| import sys
|
| import os
|
|
|
| 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
|
|
|
|
|
| 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"
|
|
|
|
|
| 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"
|
| }
|
|
|
|
|
| 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: TaskName = Field(TaskName.TASK_1, description="Select the benchmark level to initialize.")
|
| 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."""
|
| try:
|
|
|
| internal_task_name = TASK_MAP[req.task_name]
|
| state = await env.reset(task_name=internal_task_name, seed=req.seed)
|
| return state
|
| except ValueError 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(TASKS.keys()),
|
| }
|
|
|
|
|
| @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."""
|
| return [
|
| {
|
| "task_id": task_cfg.name,
|
| "id": task_cfg.name,
|
| "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 task_cfg in TASKS.values()
|
| ]
|
|
|
| @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()
|
|
|
|
|
| @app.get("/grader", tags=["🤖 Automated Benchmarking"])
|
| def grader_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. If no episode has been run yet a
|
| minimal default score is returned.
|
| """
|
|
|
| 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)
|
| 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."""
|
|
|
| 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
|
|
|
|
|
| try:
|
| p_mode = PolicyMode(POLICY_MAP[policy_mode])
|
| except ValueError:
|
| p_mode = PolicyMode.NORMAL
|
|
|
|
|
| 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)
|
|
|
|
|
|
|
|
|
|
|
|
|
| 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:
|
|
|
|
|
| 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)
|
|
|
|
|
| next_state, reward, done, info = await env.step(action)
|
|
|
| final_score = 0.0
|
| grader_id = None
|
| if done:
|
|
|
| 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")
|
|
|
|
|
| memory = []
|
| if os.path.exists(memory_path):
|
| with open(memory_path, "r") as f:
|
| try:
|
| memory = json.load(f)
|
| except:
|
| memory = []
|
|
|
|
|
| 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":
|
|
|
| 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:
|
|
|
| try:
|
|
|
| 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):
|
|
|
| try:
|
| subprocess.run(['fuser', '-k', f'{port}/tcp'], capture_output=True)
|
| except Exception:
|
| pass
|
| except Exception:
|
| pass
|
|
|
| def main():
|
| import uvicorn
|
|
|
| kill_port(7860)
|
| uvicorn.run(app, host="0.0.0.0", port=7860)
|
|
|
| if __name__ == "__main__":
|
| main()
|
|
|
|
|
