Revert "Optimizer: SSE progress, cancel job with partial result, UI progress card"

This reverts commit b6a4468ec1517d6b3943366319a5ff2181c7ec81.

app.py

@@ -1,12 +1,7 @@
 # app.py - Hugging Face Spaces entry point
 
-import json
-import threading
-import uuid
-from queue import Queue
-
 from fastapi import FastAPI, Request
-from fastapi.responses import HTMLResponse, StreamingResponse
+from fastapi.responses import HTMLResponse
 from fastapi.templating import Jinja2Templates
 import uvicorn
 import torch
@@ -24,7 +19,6 @@ from models import (
     UserAgentsResponse,
     OptimizerRequest,
     OptimizerResponse,
-    OptimizerCancelRequest,
 )
 import logic
 import nlp_processor
@@ -37,10 +31,6 @@ import optimizer
 
 app = FastAPI(title="SEO AI Editor MVP")
 
-# Один активный job_id на процесс достаточно для HF Space; для отмены без обрыва SSE.
-_OPTIMIZER_JOBS_LOCK = threading.Lock()
-_OPTIMIZER_CANCEL_EVENTS: dict = {}
-
 # Подключаем папку с шаблонами
 templates = Jinja2Templates(directory="templates")
 
@@ -268,71 +258,6 @@ async def run_optimizer(request: OptimizerRequest):
     except Exception as e:
         return OptimizerResponse(ok=False, error=str(e))
 
-
-@app.post("/api/v1/optimizer/cancel")
-async def optimizer_cancel(request: OptimizerCancelRequest):
-    """Сигнал отмены: optimize_text завершится с частичным результатом; клиент продолжает читать SSE."""
-    with _OPTIMIZER_JOBS_LOCK:
-        ev = _OPTIMIZER_CANCEL_EVENTS.get(request.job_id)
-    if ev is not None:
-        ev.set()
-    return {"ok": True, "job_id": request.job_id}
-
-
-@app.post("/api/v1/optimizer/run-stream")
-async def run_optimizer_stream(request: OptimizerRequest):
-    """SSE: прогресс + финальный JSON (в т.ч. при остановке пользователем)."""
-    data = request.model_dump()
-    job_id = str(uuid.uuid4())
-    cancel_ev = threading.Event()
-    with _OPTIMIZER_JOBS_LOCK:
-        _OPTIMIZER_CANCEL_EVENTS[job_id] = cancel_ev
-    q: Queue = Queue(maxsize=500)
-
-    def on_progress(evt: dict) -> None:
-        out = dict(evt)
-        out["job_id"] = job_id
-        q.put(out)
-
-    def worker() -> None:
-        try:
-            result = optimizer.optimize_text(data, progress_callback=on_progress, cancel_event=cancel_ev)
-            q.put({"event": "__result__", "result": result})
-        except Exception as e:
-            q.put({"event": "__error__", "error": str(e)})
-        finally:
-            with _OPTIMIZER_JOBS_LOCK:
-                _OPTIMIZER_CANCEL_EVENTS.pop(job_id, None)
-
-    threading.Thread(target=worker, daemon=True).start()
-
-    def sse_iter():
-        try:
-            while True:
-                item = q.get()
-                if item.get("event") == "__result__":
-                    payload = {"event": "complete", "result": item.get("result")}
-                    yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
-                    break
-                if item.get("event") == "__error__":
-                    payload = {"event": "error", "error": item.get("error", "unknown")}
-                    yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
-                    break
-                yield f"data: {json.dumps(item, ensure_ascii=False)}\n\n"
-        finally:
-            with _OPTIMIZER_JOBS_LOCK:
-                _OPTIMIZER_CANCEL_EVENTS.pop(job_id, None)
-
-    return StreamingResponse(
-        sse_iter(),
-        media_type="text/event-stream",
-        headers={
-            "Cache-Control": "no-cache",
-            "Connection": "keep-alive",
-            "X-Accel-Buffering": "no",
-        },
-    )
-
 # Hugging Face Spaces использует порт 7860
 if __name__ == "__main__":
     port = int(os.environ.get("PORT", 7860))

docs/FULL_FUNCTIONAL_DOCUMENTATION.md

@@ -24,7 +24,7 @@
    - смысловой поиск по словам и фразам
    - сравнение с конкурентами (включая таблицу мощных терминов)
 
-3. **LLM Optimizer** (`POST /api/v1/optimizer/run` или `run-stream` + `cancel` для прогресса и остановки)
+3. **LLM Optimizer** (`POST /api/v1/optimizer/run`)
    - итеративная локальная оптимизация текста
    - многокритериальный скоринг с защитой от деградации
    - каскад уровней правок (от минимальных к более широким)
@@ -179,11 +179,6 @@
 - `phrase_strategy_mode`
 - `bert_stage_target`
 - `error` (если есть)
-- при остановке пользователем во время streaming: `stopped_early: true`, `stop_reason: "user_cancelled"` — в `optimized_text` и метриках сохраняется **частичный** результат на момент остановки.
-
-### UI: `POST /api/v1/optimizer/run-stream` (SSE)
-- Поток `text/event-stream`, события `data: {json}`: `preparing`, `started` (с `job_id`), `step_start`, `llm_call`, финал `complete` с полным телом как у `OptimizerResponse`.
-- **`POST /api/v1/optimizer/cancel`** с телом `{"job_id": "<id из событий>"}` — выставляет флаг отмены; **не** рвёт SSE: клиент дочитывает поток до `complete` с частичным результатом.
 
 ---
 

models.py

@@ -105,10 +105,4 @@ class OptimizerResponse(BaseModel):
     optimization_mode: str = "balanced"
     phrase_strategy_mode: str = "auto"
     bert_stage_target: float = 0.70
-    stopped_early: bool = False
-    stop_reason: str = ""
-    error: str = ""
-
-
-class OptimizerCancelRequest(BaseModel):
-    job_id: str
+    error: str = ""

optimizer.py

@@ -1,7 +1,7 @@
 import json
 import re
 from itertools import combinations
-from typing import Any, Callable, Dict, List, Optional, Tuple
+from typing import Any, Dict, List, Optional, Tuple
 
 import requests
 
@@ -29,18 +29,6 @@ STAGE_ORDER = ["bert", "bm25", "ngram", "semantic", "title"]
 NGRAM_ATTEMPTS_PER_TERM = 3
 
 
-def _emit_progress(
-    progress_callback: Optional[Callable[[Dict[str, Any]], None]],
-    payload: Dict[str, Any],
-) -> None:
-    if progress_callback is None:
-        return
-    try:
-        progress_callback(dict(payload))
-    except Exception:
-        pass
-
-
 def _tokenize(text: str) -> List[str]:
     return [
         x
@@ -1299,11 +1287,7 @@ def _is_candidate_valid(
     return (len(reasons) == 0), reasons, improved
 
 
-def optimize_text(
-    request_data: Dict[str, Any],
-    progress_callback: Optional[Callable[[Dict[str, Any]], None]] = None,
-    cancel_event: Optional[Any] = None,
-) -> Dict[str, Any]:
+def optimize_text(request_data: Dict[str, Any]) -> Dict[str, Any]:
     target_text = str(request_data.get("target_text", "")).strip()
     competitors = [str(x) for x in (request_data.get("competitors") or []) if str(x).strip()]
     keywords = [str(x) for x in (request_data.get("keywords") or []) if str(x).strip()]
@@ -1328,13 +1312,6 @@ def optimize_text(
     bert_stage_target = float(request_data.get("bert_stage_target", BERT_TARGET_THRESHOLD) or BERT_TARGET_THRESHOLD)
     bert_stage_target = max(0.0, min(1.0, bert_stage_target))
 
-    def _is_cancelled() -> bool:
-        return cancel_event is not None and cancel_event.is_set()
-
-    _emit_progress(
-        progress_callback,
-        {"event": "preparing", "phase": "baseline", "message": "Считаем baseline (анализ текста и метрики)…"},
-    )
     baseline_analysis = _build_analysis_snapshot(
         target_text, competitors, keywords, language, target_title, competitor_titles
     )
@@ -1364,32 +1341,8 @@ def optimize_text(
     stage_idx = 0
     stage_no_progress_steps = 0
     stage_goal_cursor: Dict[str, Dict[str, int]] = {}
-    stopped_early = False
-    stop_reason: Optional[str] = None
-
-    _emit_progress(
-        progress_callback,
-        {
-            "event": "started",
-            "total_steps": total_loop_steps,
-            "max_iterations_setting": max_iterations,
-            "ngram_targets": ngram_row_count,
-            "stages_order": list(STAGE_ORDER),
-        },
-    )
 
     for step in range(total_loop_steps):
-        if _is_cancelled():
-            stopped_early = True
-            stop_reason = "user_cancelled"
-            logs.append(
-                {
-                    "step": step + 1,
-                    "status": "stopped_early",
-                    "reason": "Остановка пользователем (начало шага).",
-                }
-            )
-            break
         while stage_idx < len(STAGE_ORDER) and _is_stage_complete(
             STAGE_ORDER[stage_idx], current_metrics, bert_stage_target=bert_stage_target
         ):
@@ -1427,27 +1380,6 @@ def optimize_text(
             logs.append({"step": step + 1, "status": "stopped", "reason": "No sentences available for editing."})
             break
 
-        ng_cur = stage_goal_cursor.get(active_stage) or {}
-        _emit_progress(
-            progress_callback,
-            {
-                "event": "step_start",
-                "step": step + 1,
-                "total_steps": total_loop_steps,
-                "stage_index": stage_idx,
-                "active_stage": active_stage,
-                "goal_type": goal.get("type"),
-                "goal_label": str(goal.get("label", ""))[:160],
-                "cascade_level": cascade_level,
-                "score": current_metrics.get("score"),
-                "ngram_cursor": (
-                    {"term_index": int(ng_cur.get("term_index", 0)), "attempt": int(ng_cur.get("attempt_count", 0))}
-                    if active_stage == "ngram"
-                    else None
-                ),
-            },
-        )
-
         goal_key = f"{goal.get('type', '')}:{goal.get('label', '')}".strip().lower()
         base_attempt_cursor = int(goal_attempt_cursor.get(goal_key, 0))
         span_trials = 2 if cascade_level <= 2 else 3
@@ -1455,7 +1387,6 @@ def optimize_text(
         candidates: List[Dict[str, Any]] = []
         chosen_spans: List[Dict[str, Any]] = []
         candidate_idx = 0
-        optimization_cancelled = False
 
         for st in range(span_trials):
             attempt_cursor = base_attempt_cursor + st
@@ -1497,27 +1428,9 @@ def optimize_text(
                 per_span_candidates,
             )
             for strategy_variant in strategy_plan:
-                if _is_cancelled():
-                    optimization_cancelled = True
-                    stopped_early = True
-                    stop_reason = "user_cancelled"
-                    break
                 candidate_idx += 1
                 temp = min(1.1, max(0.0, temperature + (candidate_idx - 1) * 0.07))
                 try:
-                    _emit_progress(
-                        progress_callback,
-                        {
-                            "event": "llm_call",
-                            "step": step + 1,
-                            "total_steps": total_loop_steps,
-                            "active_stage": active_stage,
-                            "candidate_index": candidate_idx,
-                            "span_trial": st + 1,
-                            "span_trials": span_trials,
-                            "strategy": strategy_variant,
-                        },
-                    )
                     llm_result = _llm_edit_chunk(
                         api_key=api_key,
                         base_url=base_url,
@@ -1724,19 +1637,6 @@ def optimize_text(
                         }
                     )
 
-            if optimization_cancelled:
-                break
-
-        if optimization_cancelled:
-            logs.append(
-                {
-                    "step": step + 1,
-                    "status": "stopped_early",
-                    "reason": "Остановка пользователем: сохранён текст и метрики на момент остановки.",
-                }
-            )
-            break
-
         goal_attempt_cursor[goal_key] = base_attempt_cursor + span_trials
         primary_span = chosen_spans[0] if chosen_spans else {"operation": "-", "span_start": 0, "span_end": 0, "sentence_index": 0, "span_variant": 0, "sentence_before": ""}
 
@@ -2154,6 +2054,4 @@ def optimize_text(
         "optimization_mode": optimization_mode,
         "phrase_strategy_mode": phrase_strategy_mode,
         "bert_stage_target": round(bert_stage_target, 4),
-        "stopped_early": stopped_early,
-        "stop_reason": stop_reason or "",
     }

templates/index.html

@@ -328,24 +328,12 @@
                                 <div class="form-text">Выбор стратегии остается у пользователя.</div>
                             </div>
                         </div>
-                        <div class="d-flex gap-2 mt-3 flex-wrap align-items-center">
-                            <button type="button" class="btn btn-dark" id="btnRunLlmOpt" onclick="runLlmOptimization()">Запустить оптимизацию</button>
-                            <button type="button" class="btn btn-outline-danger" id="btnStopLlmOpt" disabled onclick="requestStopOptimizer()">Остановить и сохранить результат</button>
-                            <button type="button" class="btn btn-outline-secondary" onclick="applyOptimizedText()">Применить в Target</button>
+                        <div class="d-flex gap-2 mt-3">
+                            <button class="btn btn-dark" onclick="runLlmOptimization()">Запустить оптимизацию</button>
+                            <button class="btn btn-outline-secondary" onclick="applyOptimizedText()">Применить в Target</button>
                         </div>
                         <p class="small text-muted mt-2 mb-0">API key не сохраняется в проект и используется только для текущего запроса.</p>
                     </div>
-                    <div id="optimizerProgressCard" class="stat-card mt-3 d-none border border-info">
-                        <h6 class="card-title">Ход оптимизации (LLM)</h6>
-                        <div class="progress mb-2" style="height: 24px;">
-                            <div id="optimizerProgressBar" class="progress-bar progress-bar-striped progress-bar-animated bg-dark"
-                                 role="progressbar" style="width: 0%" aria-valuenow="0" aria-valuemin="0" aria-valuemax="100"></div>
-                        </div>
-                        <p id="optimizerProgressLine1" class="small fw-semibold mb-1 text-dark"></p>
-                        <p id="optimizerProgressLine2" class="text-muted small mb-1"></p>
-                        <p id="optimizerProgressTimer" class="text-muted small mb-2"></p>
-                        <p class="small text-muted mb-0">«Остановить» запрашивает завершение на сервере: соединение не рвётся — приходит <strong>частичный</strong> текст и лог на момент остановки.</p>
-                    </div>
                     <div id="optimizerResultsContainer">
                         <div class="text-center text-muted py-5">Запустите основной анализ и затем оптимизацию.</div>
                     </div>
@@ -363,94 +351,6 @@
     let currentData = null;
     let semanticData = null;
     let optimizerData = null;
-    /** Текущий run-stream job (для POST /optimizer/cancel без Abort fetch) */
-    let optimizerStreamJobId = null;
-
-    const OPTIMIZER_STAGE_LABELS = {
-        bert: 'BERT — релевантность фраз',
-        bm25: 'BM25 — снятие переспама',
-        ngram: 'N-граммы',
-        semantic: 'Семантика',
-        title: 'Заголовок'
-    };
-
-    function resetOptimizerProgressUI() {
-        const bar = document.getElementById('optimizerProgressBar');
-        if (bar) {
-            bar.style.width = '0%';
-            bar.setAttribute('aria-valuenow', '0');
-        }
-        const l1 = document.getElementById('optimizerProgressLine1');
-        const l2 = document.getElementById('optimizerProgressLine2');
-        const tm = document.getElementById('optimizerProgressTimer');
-        if (l1) l1.textContent = '';
-        if (l2) l2.textContent = '';
-        if (tm) tm.textContent = '';
-    }
-
-    function applyOptimizerStreamEvent(data, streamStartMs) {
-        if (!data || typeof data !== 'object') return;
-        const bar = document.getElementById('optimizerProgressBar');
-        const line1 = document.getElementById('optimizerProgressLine1');
-        const line2 = document.getElementById('optimizerProgressLine2');
-        const timerEl = document.getElementById('optimizerProgressTimer');
-        if (timerEl && streamStartMs) {
-            timerEl.textContent = 'Прошло: ' + Math.floor((Date.now() - streamStartMs) / 1000) + ' с';
-        }
-        if (!bar || !line1 || !line2) return;
-        const ev = data.event;
-        if (ev === 'preparing') {
-            bar.style.width = '2%';
-            bar.setAttribute('aria-valuenow', '2');
-            line1.textContent = 'Подготовка';
-            line2.textContent = data.message || (data.phase || '');
-            return;
-        }
-        if (ev === 'started') {
-            bar.style.width = '3%';
-            bar.setAttribute('aria-valuenow', '3');
-            line1.textContent = 'Старт: до ' + (data.total_steps || '?') + ' шагов цикла';
-            const parts = [];
-            if (data.ngram_targets != null) parts.push('целей n-gram: ' + data.ngram_targets);
-            if (data.max_iterations_setting != null) parts.push('базовых итераций: ' + data.max_iterations_setting);
-            line2.textContent = (parts.length ? parts.join(' · ') + '. ') + 'Стадии: ' + (data.stages_order || []).join(' → ');
-            return;
-        }
-        if (ev === 'step_start') {
-            const tot = Math.max(1, data.total_steps || 1);
-            const pct = Math.min(96, Math.round(((data.step - 1) / tot) * 85 + 8));
-            bar.style.width = pct + '%';
-            bar.setAttribute('aria-valuenow', String(pct));
-            const stLabel = OPTIMIZER_STAGE_LABELS[data.active_stage] || data.active_stage || '';
-            line1.textContent = 'Шаг ' + data.step + ' / ' + tot + ' · ' + stLabel;
-            let g = (data.goal_type || '') + (data.goal_label ? ': ' + data.goal_label : '');
-            if (data.score != null) g += ' · score ' + data.score;
-            line2.textContent = g;
-            if (data.ngram_cursor) {
-                line2.textContent += ' · n-gram: попытка ' + (data.ngram_cursor.attempt + 1) + '/3, термин #' + (data.ngram_cursor.term_index + 1);
-            }
-            return;
-        }
-        if (ev === 'llm_call') {
-            line2.textContent = 'Запрос к LLM… кандидат ' + data.candidate_index + ', окно ' + data.span_trial + '/' + data.span_trials + ', стратегия: ' + (data.strategy || '');
-        }
-    }
-
-    async function requestStopOptimizer() {
-        if (!optimizerStreamJobId) return;
-        try {
-            await fetch('/api/v1/optimizer/cancel', {
-                method: 'POST',
-                headers: { 'Content-Type': 'application/json' },
-                body: JSON.stringify({ job_id: optimizerStreamJobId })
-            });
-        } catch (e) {
-            console.warn(e);
-        }
-        const line2 = document.getElementById('optimizerProgressLine2');
-        if (line2) line2.textContent = 'Остановка запрошена, ждём частичный результат на сервере…';
-    }
-
     let semanticTermSortBy = 'target_weight';
     let semanticTermSortDir = 'desc';
     let availableUserAgents = [];
@@ -937,10 +837,6 @@
             return;
         }
 
-        const earlyBanner = data.stopped_early
-            ? `<div class="alert alert-warning mb-3"><strong>Остановлено вручную.</strong> Ниже — текст и метрики на момент остановки (частичный результат). Применённые шаги: <strong>${data.applied_changes ?? 0}</strong>.</div>`
-            : '';
-
         const base = data.baseline_metrics || {};
         const fin = data.final_metrics || {};
         const rows = [
@@ -1054,7 +950,7 @@
             </div>`;
         }).join('');
 
-        container.innerHTML = earlyBanner + `
+        container.innerHTML = `
             <div class="stat-card">
                 <h6 class="card-title">Результат оптимизации</h6>
                 <div class="small mb-2">Применено правок: <strong>${data.applied_changes || 0}</strong></div>
@@ -1124,77 +1020,21 @@
             phrase_strategy_mode: document.getElementById('optimizerPhraseStrategy').value || 'auto'
         };
 
-        const runBtn = document.getElementById('btnRunLlmOpt');
-        const stopBtn = document.getElementById('btnStopLlmOpt');
-        const progCard = document.getElementById('optimizerProgressCard');
-        optimizerStreamJobId = null;
-        if (runBtn) runBtn.disabled = true;
-        if (stopBtn) stopBtn.disabled = false;
-        if (progCard) progCard.classList.remove('d-none');
-        resetOptimizerProgressUI();
-
-        const streamStartMs = Date.now();
-        const tickTimer = setInterval(function () {
-            const te = document.getElementById('optimizerProgressTimer');
-            if (te) te.textContent = 'Прошло: ' + Math.floor((Date.now() - streamStartMs) / 1000) + ' с';
-        }, 1000);
-
+        document.getElementById('loader').style.display = 'block';
         try {
-            const response = await fetch('/api/v1/optimizer/run-stream', {
+            const response = await fetch('/api/v1/optimizer/run', {
                 method: 'POST',
                 headers: { 'Content-Type': 'application/json' },
                 body: JSON.stringify(payload)
             });
             if (!response.ok) throw new Error("Ошибка сервера: " + response.statusText);
-            const reader = response.body.getReader();
-            const decoder = new TextDecoder();
-            let buffer = '';
-            let finalResult = null;
-            while (true) {
-                const chunk = await reader.read();
-                if (chunk.done) break;
-                buffer += decoder.decode(chunk.value, { stream: true });
-                for (;;) {
-                    const sep = buffer.indexOf('\n\n');
-                    if (sep < 0) break;
-                    const raw = buffer.slice(0, sep);
-                    buffer = buffer.slice(sep + 2);
-                    const lines = raw.split('\n');
-                    for (let li = 0; li < lines.length; li++) {
-                        const line = lines[li];
-                        if (line.indexOf('data: ') !== 0) continue;
-                        let payloadJson;
-                        try {
-                            payloadJson = JSON.parse(line.slice(6));
-                        } catch (e) {
-                            continue;
-                        }
-                        if (payloadJson.job_id) optimizerStreamJobId = payloadJson.job_id;
-                        if (payloadJson.event === 'complete') {
-                            finalResult = payloadJson.result;
-                        } else if (payloadJson.event === 'error') {
-                            throw new Error(payloadJson.error || 'Ошибка оптимизатора');
-                        } else {
-                            applyOptimizerStreamEvent(payloadJson, streamStartMs);
-                        }
-                    }
-                }
-            }
-            if (!finalResult) throw new Error('Поток завершился без результата');
-            optimizerData = finalResult;
+            optimizerData = await response.json();
             renderOptimizerResults(optimizerData);
-            if (optimizerData.stopped_early) {
-                alert('Оптимизация остановлена. Показан частичный результат — его можно применить в Target.');
-            }
         } catch (error) {
             alert('Ошибка LLM оптимизации: ' + error.message);
             console.error(error);
         } finally {
-            clearInterval(tickTimer);
-            optimizerStreamJobId = null;
-            if (runBtn) runBtn.disabled = false;
-            if (stopBtn) stopBtn.disabled = true;
-            if (progCard) progCard.classList.add('d-none');
+            document.getElementById('loader').style.display = 'none';
         }
     }