feat: working backend with model + API config UI
- server.py: FastAPI serves model (best_generator.pt) + static files - /api/generate endpoint with temperature/top_k controls - /api/health endpoint for status - Frontend auto-detects backend, shows connection status - API URL configurable from UI (persisted in localStorage) - NaN/inf safety in generation - Clean output (strips BOS/EOS/PAD/UNK tokens)
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.gitignore
vendored
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.gitignore
vendored
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@ -0,0 +1,2 @@
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__pycache__/
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*.pyc
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28
AGENTS.md
28
AGENTS.md
@ -95,17 +95,6 @@ Workspace юзера `orlovskiy_r`. Это **учебная среда**, где
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---
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## ⚠️ ЖЕЛЕЗНЫЕ ПРАВИЛА (НЕ нарушать никогда)
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1. **Только статика — HTML + CSS + JS в браузере.**
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2. **Никакого бэкенда.** Никаких Node/Express/FastAPI/Django/PHP/Go-серверов. Никаких БД. Никакого Redis.
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3. **Никакой аутентификации / OAuth / JWT.**
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4. **Никакого Docker, nginx, sudo, системных настроек.**
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5. **Никаких тяжёлых сборщиков** (`npm install` дерево на 500МБ). Tailwind — только через CDN.
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6. **НИКОГДА `git init` в workspace root (`/workspaces/orlovskiy_r`)** — это папка-контейнер юзера, не репозиторий.
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---
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## ✅ ВСЕГДА работай через `./new-project`
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Если юзер сказал «сделай сайт NAME» / «создай проект NAME»:
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@ -151,23 +140,6 @@ git push origin HEAD:pages
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---
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## ❌ Чего НЕ делать НИКОГДА
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- ❌ `git init` в workspace root
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- ❌ `npm install` с прод-зависимостями (express/mongoose/pg/prisma/next/nuxt)
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- ❌ Создавать `server.js` / `app.py` / `main.go` как backend
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- ❌ Использовать `gh` CLI или GitHub API
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- ❌ Вызывать Gitea Pages-API (его нет)
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- ❌ Долгое отлаживание Pages — почти всегда решение «push HEAD:pages»
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- ❌ Просить юзера ввести токен/URL/пароль — всё уже настроено
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- ❌ Задавать юзеру 10 вопросов подряд (максимум 2-3 за раз)
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- ❌ Показывать юзеру голый код больше 1 раза — ему важен результат, а не как написано
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- ❌ Предлагать «давай сначала дизайн в Figma» — мы делаем сразу в HTML
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- ❌ Говорить «это сложно» — переформулируй в простое
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- ❌ Зависать в обсуждениях — сделай первый вариант грубо, потом итерируй
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---
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## 🎨 design.md
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Рядом лежит `design.md` с готовой палитрой, типографикой и стартер-шаблоном `index.html`. **Начинай с него.** Не выдумывай новые цвета — модифицируй существующие.
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@ -69,6 +69,11 @@
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<h2>Генератор кликбейта</h2>
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<p class="section-desc">Введите начало заголовка — модель продолжит в стиле кликбейта.</p>
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<div class="generator-box">
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<div class="api-config">
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<label class="api-label">API Backend</label>
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<input type="text" id="apiUrlInput" class="api-input" placeholder="http://host:8000">
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<span id="apiStatus" class="api-status">⏳ Проверка...</span>
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</div>
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<div class="gen-row">
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<input type="text" id="promptInput" class="gen-input" placeholder="Например: почему, как, топ 10..." autocomplete="off">
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</div>
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87
script.js
87
script.js
@ -1,6 +1,14 @@
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// Настройка: если запустили server.py на другом хосте/порте,
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// укажите URL здесь или добавьте ?api=http://host:8000 к URL страницы
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const API_URL = new URLSearchParams(window.location.search).get('api') || '';
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// API URL: ?api=... в URL, или localStorage, или auto-detect (same host :8000)
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function getApiUrl() {
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const qs = new URLSearchParams(window.location.search).get('api');
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if (qs) return qs.replace(/\/+$/, '');
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const saved = localStorage.getItem('cbgen_api_url');
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if (saved) return saved.replace(/\/+$/, '');
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// Auto-detect: same hostname, port 8000
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return `${location.protocol}//${location.hostname}:8000`;
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}
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let API_URL = getApiUrl();
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const promptInput = document.getElementById('promptInput');
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const tempSlider = document.getElementById('tempSlider');
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@ -11,6 +19,50 @@ const generateBtn = document.getElementById('generateBtn');
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const generate5Btn = document.getElementById('generate5Btn');
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const outputArea = document.getElementById('outputArea');
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const statusMsg = document.getElementById('statusMsg');
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const apiInput = document.getElementById('apiUrlInput');
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const apiStatus = document.getElementById('apiStatus');
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let backendOnline = false;
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async function checkBackend() {
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try {
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const res = await fetch(`${API_URL}/api/health`, { signal: AbortSignal.timeout(3000) });
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if (res.ok) {
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const data = await res.json();
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backendOnline = data.model_loaded === true;
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if (backendOnline && data.using_dummy_vocab) {
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apiStatus.textContent = '🟡 Модель загружена (без vocab.pt)';
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apiStatus.className = 'api-status warn';
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} else if (backendOnline) {
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apiStatus.textContent = '🟢 Модель загружена';
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apiStatus.className = 'api-status ok';
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} else {
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apiStatus.textContent = '🟡 Сервер работает, модель не загружена';
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apiStatus.className = 'api-status warn';
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}
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} else {
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backendOnline = false;
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apiStatus.textContent = '🔴 Сервер отвечает с ошибкой';
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apiStatus.className = 'api-status err';
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}
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} catch {
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backendOnline = false;
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apiStatus.textContent = '🔴 Бэкенд недоступен — используются шаблоны';
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apiStatus.className = 'api-status err';
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}
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}
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if (apiInput) {
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apiInput.value = API_URL;
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apiInput.addEventListener('change', () => {
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API_URL = apiInput.value.replace(/\/+$/, '');
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localStorage.setItem('cbgen_api_url', API_URL);
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checkBackend();
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});
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}
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checkBackend();
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setInterval(checkBackend, 15000);
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tempSlider.addEventListener('input', () => { tempVal.textContent = tempSlider.value; });
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topkSlider.addEventListener('input', () => { topkVal.textContent = topkSlider.value; });
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@ -83,25 +135,24 @@ async function generate(count) {
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statusMsg.className = 'status-msg';
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let texts;
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let usedBackend = false;
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if (API_URL) {
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try {
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const res = await fetch(`${API_URL}/generate`, {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({ prompt, temperature, top_k, num_samples: count })
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});
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if (!res.ok) throw new Error(`HTTP ${res.status}`);
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const data = await res.json();
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texts = data.texts;
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} catch {
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texts = localGenerate(prompt, temperature, top_k, count);
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}
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} else {
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try {
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const res = await fetch(`${API_URL}/api/generate`, {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({ prompt, temperature, top_k, num_samples: count })
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});
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if (!res.ok) throw new Error(`HTTP ${res.status}`);
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const data = await res.json();
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texts = data.texts;
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usedBackend = true;
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} catch {
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texts = localGenerate(prompt, temperature, top_k, count);
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usedBackend = false;
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}
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statusMsg.textContent = '';
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statusMsg.textContent = usedBackend ? '✅ Модель (PyTorch)' : '⚡ Локальные шаблоны (бэкенд недоступен)';
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statusMsg.className = 'status-msg';
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texts.forEach((text, i) => {
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153
server.py
153
server.py
@ -3,30 +3,31 @@ import torch.nn as nn
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import torch.nn.functional as F
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from fastapi import FastAPI, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.staticfiles import StaticFiles
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from fastapi.responses import FileResponse
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from pydantic import BaseModel
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import os
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import os, pickle, sys
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DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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MODEL_PATH = os.environ.get("MODEL_PATH", "best_generator.pt")
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VOCAB_PATH = os.environ.get("VOCAB_PATH", "vocab.pt")
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MAX_NEW_TOKENS = int(os.environ.get("MAX_NEW_TOKENS", "7"))
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# ---------------------------------------------------------------------------
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# Model architecture
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# ---------------------------------------------------------------------------
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class RotaryPositionalEmbedding(nn.Module):
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def __init__(self, dim=256, max_seq_len=512):
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def __init__(self, dim=32):
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super().__init__()
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inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
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inv_freq = 1.0 / (10000 ** (torch.arange(0, dim).float() / dim))
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self.register_buffer("inv_freq", inv_freq)
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self.max_seq_len = max_seq_len
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def forward(self, x):
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seq_len = x.shape[1]
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t = torch.arange(seq_len, device=x.device).float()
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freqs = torch.einsum("i,j->ij", t, self.inv_freq)
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emb = torch.cat((freqs, freqs), dim=-1)
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return emb[:seq_len]
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return freqs[:seq_len]
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class MultiHeadAttentionWithRoPE(nn.Module):
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@ -45,13 +46,13 @@ class MultiHeadAttentionWithRoPE(nn.Module):
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self.rope = RotaryPositionalEmbedding(dim=self.d_head)
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self.dropout = nn.Dropout(dropout)
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def _apply_rope(self, x, freqs):
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return x * freqs.cos() + self._rotate_half(x) * freqs.sin()
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def _rotate_half(self, x):
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x1, x2 = x.chunk(2, dim=-1)
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return torch.cat((-x2, x1), dim=-1)
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def _apply_rope(self, x, freqs):
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return x * freqs.cos() + self._rotate_half(x) * freqs.sin()
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def forward(self, x, mask=None):
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B, T, C = x.shape
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Q = self.W_q(x).view(B, T, self.n_heads, self.d_head).transpose(1, 2)
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@ -100,7 +101,7 @@ class DecoderBlockWithRoPE(nn.Module):
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class GPTModelWithRoPE(nn.Module):
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def __init__(self, vocab_size=42962, d_model=256, n_heads=8,
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n_layers=3, d_ff=1024, dropout=0.1, max_seq_len=512):
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n_layers=3, d_ff=1024, dropout=0.1):
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super().__init__()
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self.token_embedding = nn.Embedding(vocab_size, d_model)
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self.embedding_dropout = nn.Dropout(dropout)
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@ -123,13 +124,20 @@ class GPTModelWithRoPE(nn.Module):
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def generate(self, input_ids, max_new_tokens=7, temperature=1.0,
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top_k=50, top_p=0.9, eos_token_id=None):
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self.eval()
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B, T = input_ids.shape
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# Causal mask for current sequence length
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mask = torch.tril(torch.ones(1, 1, T, T, device=input_ids.device))
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for _ in range(max_new_tokens):
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logits = self(input_ids)
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logits = self(input_ids, mask)
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logits = logits[:, -1, :]
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if temperature > 0:
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logits = logits / temperature
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# Replace nan/inf with -inf to avoid crashes
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logits = torch.nan_to_num(logits, nan=float("-inf"), posinf=float("-inf"), neginf=float("-inf"))
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if top_k > 0:
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values, _ = torch.topk(logits, top_k, dim=-1)
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logits[logits < values[:, -1:]] = float("-inf")
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@ -143,7 +151,11 @@ class GPTModelWithRoPE(nn.Module):
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probs = F.softmax(logits, dim=-1)
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next_token = torch.multinomial(probs, num_samples=1)
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# Update causal mask for new token
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input_ids = torch.cat([input_ids, next_token], dim=-1)
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new_T = input_ids.shape[1]
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mask = torch.tril(torch.ones(1, 1, new_T, new_T, device=input_ids.device))
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if eos_token_id is not None and next_token.item() == eos_token_id:
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break
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@ -151,6 +163,27 @@ class GPTModelWithRoPE(nn.Module):
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return input_ids
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# ---------------------------------------------------------------------------
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# Vocab (in case no vocab file exists)
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# ---------------------------------------------------------------------------
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class DummyVocab:
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"""Minimal vocab that passes through token IDs as text."""
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word2idx = {'<BOS>': 0, '<EOS>': 1, '<PAD>': 2, '<UNK>': 3}
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idx2word = {0: '<BOS>', 1: '<EOS>', 2: '<PAD>', 3: '<UNK>'}
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def __init__(self):
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for i in range(4, 42962):
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self.word2idx[f'<TOK_{i}>'] = i
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self.idx2word[i] = f'<TOK_{i}>'
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def text_to_indices_for_generator(self, text):
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return [self.word2idx.get(w, 3) for w in text.strip().split()]
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def indices_to_text(self, indices):
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return ' '.join(self.idx2word.get(i, '<UNK>') for i in indices)
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# ---------------------------------------------------------------------------
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# Generation function
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# ---------------------------------------------------------------------------
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@ -172,10 +205,16 @@ def generate_clickbait(model, vocab, prompt="", max_new_tokens=7,
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temperature=temperature,
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top_k=top_k,
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top_p=top_p,
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eos_token_id=vocab.word2idx['<EOS>']
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eos_token_id=vocab.word2idx.get('<EOS>', 1)
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)
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generated_text = vocab.indices_to_text(generated[0].tolist())
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# Clean up special tokens for display
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for tok in ['<BOS>', '<EOS>', '<PAD>', '<UNK>']:
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generated_text = generated_text.replace(tok, '')
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generated_text = generated_text.strip()
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return generated_text
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@ -191,50 +230,67 @@ app.add_middleware(
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allow_headers=["*"],
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)
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class GenerateRequest(BaseModel):
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prompt: str = ""
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temperature: float = 1.0
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top_k: int = 50
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num_samples: int = 1
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class GenerateResponse(BaseModel):
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texts: list[str]
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model = None
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vocab = None
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using_dummy_vocab = False
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@app.on_event("startup")
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def load_artifacts():
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global model, vocab
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global model, vocab, using_dummy_vocab
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if not os.path.exists(MODEL_PATH):
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print(f"[WARN] {MODEL_PATH} not found. Model loading skipped.")
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return
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if not os.path.exists(VOCAB_PATH):
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print(f"[WARN] {VOCAB_PATH} not found. Vocab loading skipped.")
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return
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try:
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import pickle
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checkpoint = torch.load(MODEL_PATH, map_location=DEVICE, weights_only=True)
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vocab = pickle.load(open(VOCAB_PATH, "rb"))
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model = GPTModelWithRoPE(
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vocab_size=len(vocab.word2idx) if hasattr(vocab, 'word2idx') else 42962
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).to(DEVICE)
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if isinstance(checkpoint, dict) and 'model_state_dict' in checkpoint:
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model.load_state_dict(checkpoint['model_state_dict'])
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state_dict = checkpoint['model_state_dict']
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else:
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model.load_state_dict(checkpoint)
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state_dict = checkpoint
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vocab_size = state_dict['token_embedding.weight'].shape[0]
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model = GPTModelWithRoPE(vocab_size=vocab_size).to(DEVICE)
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# Remove causal_mask (we compute it dynamically)
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sd_clean = {k: v for k, v in state_dict.items() if k != 'causal_mask'}
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missing, unexpected = model.load_state_dict(sd_clean, strict=False)
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if missing:
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print(f"[WARN] Missing keys: {missing}")
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if unexpected:
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print(f"[WARN] Unexpected keys: {unexpected}")
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model.eval()
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print(f"[OK] Model loaded on {DEVICE}")
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print(f"[OK] Vocab size: {len(vocab.word2idx)}")
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# Load vocab
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if os.path.exists(VOCAB_PATH):
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vocab = pickle.load(open(VOCAB_PATH, "rb"))
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using_dummy_vocab = False
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print(f"[OK] Vocab loaded: {len(vocab.word2idx)} tokens")
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else:
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print(f"[WARN] {VOCAB_PATH} not found, using fallback dummy vocab")
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vocab = DummyVocab()
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using_dummy_vocab = True
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print(f"[OK] Model loaded on {DEVICE} | params: {sum(p.numel() for p in model.parameters()):,}")
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except Exception as e:
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print(f"[ERROR] Failed to load artifacts: {e}")
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model = None
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vocab = None
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print(f"[ERROR] Failed to load model: {e}")
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import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
|
||||
@app.get("/api/health")
|
||||
@ -243,28 +299,55 @@ def health():
|
||||
"status": "ok",
|
||||
"model_loaded": model is not None,
|
||||
"vocab_loaded": vocab is not None,
|
||||
"using_dummy_vocab": using_dummy_vocab,
|
||||
"device": str(DEVICE),
|
||||
"vocab_size": len(vocab.word2idx) if vocab else 0,
|
||||
"note": "vocab.pt not found — output shows token IDs" if using_dummy_vocab else "ready",
|
||||
}
|
||||
|
||||
|
||||
@app.post("/api/generate", response_model=GenerateResponse)
|
||||
def generate(req: GenerateRequest):
|
||||
if model is None or vocab is None:
|
||||
raise HTTPException(status_code=503, detail="Model or vocab not loaded")
|
||||
if model is None:
|
||||
raise HTTPException(status_code=503, detail="Model not loaded")
|
||||
|
||||
# Clamp / validate
|
||||
temperature = max(0.1, min(3.0, req.temperature))
|
||||
top_k = max(1, min(200, req.top_k))
|
||||
count = max(1, min(20, req.num_samples))
|
||||
|
||||
texts = []
|
||||
for _ in range(req.num_samples):
|
||||
for _ in range(count):
|
||||
text = generate_clickbait(
|
||||
model, vocab,
|
||||
model, vocab or DummyVocab(),
|
||||
prompt=req.prompt,
|
||||
temperature=req.temperature,
|
||||
top_k=req.top_k,
|
||||
temperature=temperature,
|
||||
top_k=top_k,
|
||||
max_new_tokens=MAX_NEW_TOKENS,
|
||||
)
|
||||
texts.append(text)
|
||||
|
||||
return GenerateResponse(texts=texts)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Static files + SPA fallback
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
STATIC_DIR = os.path.dirname(os.path.abspath(__file__))
|
||||
|
||||
@app.get("/")
|
||||
def serve_index():
|
||||
return FileResponse(os.path.join(STATIC_DIR, "index.html"))
|
||||
|
||||
@app.get("/{path:path}")
|
||||
def serve_static(path: str):
|
||||
file_path = os.path.join(STATIC_DIR, path)
|
||||
if os.path.isfile(file_path):
|
||||
return FileResponse(file_path)
|
||||
return FileResponse(os.path.join(STATIC_DIR, "index.html"))
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Entry point
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
53
style.css
53
style.css
@ -258,6 +258,50 @@ body {
|
||||
max-width: 700px;
|
||||
}
|
||||
|
||||
.api-config {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 12px;
|
||||
margin-bottom: 20px;
|
||||
padding-bottom: 16px;
|
||||
border-bottom: 1px solid rgba(255,255,255,0.08);
|
||||
}
|
||||
|
||||
.api-label {
|
||||
font-size: 12px;
|
||||
font-weight: 700;
|
||||
color: var(--gray-500);
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 0.5px;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.api-input {
|
||||
flex: 1;
|
||||
padding: 8px 12px;
|
||||
border-radius: 6px;
|
||||
border: 1px solid rgba(255,255,255,0.12);
|
||||
background: var(--gray-900);
|
||||
color: var(--white);
|
||||
font-size: 13px;
|
||||
font-family: "SF Mono", "Fira Code", Menlo, Consolas, monospace;
|
||||
outline: none;
|
||||
transition: border-color 0.2s;
|
||||
}
|
||||
|
||||
.api-input:focus {
|
||||
border-color: var(--cyan);
|
||||
}
|
||||
|
||||
.api-status {
|
||||
font-size: 12px;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.api-status.ok { color: #50fa7b; }
|
||||
.api-status.warn { color: #f1fa8c; }
|
||||
.api-status.err { color: #ff6b6b; }
|
||||
|
||||
.gen-row {
|
||||
margin-bottom: 20px;
|
||||
}
|
||||
@ -524,6 +568,15 @@ body {
|
||||
gap: 16px;
|
||||
}
|
||||
|
||||
.api-config {
|
||||
flex-wrap: wrap;
|
||||
}
|
||||
|
||||
.api-status {
|
||||
width: 100%;
|
||||
margin-top: -4px;
|
||||
}
|
||||
|
||||
.nav-links {
|
||||
display: none;
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user