'JetBrains Mono'">${e.toString()}</div>
`;
logViewport.prepend(errorEntry);
}
};
function updateHUD(r) {
totalReward += r;
counter++;
valReward.textContent = totalReward.toFixed(3);
valAccuracy.textContent = (totalReward / counter).toFixed(3);
}
function renderEntry(text, action, reward, mode, reason, meta) {
const colors = { ALLOW:'var(--accent)', BAN_USER:'var(--danger)', HARD_FILTER:'var(--danger)', SOFT_HIDE:'#fbbf24', ALLOW_WITH_WARNING:'var(--accent)', ESCALATE_HUMAN:'var(--success)' };
const entry = document.createElement('div');
entry.className = 'log-entry';
entry.style.borderColor = colors[action] || 'var(--accent)';
entry.innerHTML = `
<div class="log-meta">
<span>POLICY: ${mode}</span>
<span>VERDICT: +${reward.toFixed(3)}</span>
</div>
<div style="display:flex; gap:8px; margin-bottom:10px;">
<span style="font-size:0.6rem; color:var(--muted); border:1px solid rgba(255,255,255,0.1); padding:2px 6px; border-radius:4px; text-transform:uppercase;">${meta.history.replace(/_/g,' ')}</span>
<span style="font-size:0.6rem; color:var(--muted); border:1px solid rgba(255,255,255,0.1); padding:2px 6px; border-radius:4px; text-transform:uppercase;">${meta.context.replace(/_/g,' ')}</span>
</div>
<div class="log-text">${text}</div>
<div style="font-size:0.75rem; color:var(--accent); background:rgba(56,189,248,0.04); padding:12px; border-radius:12px; margin-top:12px; border:1px solid rgba(56,189,248,0.1); white-space: pre-wrap; line-height: 1.6;">
${reason}
</div>
<div style="display:flex; align-items:center; justify-content:space-between; margin-top:12px;">
<span class="log-badge" style="background:${colors[action] || 'var(--accent)'}; color:#020617; margin-top:0">${action}</span>
<div class="hitl-actions" id="hitl-${counter}" style="display:flex; gap:5px;">
<button onclick="showOverrideMenu(this, ${reward}, '${action}', \`${text.replace(/`/g, '\\`')}\`)" class="audit-btn">AUDIT</button>
<button onclick="verifyAction(this)" class="verify-btn">VERIFY</button>
</div>
</div>
`;
const hint = document.getElementById('empty-hint');
if (hint) hint.remove();
logViewport.prepend(entry);
}
function verifyAction(btn) {
btn.parentElement.innerHTML = '<span style="color:var(--success); font-size:0.6rem; font-weight:800; border:1px solid var(--success); padding:2px 6px; border-radius:4px;">✓ HUMAN VERIFIED</span>';
}
function closeInspector() {
document.body.classList.remove('audit-active');
const pane = document.getElementById('inspector-pane');
pane.style.visibility = 'hidden';
pane.style.opacity = '0';
if (window.__active_row) window.__active_row.classList.remove('active-audit');
}
function showOverrideMenu(btn, originalReward, originalAction, originalText) {
const pane = document.getElementById('inspector-pane');
const content = document.getElementById('inspector-content');
const row = btn.closest('.log-entry');
if (window.__active_row) window.__active_row.classList.remove('active-audit');
row.classList.add('active-audit');
window.__active_row = row;
window.__pending_text = originalText;
window.__pending_reward = originalReward;
window.__pending_hitl_id = btn.parentElement.id;
window.__selected_action = null;
content.innerHTML = `
<div style="display:flex; flex-direction:column; gap:20px;">
<div style="background:rgba(255,255,255,0.03); padding:20px; border-radius:16px; border:1px solid rgba(255,255,255,0.05);">
<div style="font-size:0.6rem; color:var(--muted); text-transform:uppercase; font-weight:800; margin-bottom:10px;">Original Content</div>
<div style="font-size:0.9rem; line-height:1.5;">"${originalText}"</div>
</div>
<div style="display:flex; flex-direction:column; gap:12px;">
<label style="font-size:0.65rem; color:var(--danger); font-weight:800; text-transform:uppercase;">Correction Verdict</label>
<div style="display:grid; grid-template-columns: 1fr 1fr; gap:10px;" id="action-selector">
<button onclick="selectAction(this, 'ALLOW')" class="grid-btn">ALLOW</button>
<button onclick="selectAction(this, 'ALLOW_WITH_WARNING')" class="grid-btn">WARNING</button>
<button onclick="selectAction(this, 'SOFT_HIDE')" class="grid-btn">HIDE</button>
<button onclick="selectAction(this, 'ESCALATE_HUMAN')" class="grid-btn">ESCALATE</button>
<button onclick="selectAction(this, 'BAN_USER')" class="grid-btn" style="grid-column: span 2;">BAN USER</button>
</div>
</div>
<div style="display:flex; flex-direction:column; gap:10px;">
<label style="font-size:0.65rem; color:var(--muted); font-weight:800; text-transform:uppercase;">Memory Reason (Optional)</label>
<textarea id="feedback-reason" placeholder="Why is this correction necessary?" style="min-height:100px; font-size:0.85rem; background:rgba(0,0,0,0.4); padding:15px; border:1px solid rgba(255,255,255,0.1); border-radius:12px; color:white; width:100%; resize:none;"></textarea>
</div>
<button id="btn-submit-feedback" onclick="submitFeedback()" class="btn btn-primary" style="margin-top:10px; opacity:0.5;" disabled>REINFORCE SYSTEM</button>
<div id="feedback-status" style="font-size:0.7rem; color:var(--muted); text-align:center;">Select an action to enable submission.</div>
</div>
`;
pane.style.visibility = 'visible';
pane.style.opacity = '1';
document.body.classList.add('audit-active');
}
function selectAction(btn, action) {
// Clear state
const btns = document.querySelectorAll('#action-selector .grid-btn');
btns.forEach(b => {
b.style.background = 'rgba(255,255,255,0.05)';
b.style.color = 'white';
});
// Set active
btn.style.background = 'var(--accent)';
btn.style.color = '#020617';
window.__selected_action = action;
// Enable submit
const submit = document.getElementById('btn-submit-feedback');
submit.disabled = false;
submit.style.opacity = '1';
document.getElementById('feedback-status').innerHTML = "Ready to reinforce local memory.";
}
async function submitFeedback() {
const action = window.__selected_action;
const reason = document.getElementById('feedback-reason').value.trim() || "Manual correction by human auditor.";
const text = window.__pending_text;
const originalReward = window.__pending_reward;
const hitlId = window.__pending_hitl_id;
const statusDiv = document.getElementById('feedback-status');
const submitBtn = document.getElementById('btn-submit-feedback');
submitBtn.disabled = true;
statusDiv.innerHTML = "⏳ REINFORCING LOGIC...";
try {
await fetch('/feedback', {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({
text: text,
corrected_action: action,
reason: reason
})
});
const correction = - (originalReward + 1.0);
updateHUD(correction);
const container = document.getElementById(hitlId);
container.innerHTML = `<span style="color:var(--danger); font-size:0.6rem; font-weight:800; border:1px solid var(--danger); padding:2px 6px; border-radius:4px;">🧠 MEMORY REINFORCED</span>`;
statusDiv.innerHTML = "✅ SYSTEM REINFORCED!";
setTimeout(closeInspector, 1000);
} catch (e) {
statusDiv.innerHTML = "❌ MEMORY WRITE FAILED";
submitBtn.disabled = false;
}
}
</script>
</body>
</html>
"""
@app.post("/reset", tags=["🤖 Automated Benchmarking"], summary="1. Initialize Environment (Task Selection)")
async def reset_env(req: ResetRequest = Body(default=ResetRequest())):
"""Resets the environment with a given task and seed. This must be the first step in any benchmarking track.
Accepts either ``task_id`` (legacy machine ID like ``clear_cut_moderation``)
or ``task_name`` (Swagger UI enum). ``task_id`` takes precedence when both
are supplied.
"""
try:
if req.task_id:
# Validator sends task_id (legacy ID like "clear_cut_moderation")
task_cfg = resolve_task(req.task_id)
internal_task_name = task_cfg.name
elif req.task_name:
# Swagger UI sends the enum
internal_task_name = TASK_MAP[req.task_name]
else:
# Default to Task 1
internal_task_name = "Task 1: Basic Safety"
state = await env.reset(task_name=internal_task_name, seed=req.seed)
return state
except (ValueError, KeyError) as e:
raise HTTPException(status_code=400, detail=str(e))
@app.get("/health", tags=["📊 System Monitoring"])
def health_check():
"""Health check endpoint required by OpenEnv runtime validation."""
return {"status": "healthy"}
@app.get("/metadata", tags=["📊 System Monitoring"])
def metadata():
"""Returns environment metadata required by OpenEnv runtime validation."""
return {
"name": "SocialStreamModerationEnv",
"description": (
"A content-moderation RL environment where an agent must classify "
"social-media posts as safe or harmful under varying policy regimes, "
"with tasks spanning basic safety, contextual nuance, and fairness."
),
"version": "1.2.0",
"tasks": list(CANONICAL_TO_LEGACY.values()),
}
@app.get("/schema", tags=["📊 System Monitoring"])
def schema():
"""Returns action, observation, and state schemas for OpenEnv validation."""
return {
"action": {
"type": "string",
"enum": [a.value for a in ModerationAction],
},
"observation": {
"type": "object",
"properties": {
"post_id": {"type": "string"},
"text": {"type": "string"},
"user_history_summary": {"type": "string"},
"context_type": {"type": "string"},
"platform_policy_mode": {"type": "string"},
"user_group": {"type": "string"},
"step_index": {"type": "integer"},
"total_steps": {"type": "integer"},
},
},
"state": {
"type": "object",
"properties": {
"post_id": {"type": "string"},
"text": {"type": "string"},
"context_type": {"type": "string"},
"platform_policy_mode": {"type": "string"},
"user_group": {"type": "string"},
"step_index": {"type": "integer"},
"total_steps": {"type": "integer"},
},
},
}
@app.get("/tasks", tags=["🤖 Automated Benchmarking"])
async def list_tasks():
"""Returns the list of tasks available in the environment for discovery.
``task_id`` / ``id`` use the legacy machine-readable IDs that match
``openenv.yaml`` (e.g. ``clear_cut_moderation``) so the external validator
can cross-reference them.
"""
return [
{
"task_id": CANONICAL_TO_LEGACY.get(canonical, canonical),
"id": CANONICAL_TO_LEGACY.get(canonical, canonical),
"name": task_cfg.name,
"difficulty": task_cfg.difficulty,
"description": f"Episode length: {task_cfg.episode_length} posts. Policy mode: {task_cfg.policy_mode.value}.",
"grader_id": task_cfg.grader_id,
}
for canonical, task_cfg in TASKS.items()
]
@app.get("/graders", tags=["🛡️ Automated Benchmarking"])
async def list_graders_endpoint():
"""Returns the list of graders available in the environment for discovery."""
return _list_graders()
# Per-task score cache so /grader?task_id=... can return past scores
_task_scores: Dict[str, float] = {}
@app.get("/grader", tags=["🤖 Automated Benchmarking"])
def grader_score(task_id: Optional[str] = Query(None, description="Legacy task ID to retrieve a specific task's score.")):
"""Returns the grader score for the current (or most recent) episode.
The Scaler / OpenEnv hackathon validator calls this endpoint after running
an episode to obtain the final score. Accepts an optional ``task_id``
query parameter to retrieve the score for a specific task.
If no episode has been run yet a minimal default score is returned.
"""
# If a specific task_id is requested, look up its cached score
if task_id:
if task_id in _task_scores:
return {"score": _task_scores[task_id]}
# Also check canonical name
canonical = TASK_ALIASES.get(task_id)
if canonical and canonical in _task_scores:
return {"score": _task_scores[canonical]}
return {"score": 0.001}
# Use the environment's last episode info to compute the score
if env.episode_history:
task = env.current_task
if task is not None:
grader_inst = get_grader(task.grader_id)
score = grader_inst.grade(env.episode_history)
# Cache the score under both canonical name and legacy ID
_task_scores[task.name] = score
legacy_id = CANONICAL_TO_LEGACY.get(task.name)
if legacy_id:
_task_scores[legacy_id] = score
else:
score = grade_episode(env.episode_history, use_fairness=False)
else:
score = 0.001
return {"score": score}
@app.post("/evaluate", tags=["🧪 Interactive Lab"], summary="Test Model Logic (XAI Insight)")
async def evaluate_text(
req: EvaluateRequest,
policy_mode: PolicyModeChoice = Query(PolicyModeChoice.NORMAL, description="Select the active safety policy regime."),
user_history: UserHistoryChoice = Query(UserHistoryChoice.CLEAN, description="Select the author's moderation history."),
context_type: ContextTypeChoice = Query(ContextTypeChoice.ROOT, description="Select the content hierarchical context.")
):
"""Internal endpoint for the Interactive Sandbox UI. Returns logic reasoning and calculated rewards."""
# Use proper Enum types
from envs.social_stream_moderation.models import PolicyMode, State
from envs.social_stream_moderation.graders import compute_per_post_reward, REWARD_TABLE
from inference import get_agent
# Map choice to model enum
try:
p_mode = PolicyMode(POLICY_MAP[policy_mode])
except ValueError:
p_mode = PolicyMode.NORMAL
# Predict action using rule-based or LLM logic
agent = get_agent(api_base_url=req.api_base_url, model_name=req.model_name, api_key=req.api_key)
mock_state = State(
post_id="playground_test",
text=req.text,
user_history_summary=HISTORY_MAP[user_history],
context_type=CONTEXT_MAP[context_type],
platform_policy_mode=p_mode.value,
user_group="A",
step_index=0,
total_steps=1
)
action, reason = agent.predict(mock_state)
# We need to simulate a 'correct' label for reward calculation in demo
# For simplicity in demo, if the agent bans, we assume it caught something
# (HarmLabel mapping is internal, so we'll just derive a representative reward)
# Check if this input matches our known harmful patterns to determine reward
from envs.social_stream_moderation.models import HarmLabel
from inference import SAFETY_KEYWORDS
best_harm_guess = HarmLabel.SAFE
for category, keywords in SAFETY_KEYWORDS.items():
if any(kw in req.text.lower() for kw in keywords):
best_harm_guess = category
break
reward = compute_per_post_reward(best_harm_guess, action, p_mode)
return {
"action": action.value,
"reward": float(reward),
"reason": reason
}
@app.post("/step", tags=["🧪 Interactive Lab"])
async def step_env(req: StepRequest):
try:
next_state, reward, done, info = await env.step(req.action)
final_score = 0.0
grader_id = None
if done:
# The environment now uses the task-specific grader internally;
# the final score and grader_id are returned in ``info``.
final_score = info.get("score", 0.0)
grader_id = info.get("grader_id")
return {
"next_state": next_state,
"reward": reward,
"done": done,
"info": info,
"final_score": final_score,
"grader_id": grader_id,
}
except RuntimeError as e:
raise HTTPException(status_code=400, detail=str(e))
@app.post("/predict_and_step", tags=["🤖 Automated Benchmarking"], summary="2. Autonomous Model Execution (Autonomous)")
async def predict_and_step(req: Optional[LLMConfigRequest] = Body(None)):
"""Predicts using dynamic agent and steps the env automatically. This matches our inference.py autonomous loop."""
from inference import get_agent
state = env._get_state()
if state is None:
raise HTTPException(status_code=400, detail="No active episode. Please call /reset first.")
agent = get_agent(
api_base_url=req.api_base_url if req else None,
model_name=req.model_name if req else None,
api_key=req.api_key if req else None
)
action, reason = agent.predict(state)
# Execute the step with the model's prediction
next_state, reward, done, info = await env.step(action)
final_score = 0.0
grader_id = None
if done:
# The environment now uses the task-specific grader internally
final_score = info.get("score", 0.0)
grader_id = info.get("grader_id")
return {
"prediction": action.value,
"reason": reason,
"reward": reward,
"done": done,
"final_score": final_score,
"grader_id": grader_id,
"next_state": next_state,
"info": info
}
@app.post("/feedback")
async def save_feedback(req: FeedbackRequest):
"""Saves human correction to local JSON memory for reinforcement learning."""
import json
memory_path = os.path.join(os.path.dirname(__file__), "..", "envs", "social_stream_moderation", "human_memory.json")
# Load existing memory
memory = []
if os.path.exists(memory_path):
with open(memory_path, "r") as f:
try:
memory = json.load(f)
except:
memory = []
# Check for duplicates or update
found = False
for entry in memory:
if entry["text"] == req.text:
entry["action"] = req.corrected_action
entry["reason"] = req.reason
found = True
break
if not found:
memory.append({
"text": req.text,
"action": req.corrected_action,
"reason": req.reason
})
with open(memory_path, "w") as f:
json.dump(memory, f, indent=2)
return {"status": "success", "message": "Memory reinforced."}
@app.get("/state", tags=["📊 System Monitoring"])
def get_state():
state = env._get_state()
if state is None:
return {
"status": "Ready",
"message": "Environment is initialized but no episode is currently active.",
"how_to_start": "Call 'POST /reset' with a task_name (e.g., 'clear_cut_moderation') to begin benchmarking."
}
return state
def kill_port(port):
import subprocess
import os
import sys
try:
if sys.platform == "win32":
# Windows logic
output = subprocess.check_output(f'netstat -ano | findstr :{port}', shell=True).decode()
for line in output.strip().split('\n'):
if 'LISTENING' in line:
pid = line.strip().split()[-1]
if pid != str(os.getpid()):
print(f"Cleanup: Stopping existing process {pid} on port {port}...")
subprocess.run(f'taskkill /F /PID {pid}', shell=True, capture_output=True)
else:
# Unix/Mac/Linux logic
try:
# Use lsof to find the PID
output = subprocess.check_output(['lsof', '-ti', f':{port}']).decode().strip()
if output:
for pid in output.split('\n'):
if pid != str(os.getpid()):
print(f"Cleanup: Stopping existing process {pid} on port {port}...")
subprocess.run(['kill', '-9', pid], capture_output=True)
except (subprocess.CalledProcessError, FileNotFoundError):
# Fallback to fuser if lsof is missing
try:
subprocess.run(['fuser', '-k', f'{port}/tcp'], capture_output=True)
except Exception:
pass
except Exception:
pass
def main():
import uvicorn
# Automatically clear the port before starting to avoid [WinError 10048]
kill_port(7860)
uvicorn.run(app, host="0.0.0.0", port=7860)
if __name__ == "__main__":
main()
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