Multi-AI · מערכת חכמה

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שיחה חדשה

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Enter לשליחה · Shift+Enter לשורה חדשה

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\n\n\n\n\n\n`;\n return new Response(html, {\n headers: {\n \"Content-Type\": \"text/html; charset=utf-8\",\n \"Cache-Control\": \"no-store\",\n },\n });\n}","label",[133],"d740001c-bf26-4874-aed8-26491622151a","new-file-6277.ts","5e10e9aa-b1d5-11f1-ac9c-1607ee4eb77e",115,["D",1789566538937],[143],"9b0e279b-4713-44d3-82eb-0f7dc4385b2e","new-file-5400.ts","5edfe03e-b1d5-11f1-ac9c-1607ee4eb77e",116,["D",1789566540297],[149],"d1ffa8f4-635a-41e6-82c7-b35d9a09d648",["D",1789566541170],[28],"7632b2db-ee15-4021-967b-05b73a8efd15","main.ts",98,["D",1789561272847],93,"http","// ============================================\n// main.ts - v34 FINAL\n// v33 + תיקונים:\n// #2: הוחזרו גבולות v30 (MAX_TOKENS=16000, MAX_JA=16000, P_MS=40000, J_MS=60000)\n// #3: תרגום חכם עם glosar + context + anti-hallucination\n// #4: ניקוי \"שהתמונה תעשה על ידי X\"\n// ============================================\n\n// === IMPORTS ===\nimport { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\nimport { renderPage } from \"./page.ts\";\n\n// === TYPES ===\nexport interface AIR {\n p: string;\n pk: string;\n a: string;\n e?: string;\n ms?: number;\n ti?: number;\n to?: number;\n}\nexport interface Msg {\n role: \"user\" | \"assistant\";\n content: string;\n ts?: number;\n}\nexport interface PL {\n [key: string]: string;\n}\n\n// === CONSTANTS - CHAT MODELS (מאומתים מ-API) ===\nconst MODELS: Record = {\n gemini: \"gemini-3.8-flash\",\n groq: \"openai/gpt-oss-120b\",\n cerebras: \"gpt-oss-120b\",\n nvidia: \"meta/llama-3.3-70b-instruct\",\n mistral: \"mistral-small-latest\",\n openrouter: \"nvidia/nemotron-3-super-120b-a12b:free\",\n cohere: \"command-r-plus\",\n zai: \"glm-4-flash\",\n dahl: \"MiniMaxAI/MiniMax-M2.7\",\n};\n\n// === IMAGE MODELS (מאומתים) ===\nconst NANO_BANANA = \"gemini-2.5-flash-image\";\nconst NANO_BANANA_PRO = \"gemini-3-pro-image\";\nconst NANO_BANANA_2 = \"gemini-3.1-flash-image\";\nconst NANO_BANANA_2_LITE = \"gemini-3.1-flash-lite-image\";\nconst JUDGE_MODEL = \"gemini-3.1-pro-preview\";\nconst TITLE_MODEL = \"gemini-3.5-flash-lite\";\n\n// === CF / ENDPOINTS ===\nconst CF_MODEL = \"@cf/meta/llama-3.3-70b-instruct-fp8-fast\";\nconst CF_IMAGE_MODEL = \"@cf/black-forest-labs/flux-1-schnell\";\nconst POLLINATIONS_BASE = \"https://gen.pollinations.ai\";\nconst LUMENFALL_BASE = \"https://api.lumenfall.ai/openai/v1/images/generations\";\nconst HUGGINGFACE_BASE = \"https://api-inference.huggingface.co/models\";\n\n// === LUMENFALL MODELS ===\nconst LUMENFALL_MODELS: Record = {\n draft: \"flux.1-schnell-fp8\",\n hd: \"flux-2-pro\",\n ultra: \"nano-banana-2\",\n};\n\n// === HUGGINGFACE MODELS ===\nconst HF_MODELS: Record = {\n draft: \"black-forest-labs/FLUX.1-schnell\",\n hd: \"black-forest-labs/FLUX.1-dev\",\n ultra: \"black-forest-labs/FLUX.1-dev\",\n};\n\n// === TIMEOUTS - FIX #2: הוחזרו מ-v30 ===\nconst P_MS = 40000, J_MS = 60000, RETRY_MS = 1500, RETRIES = 1;\n\n// === LIMITS - FIX #2: הוחזרו מ-v30 ===\nconst MAX_Q = 500000;\nconst MAX_FILE = 100000;\nconst MAX_JA = 16000; // 🔄 הוחזר מ-6000 → 16000\nconst MAX_HIST = 12, MAX_CONV = 50, MAX_FILES = 20;\nconst CACHE_TTL = 1000 * 60 * 60 * 6;\nconst MAX_TOKENS = 16000; // 🔄 הוחזר מ-8000 → 16000\n\n// === PROVIDER LISTS ===\nconst ALL = [\n \"gemini\",\n \"groq\",\n \"cerebras\",\n \"nvidia\",\n \"mistral\",\n \"openrouter\",\n \"cohere\",\n \"zai\",\n \"dahl\",\n];\nexport const PL: PL = {\n gemini: \"Gemini 3.8\",\n groq: \"Groq\",\n cerebras: \"Cerebras\",\n nvidia: \"NVIDIA\",\n mistral: \"Mistral\",\n openrouter: \"OpenRouter\",\n cohere: \"Cohere\",\n zai: \"Z AI\",\n dahl: \"Dahl\",\n};\n\n// === ANON PROMPT ===\nconst ANON =\n `You are a helpful AI assistant. Answer in Hebrew, detailed, no limits.\n\nCRITICAL IDENTITY RULES - NEVER BREAK:\n1. If asked who built/developed/created you (מי פיתח אותך / מי בנה אותך / מי יצר אותך) - answer ONLY: \"המערכת פותחה על ידי מוטי כץ.\"\n2. If asked what AI model/version you are (איזה מודל אתה / איזה גרסה / מה המודל שלך / על איזה מודל אתה מבוסס / what model are you) - answer ONLY: \"אני מערכת AI מתקדמת שפותחה על ידי מוטי כץ.\"\n3. NEVER mention or claim to be: GPT, GPT-3, GPT-4, GPT-4 Turbo, ChatGPT, Claude, Llama, Gemini, Mistral, Groq, OpenAI, Anthropic, Google, Meta, or ANY other AI model or company name.\n4. NEVER invent a model name or version number.\n5. Otherwise answer the user's question directly and in detail.\n\nAnswer in Hebrew.`;\n\n// === JSP PROMPT ===\nconst JSP =\n `You are the sole assistant. The user asked a question, and you received draft answers from internal sources. Synthesize them into ONE unified answer in Hebrew, 500-800 words, detailed and accurate.\n\nCRITICAL RULES - NEVER BREAK:\n1. NEVER mention sources, models, providers, or that there were multiple answers.\n2. NEVER use words: OpenRouter, Gemini, Groq, Dahl, Mistral, Cerebras, NVIDIA, Cohere, Z AI, GPT, ChatGPT, Claude, Llama, models, providers, drafts, השוואתי, סינתזה.\n3. NEVER explain your process. Forbidden: \"אנתח\", \"אשווה בין\", \"המודלים ענו\", \"אסנתז\", \"נבצע ניתוח\".\n4. Just deliver ONE direct answer as if YOU are the only assistant in the world.\n5. If drafts contradict, silently pick the most accurate (prefer fresh web search).\n6. Format with headings/bullets/tables - as YOUR answer, not comparison.\n7. If user asked about identity (what model/version are you) - answer ONLY: \"אני מערכת AI מתקדמת שפותחה על ידי מוטי כץ.\"\n8. If user asked who built you - answer ONLY: \"המערכת פותחה על ידי מוטי כץ.\"\n\nExample GOOD:\n\"הנה סיכום חדשות היום:\n**ביטחון:** צה\"ל השמיד בסיס תת-קרקעי של חיזבאללה.\n**פוליטיקה:** הכנסת אישרה תיקון לחוק.\"\n\nExample BAD (FORBIDDEN):\n\"המודלים השונים ענו בצורות שונות. OpenRouter היה מפורט, Gemini היה תמציתי.\"`;\n\n// ============================================\n// UTILS\n// ============================================\nconst rateLimitMap = new Map();\nfunction checkRateLimit(cid: string): boolean {\n const now = Date.now();\n const arr = rateLimitMap.get(cid) || [];\n const recent = arr.filter((t) => now - t < 60000);\n if (recent.length >= 30) return false;\n recent.push(now);\n rateLimitMap.set(cid, recent);\n return true;\n}\nasync function wt(p: Promise, ms: number, fb: T): Promise {\n let t: any;\n const tp = new Promise((r) => {\n t = setTimeout(() => r(fb), ms);\n });\n try {\n return await Promise.race([p, tp]);\n } finally {\n if (t) clearTimeout(t);\n }\n}\nasync function fr(\n url: string,\n o: RequestInit,\n mr = RETRIES,\n): Promise {\n let last: Response | null = null;\n for (let i = 0; i <= mr; i++) {\n const r = await fetch(url, o);\n if (r.status !== 429 && r.status !== 402) return r;\n last = r;\n if (i < mr) await new Promise((x) => setTimeout(x, RETRY_MS));\n }\n return last as Response;\n}\nfunction tr(s: string, n: number): string {\n return !s ? \"\" : s.length <= n ? s : s.slice(0, n) + \"...\";\n}\nfunction et(s: string): number {\n return s ? Math.max(1, Math.round(s.length / 4)) : 0;\n}\nfunction sj(d: unknown): string {\n return JSON.stringify(d).replace(//g, \"\\\\u003e\")\n .replace(/&/g, \"\\\\u0026\").replace(/\\u2028/g, \"\\\\u2028\").replace(\n /\\u2029/g,\n \"\\\\u2029\",\n );\n}\nfunction qHash(q: string): string {\n return q.toLowerCase().trim().replace(/\\s+/g, \" \").slice(0, 300);\n}\nfunction isNewsQuery(q: string): boolean {\n const l = q.toLowerCase();\n return l.includes(\"חדשות\") || l.includes(\"היום\") || l.includes(\"news\");\n}\nfunction isMetaQuestion(q: string): boolean {\n const l = q.toLowerCase().trim();\n const patterns = [\n \"איזה מודל\",\n \"איזה גרסה\",\n \"מה המודל\",\n \"מה הגרסה\",\n \"איזה מודל אתה\",\n \"מי פיתח\",\n \"מי בנה\",\n \"מי יצר\",\n \"מי אתה\",\n \"מה אתה\",\n \"על איזה מודל\",\n \"על איזה גרסה\",\n \"what model\",\n \"which version\",\n \"what version\",\n \"who made you\",\n \"who built you\",\n \"who created you\",\n ];\n return patterns.some((k) => l.includes(k));\n}\nfunction validateAnswer(answer: string): boolean {\n if (!answer || answer.length < 15) return false;\n const trimmed = answer.trim();\n if (/^Source:\\s/m.test(trimmed)) return false;\n if (/^URL:\\s/m.test(trimmed)) return false;\n if (trimmed.startsWith(\"Source:\") || trimmed.startsWith(\"--- \")) return false;\n if (\n trimmed.includes(\"support.apple.com\") || trimmed.includes(\"apple.com/he-il\")\n ) return false;\n if (trimmed.includes(\"iPhone 1\") || trimmed.includes(\"iOS 26\")) return false;\n const forbidden = [\n \"OpenRouter said\",\n \"Gemini said\",\n \"Groq said\",\n \"Dahl said\",\n \"Mistral said\",\n \"Cerebras said\",\n ];\n if (forbidden.some((f) => trimmed.includes(f))) return false;\n const urlCount = (trimmed.match(/https?:\\/\\//g) || []).length;\n if (urlCount > 3 && urlCount * 100 > trimmed.length) return false;\n return true;\n}\n\n// ============================================\n// TRANSLATION CACHE + GLOSAR (FIX #3)\n// ============================================\nconst translationCache = new Map();\nconst TRANSLATION_CACHE_MAX = 200;\nfunction cacheTranslation(src: string, dst: string) {\n if (translationCache.size >= TRANSLATION_CACHE_MAX) {\n const firstKey = translationCache.keys().next().value;\n if (firstKey) translationCache.delete(firstKey);\n }\n translationCache.set(src, dst);\n}\n// 🔄 FIX #3: glosar למילים עבריות עם הקשר פעולה\nconst HEBREW_GLOSAR: Record = {\n \"אוכל\": \"eating\",\n \"שותה\": \"drinking\",\n \"רץ\": \"running\",\n \"ישן\": \"sleeping\",\n \"מדבר\": \"speaking\",\n \"צוחק\": \"laughing\",\n \"בוכה\": \"crying\",\n \"קורא\": \"reading\",\n \"כותב\": \"writing\",\n \"מצייר\": \"painting\",\n \"שר\": \"singing\",\n \"רוקד\": \"dancing\",\n \"אוכל המבורגר\": \"eating a hamburger\",\n \"שותה קפה\": \"drinking coffee\",\n \"קוראת ספר\": \"reading a book\",\n \"אייל\": \"deer\",\n \"צבי\": \"deer\",\n \"ארנב\": \"rabbit\",\n \"חתול\": \"cat\",\n \"כלב\": \"dog\",\n \"מכונית\": \"car\",\n \"רולס רויס\": \"Rolls-Royce\",\n};\nfunction preprocessWithGlosar(text: string): string {\n // החלף רק אם המילה מופיעה כיחידה שלמה\n let result = text;\n for (const [heb, eng] of Object.entries(HEBREW_GLOSAR)) {\n const re = new RegExp(\n `(? {\n if (!cid) return [];\n try {\n const r = await sqlite.execute({\n sql:\n \"SELECT role, content FROM conversations WHERE conversation_id =? ORDER BY created_at DESC LIMIT?\",\n args: [cid, MAX_HIST],\n });\n return (r.rows as any[] || []).map((\n row,\n ) => ({\n role: String(row.role) === \"user\" ? \"user\" : \"assistant\",\n content: String(row.content || \"\"),\n } as Msg)).filter((m) => m.content).reverse();\n } catch {\n return [];\n }\n}\nasync function dbSave(cid: string, role: string, content: string) {\n if (!cid || !content) return;\n try {\n await sqlite.execute({\n sql:\n \"INSERT INTO conversations (conversation_id, role, content, created_at) VALUES (?,?,?,?)\",\n args: [cid, role, content, Date.now()],\n });\n } catch {}\n}\nasync function dbSaveRaw(cid: string, question: string, raws: AIR[]) {\n if (!cid || !raws.length) return;\n const now = Date.now();\n try {\n for (const r of raws) {\n if (!r.a) continue;\n await sqlite.execute({\n sql:\n \"INSERT INTO raw_answers (conversation_id, question, provider, provider_label, answer, created_at) VALUES (?,?,?,?,?,?)\",\n args: [cid, question, r.pk, r.p, r.a, now],\n });\n }\n } catch {}\n}\nasync function dbGetRaw(cid: string) {\n try {\n const r = await sqlite.execute({\n sql:\n \"SELECT question, provider, provider_label, answer, created_at FROM raw_answers WHERE conversation_id =? ORDER BY created_at ASC\",\n args: [cid],\n });\n return (r.rows as any[] || []).map((row) => ({\n question: String(row.question || \"\"),\n provider: String(row.provider || \"\"),\n label: String(row.provider_label || \"\"),\n answer: String(row.answer || \"\"),\n ts: Number(row.created_at) || 0,\n }));\n } catch {\n return [];\n }\n}\nasync function getCache(qh: string) {\n try {\n const r = await sqlite.execute({\n sql: \"SELECT answer, raw_json, created_at FROM cache WHERE qhash=?\",\n args: [qh],\n });\n const row = (r.rows as any[])?.[0];\n if (!row) return null;\n if (Date.now() - Number(row.created_at) > CACHE_TTL) return null;\n return {\n answer: String(row.answer),\n raw: JSON.parse(String(row.raw_json || \"[]\")),\n };\n } catch {\n return null;\n }\n}\nasync function setCache(qh: string, q: string, answer: string, raw: AIR[]) {\n try {\n await sqlite.execute({\n sql:\n \"INSERT OR REPLACE INTO cache (qhash, question, answer, raw_json, created_at) VALUES (?,?,?,?,?)\",\n args: [qh, q, answer, JSON.stringify(raw), Date.now()],\n });\n } catch {}\n}\nasync function dbMeta(cid: string, title: string) {\n if (!cid) return;\n try {\n const ex = await sqlite.execute({\n sql: \"SELECT conversation_id FROM conv_meta WHERE conversation_id =?\",\n args: [cid],\n });\n const now = Date.now();\n const safeTitle = tr(title, 60) || \"שיחה חדשה\";\n if ((ex.rows as any[]).length === 0) {\n await sqlite.execute({\n sql:\n \"INSERT INTO conv_meta (conversation_id, title, created_at, updated_at, pinned) VALUES (?,?,?,?,?)\",\n args: [cid, safeTitle, now, now, 0],\n });\n } else {\n await sqlite.execute({\n sql:\n \"UPDATE conv_meta SET updated_at =?, title = COALESCE(title,?) WHERE conversation_id =?\",\n args: [now, safeTitle, cid],\n });\n }\n } catch {}\n}\nasync function dbList() {\n try {\n const r = await sqlite.execute({\n sql:\n \"SELECT conversation_id, title, updated_at, pinned FROM conv_meta ORDER BY pinned DESC, updated_at DESC LIMIT?\",\n args: [MAX_CONV],\n });\n return (r.rows as any[] || []).map((row) => ({\n id: String(row.conversation_id || \"\"),\n title: String(row.title || \"ללא כותרת\"),\n updated_at: Number(row.updated_at) || 0,\n pinned: Number(row.pinned) || 0,\n }));\n } catch {\n return [];\n }\n}\nasync function dbFull(cid: string): Promise {\n if (!cid) return [];\n try {\n const r = await sqlite.execute({\n sql:\n \"SELECT role, content, created_at FROM conversations WHERE conversation_id =? ORDER BY created_at ASC\",\n args: [cid],\n });\n return (r.rows as any[] || []).map((\n row,\n ) => ({\n role: String(row.role) === \"user\" ? \"user\" : \"assistant\",\n content: String(row.content || \"\"),\n ts: Number(row.created_at) || 0,\n } as Msg)).filter((m) => m.content);\n } catch {\n return [];\n }\n}\nasync function dbDel(cid: string) {\n if (!cid) return false;\n try {\n await sqlite.execute({\n sql: \"DELETE FROM conversations WHERE conversation_id =?\",\n args: [cid],\n });\n await sqlite.execute({\n sql: \"DELETE FROM conv_meta WHERE conversation_id =?\",\n args: [cid],\n });\n await sqlite.execute({\n sql: \"DELETE FROM raw_answers WHERE conversation_id =?\",\n args: [cid],\n });\n return true;\n } catch {\n return false;\n }\n}\nasync function dbRen(cid: string, title: string) {\n if (!cid || !title) return false;\n try {\n await sqlite.execute({\n sql:\n \"UPDATE conv_meta SET title =?, updated_at =? WHERE conversation_id =?\",\n args: [tr(title, 80), Date.now(), cid],\n });\n return true;\n } catch {\n return false;\n }\n}\nasync function dbHealth(provider: string, success: boolean) {\n try {\n const now = Date.now();\n const ex = await sqlite.execute({\n sql: \"SELECT provider FROM provider_health WHERE provider =?\",\n args: [provider],\n });\n if ((ex.rows as any[]).length === 0) {\n await sqlite.execute({\n sql:\n \"INSERT INTO provider_health (provider, last_success, last_failure, failure_count) VALUES (?,?,?,?)\",\n args: [provider, success ? now : 0, success ? 0 : now, success ? 0 : 1],\n });\n } else if (success) {\n await sqlite.execute({\n sql:\n \"UPDATE provider_health SET last_success =?, failure_count = 0 WHERE provider =?\",\n args: [now, provider],\n });\n } else {\n await sqlite.execute({\n sql:\n \"UPDATE provider_health SET last_failure =?, failure_count = failure_count + 1 WHERE provider =?\",\n args: [now, provider],\n });\n }\n } catch {}\n}\nasync function dbGetHealth() {\n try {\n const r = await sqlite.execute({\n sql:\n \"SELECT provider, last_success, last_failure, failure_count FROM provider_health\",\n });\n return (r.rows as any[] || []).map((row) => ({\n provider: String(row.provider),\n last_success: Number(row.last_success) || 0,\n last_failure: Number(row.last_failure) || 0,\n failure_count: Number(row.failure_count) || 0,\n }));\n } catch {\n return [];\n }\n}\nasync function getCachedTitle(qh: string): Promise {\n try {\n const r = await sqlite.execute({\n sql: \"SELECT title, created_at FROM title_cache WHERE qhash=?\",\n args: [qh],\n });\n const row = (r.rows as any[])?.[0];\n if (!row) return null;\n if (Date.now() - Number(row.created_at) > CACHE_TTL) return null;\n return String(row.title);\n } catch {\n return null;\n }\n}\nasync function setCachedTitle(qh: string, title: string) {\n try {\n await sqlite.execute({\n sql:\n \"INSERT OR REPLACE INTO title_cache (qhash, title, created_at) VALUES (?,?,?)\",\n args: [qh, title, Date.now()],\n });\n } catch {}\n}\n\n// ============================================\n// TRANSLATE\n// ============================================\nasync function generateTitle(q: string, k: string): Promise {\n if (!k) return tr(q, 40);\n const qh = \"title:\" + qHash(q);\n const cached = await getCachedTitle(qh);\n if (cached) return cached;\n try {\n const url = \"https://generativelanguage.googleapis.com/v1beta/models/\" +\n TITLE_MODEL + \":generateContent?key=\" + encodeURIComponent(k);\n const r = await fr(url, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n contents: [{\n parts: [{\n text: 'כותרת 3-4 מילים בעברית (בלי מירכאות): \"' + tr(q, 200) + '\"',\n }],\n }],\n generationConfig: { temperature: 0.3, maxOutputTokens: 20 },\n }),\n }, 0);\n if (!r.ok) return tr(q, 40);\n const d = await r.json();\n const title =\n d?.candidates?.[0]?.content?.parts?.[0]?.text?.trim().replace(/[\"']/g, \"\")\n .slice(0, 40) || tr(q, 40);\n if (title && title !== tr(q, 40)) await setCachedTitle(qh, title);\n return title;\n } catch {\n return tr(q, 40);\n }\n}\nfunction ctx(hist: Msg[], q: string): string {\n if (hist.length === 0) return q;\n return tr(\n \"Previous:\\n\" + hist.map((m) =>\n (m.role === \"user\" ? \"User: \" : \"Assistant: \") + m.content\n ).join(\"\\n\") + \"\\n\\nQuestion: \" + q,\n 15000,\n );\n}\nasync function webSearch(q: string, k: string): Promise {\n if (!k || !q) return \"\";\n try {\n const today = new Date().toISOString().split(\"T\")[0];\n const searchQ = isNewsQuery(q) ? q + \" \" + today + \" Israel\" : q;\n const r = await fetch(\"https://api.tavily.com/search\", {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n api_key: k,\n query: searchQ,\n max_results: 8,\n search_depth: \"advanced\",\n time_range: isNewsQuery(q) ? \"day\" : undefined,\n include_answer: true,\n }),\n });\n if (!r.ok) return \"\";\n const d = await r.json();\n return (d.results as any[] || []).map((x) =>\n \"Source: \" + x.title + \"\\n\" + x.content + \"\\nURL: \" + x.url\n ).join(\"\\n\\n\") + (d.answer ? \"\\n\\nSummary: \" + d.answer : \"\");\n } catch {\n return \"\";\n }\n}\n\n// === FIX #3: translateToEnglish משופר עם glosar + context ===\nasync function translateToEnglish(p: string, k: string): Promise {\n if (!k || !p) return p;\n if (/^[a-zA-Z0-9,.'\"\\-\\s]+$/.test(p) && p.length < 100) return p;\n\n // Check cache first\n const cached = translationCache.get(p);\n if (cached) return cached;\n\n // 🔄 FIX #3: glosar preprocessing\n const processed = preprocessWithGlosar(p);\n // אם ה-glosar כבר תרגם הכל - החזר ישר\n if (/^[a-zA-Z0-9,.'\"\\-\\s]+$/.test(processed)) {\n const result = processed.slice(0, 500);\n cacheTranslation(p, result);\n return result;\n }\n\n try {\n const url = \"https://generativelanguage.googleapis.com/v1beta/models/\" +\n TITLE_MODEL + \":generateContent?key=\" + encodeURIComponent(k);\n const r = await fr(url, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n contents: [{\n parts: [{\n text:\n `You are a professional Hebrew-to-English translator for AI image prompts.\n\nTASK: Translate the Hebrew text into natural English for AI image generation.\n\nCRITICAL RULES:\n1. Output ONLY the English translation.\n2. Preserve ALL details - never summarize or omit words.\n3. Interpret ambiguous words by CONTEXT:\n - \"אוכל\" after a living subject (person/animal) = \"eating\" (verb), NOT \"food\"\n - \"שותה\" = \"drinking\"\n - \"רץ\" = \"running\"\n - \"ישן\" = \"sleeping\"\n4. Do NOT invent words that do not exist in the source.\n5. If the source is short (1-3 words), DO NOT add extra words.\n6. If words are already in English, keep them as-is.\n7. Add commas between descriptive clauses.\n\nHebrew text: \"${processed}\"\n\nEnglish translation:`,\n }],\n }],\n generationConfig: { temperature: 0.0, maxOutputTokens: 500 },\n }),\n }, 0);\n if (!r.ok) return p;\n const d = await r.json();\n const translated =\n d?.candidates?.[0]?.content?.parts?.[0]?.text?.trim().replace(\n /^[\"']|[\"']$/g,\n \"\",\n ) || p;\n\n // 🔄 FIX #3: זיהוי הזיה - אם 3 מילים הפכו ליותר מדי\n const srcWords = p.split(/\\s+/).filter((w) => w.length > 0).length;\n const dstWords = translated.split(/\\s+/).filter((w) => w.length > 0).length;\n const isHallucination = srcWords <= 3 && dstWords > srcWords * 2.5;\n\n if (!isHallucination && translated.length >= p.length * 0.6) {\n const result = translated.slice(0, 500);\n cacheTranslation(p, result);\n return result;\n }\n\n // retry with simpler prompt\n const r2 = await fr(url, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n contents: [{\n parts: [{\n text:\n `Translate to English, preserve ALL details, do not add extra words. Hebrew: ${processed}\\nEnglish:`,\n }],\n }],\n generationConfig: { temperature: 0.0, maxOutputTokens: 500 },\n }),\n }, 0);\n if (r2.ok) {\n const d2 = await r2.json();\n const t2 =\n d2?.candidates?.[0]?.content?.parts?.[0]?.text?.trim().replace(\n /^[\"']|[\"']$/g,\n \"\",\n ) || \"\";\n if (t2.length > translated.length) {\n const result = t2.slice(0, 500);\n cacheTranslation(p, result);\n return result;\n }\n }\n const final = translated.slice(0, 500);\n cacheTranslation(p, final);\n return final;\n } catch {\n return p;\n }\n}\n\n// === FIX #3: translateToEnglishSafe עם סף מינימום 0.8 לקלט קצר ===\nasync function translateToEnglishSafe(p: string, k: string): Promise {\n const primary = await translateToEnglish(p, k);\n // 🔄 FIX #3: סף גבוה יותר לקלט קצר\n const minRatio = p.length < 30 ? 0.8 : 0.6;\n if (p.length > 15 && primary.length < p.length * minRatio) {\n try {\n const chunks = p.split(/[\\s,،;.\\n]+/).map((s) => s.trim()).filter((s) =>\n s.length > 1\n );\n if (chunks.length > 1) {\n const parts: string[] = [];\n for (const c of chunks) {\n const t = await translateToEnglish(c, k);\n parts.push(t);\n }\n const joined = parts.join(\", \");\n if (joined.length > primary.length * 1.2) return joined.slice(0, 500);\n }\n } catch {}\n }\n return primary;\n}\n\n// ============================================\n// POLLINATIONS CATALOG\n// ============================================\nlet pollinationsCatalog: any[] | null = null;\nconst aliasToCanonical = new Map();\nconst canonicalToMeta = new Map();\nasync function fetchPollinationsCatalog(): Promise {\n if (pollinationsCatalog) return pollinationsCatalog;\n try {\n const r = await fetch(POLLINATIONS_BASE + \"/models\");\n if (!r.ok) return [];\n const data = await r.json();\n const list = Array.isArray(data)\n ? data\n : (data?.models || data?.data || []);\n pollinationsCatalog = list;\n aliasToCanonical.clear();\n canonicalToMeta.clear();\n for (const m of list) {\n const canonical = m.id || m.name || \"\";\n if (!canonical) continue;\n const meta = {\n id: canonical,\n publisher: m.publisher || m.brand || canonical.split(\"/\")[0],\n brand_url: m.brand_url || \"\",\n aliases: m.aliases || [],\n ...m,\n };\n canonicalToMeta.set(canonical, meta);\n aliasToCanonical.set(canonical, canonical);\n for (const alias of (m.aliases || [])) {\n aliasToCanonical.set(alias, canonical);\n }\n const short = canonical.split(\"/\").pop()!;\n if (!aliasToCanonical.has(short)) aliasToCanonical.set(short, canonical);\n }\n return list;\n } catch {\n return [];\n }\n}\nfunction resolveModelId(input: string): string {\n if (aliasToCanonical.has(input)) return aliasToCanonical.get(input)!;\n const low = input.toLowerCase();\n for (const [k, v] of aliasToCanonical.entries()) {\n if (k.toLowerCase() === low) return v;\n }\n return input;\n}\nfunction getModelMeta(id: string) {\n const canonical = resolveModelId(id);\n return canonicalToMeta.get(canonical) ||\n { id: canonical, publisher: \"Unknown\", brand_url: \"\" };\n}\n\n// ============================================\n// IMAGE GENERATION - 9 LAYERS\n// ============================================\nasync function genImage(\n promptEn: string,\n acc: string,\n tok: string,\n style: string = \"photorealistic\",\n quality: string = \"hd\",\n geminiKey: string = \"\",\n pollinationsKey: string = \"\",\n lumenfallKey: string = \"\",\n huggingfaceKey: string = \"\",\n): Promise {\n const pk = \"image\", p = \"🎨 ULTRA\";\n const seed = Math.floor(Math.random() * 999999) + 1;\n let width = 1280, height = 1280, steps = 20;\n if (quality === \"ultra\") {\n width = 1536;\n height = 1536;\n steps = 30;\n } else if (quality === \"draft\") {\n width = 768;\n height = 768;\n steps = 12;\n }\n\n let suffix =\n \", ultra detailed, 8k, masterpiece, photorealistic, cinematic lighting, sharp focus, best quality\";\n if (style === \"anime\") suffix = \", anime style, vibrant, 8k\";\n else if (style === \"3d\") suffix = \", 3d render, octane, 8k\";\n else if (style === \"fast\") suffix = \", quick sketch, minimal\";\n const enhanced = promptEn + suffix;\n const enc = encodeURIComponent(enhanced);\n\n // LAYER 1: Nano Banana Pro (ultra only)\n if (geminiKey && quality === \"ultra\") {\n try {\n const url = \"https://generativelanguage.googleapis.com/v1beta/models/\" +\n NANO_BANANA_PRO + \":generateContent?key=\" +\n encodeURIComponent(geminiKey);\n const r = await fr(url, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n contents: [{ parts: [{ text: enhanced }] }],\n generationConfig: { responseModalities: [\"IMAGE\", \"TEXT\"] },\n }),\n }, 0);\n if (r.ok) {\n const d = await r.json();\n const parts = d?.candidates?.[0]?.content?.parts || [];\n for (const part of parts) {\n if (part.inlineData && part.inlineData.data) {\n console.log(\"[IMG] Layer 1 (Nano Banana Pro) OK\");\n return {\n p: \"🍌 ננו-בננה PRO\",\n pk,\n a: \"![תמונה](data:\" + (part.inlineData.mimeType || \"image/png\") +\n \";base64,\" + part.inlineData.data + \")\",\n ms: 0,\n };\n }\n }\n }\n console.log(\"[IMG] Layer 1 failed status:\", r.status);\n } catch (e) {\n console.log(\"[IMG] Layer 1 error:\", String(e));\n }\n }\n\n // LAYER 2: Nano Banana 2\n if (geminiKey && quality !== \"draft\") {\n try {\n const url = \"https://generativelanguage.googleapis.com/v1beta/models/\" +\n NANO_BANANA_2 + \":generateContent?key=\" + encodeURIComponent(geminiKey);\n const r = await fr(url, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n contents: [{ parts: [{ text: enhanced }] }],\n generationConfig: { responseModalities: [\"IMAGE\", \"TEXT\"] },\n }),\n }, 0);\n if (r.ok) {\n const d = await r.json();\n const parts = d?.candidates?.[0]?.content?.parts || [];\n for (const part of parts) {\n if (part.inlineData && part.inlineData.data) {\n console.log(\"[IMG] Layer 2 (Nano Banana 2) OK\");\n return {\n p: \"🍌 ננו-בננה 2\",\n pk,\n a: \"![תמונה](data:\" + (part.inlineData.mimeType || \"image/png\") +\n \";base64,\" + part.inlineData.data + \")\",\n ms: 0,\n };\n }\n }\n }\n console.log(\"[IMG] Layer 2 failed status:\", r.status);\n } catch (e) {\n console.log(\"[IMG] Layer 2 error:\", String(e));\n }\n }\n\n // LAYER 3: Nano Banana 2 Lite (draft)\n if (geminiKey && quality === \"draft\") {\n try {\n const url = \"https://generativelanguage.googleapis.com/v1beta/models/\" +\n NANO_BANANA_2_LITE + \":generateContent?key=\" +\n encodeURIComponent(geminiKey);\n const r = await fr(url, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n contents: [{ parts: [{ text: enhanced }] }],\n generationConfig: { responseModalities: [\"IMAGE\", \"TEXT\"] },\n }),\n }, 0);\n if (r.ok) {\n const d = await r.json();\n const parts = d?.candidates?.[0]?.content?.parts || [];\n for (const part of parts) {\n if (part.inlineData && part.inlineData.data) {\n console.log(\"[IMG] Layer 3 (Nano Banana 2 Lite) OK\");\n return {\n p: \"🍌 ננו-בננה Lite\",\n pk,\n a: \"![תמונה](data:\" + (part.inlineData.mimeType || \"image/png\") +\n \";base64,\" + part.inlineData.data + \")\",\n ms: 0,\n };\n }\n }\n }\n console.log(\"[IMG] Layer 3 failed status:\", r.status);\n } catch (e) {\n console.log(\"[IMG] Layer 3 error:\", String(e));\n }\n }\n\n // LAYER 4: Nano Banana classic\n if (geminiKey) {\n try {\n const url = \"https://generativelanguage.googleapis.com/v1beta/models/\" +\n NANO_BANANA + \":generateContent?key=\" + encodeURIComponent(geminiKey);\n const r = await fr(url, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n contents: [{ parts: [{ text: enhanced }] }],\n generationConfig: { responseModalities: [\"IMAGE\", \"TEXT\"] },\n }),\n }, 0);\n if (r.ok) {\n const d = await r.json();\n const parts = d?.candidates?.[0]?.content?.parts || [];\n for (const part of parts) {\n if (part.inlineData && part.inlineData.data) {\n console.log(\"[IMG] Layer 4 (Nano Banana Classic) OK\");\n return {\n p: \"🍌 ננו-בננה\",\n pk,\n a: \"![תמונה](data:\" + (part.inlineData.mimeType || \"image/png\") +\n \";base64,\" + part.inlineData.data + \")\",\n ms: 0,\n };\n }\n }\n }\n console.log(\"[IMG] Layer 4 failed status:\", r.status);\n } catch (e) {\n console.log(\"[IMG] Layer 4 error:\", String(e));\n }\n }\n\n // LAYER 5: Pollinations API\n if (pollinationsKey) {\n try {\n await fetchPollinationsCatalog();\n let desiredModel = \"black-forest-labs/flux.2-pro\";\n if (quality === \"ultra\") desiredModel = \"google/gemini-3-pro-image\";\n else if (quality === \"draft\") {\n desiredModel = \"black-forest-labs/flux.1-schnell\";\n }\n const canonical = resolveModelId(desiredModel);\n const meta = getModelMeta(canonical);\n const r = await fetch(POLLINATIONS_BASE + \"/v1/images/generations\", {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n Authorization: \"Bearer \" + pollinationsKey,\n },\n body: JSON.stringify({\n model: canonical,\n prompt: enhanced,\n width,\n height,\n n: 1,\n response_format: \"url\",\n }),\n });\n if (r.ok) {\n const d = await r.json();\n const imgUrl = d?.data?.[0]?.url ||\n (d?.data?.[0]?.b64_json\n ? `data:image/png;base64,${d.data[0].b64_json}`\n : \"\");\n if (imgUrl) {\n console.log(\"[IMG] Layer 5 (Pollinations API) OK\");\n return {\n p: `🎨 ${meta.publisher}`,\n pk,\n a: `![תמונה](${imgUrl})`,\n ms: 0,\n };\n }\n }\n console.log(\"[IMG] Layer 5 failed status:\", r.status);\n } catch (e) {\n console.log(\"[IMG] Layer 5 error:\", String(e));\n }\n }\n\n // LAYER 6: Lumenfall FLUX\n if (lumenfallKey) {\n try {\n const lumenModel = LUMENFALL_MODELS[quality] || LUMENFALL_MODELS.hd;\n let size = \"1024x1024\";\n if (quality === \"draft\") size = \"512x512\";\n else if (quality === \"ultra\") size = \"1536x1536\";\n const r = await fr(LUMENFALL_BASE, {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n Authorization: \"Bearer \" + lumenfallKey,\n },\n body: JSON.stringify({\n model: lumenModel,\n prompt: enhanced,\n size,\n n: 1,\n response_format: \"url\",\n }),\n }, 0);\n if (r.ok) {\n const d = await r.json();\n const imgUrl = d?.data?.[0]?.url || \"\";\n if (imgUrl) {\n console.log(\"[IMG] Layer 6 (Lumenfall) OK\");\n return {\n p: `⚡ Lumenfall ${lumenModel}`,\n pk,\n a: `![תמונה](${imgUrl})`,\n ms: 0,\n };\n }\n }\n console.log(\"[IMG] Layer 6 failed status:\", r.status);\n } catch (e) {\n console.log(\"[IMG] Layer 6 error:\", String(e));\n }\n }\n\n // LAYER 7: HuggingFace FLUX\n if (huggingfaceKey) {\n try {\n const hfModel = HF_MODELS[quality] || HF_MODELS.hd;\n const url = HUGGINGFACE_BASE + \"/\" + hfModel;\n const r = await fr(url, {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n Authorization: \"Bearer \" + huggingfaceKey,\n },\n body: JSON.stringify({\n inputs: enhanced,\n parameters: { width, height },\n }),\n }, 0);\n if (r.ok) {\n const buf = await r.arrayBuffer();\n if (buf.byteLength > 5000) {\n const bytes = new Uint8Array(buf);\n let binary = \"\";\n const chunk = 8192;\n for (let i = 0; i < bytes.length; i += chunk) {\n binary += String.fromCharCode.apply(\n null,\n Array.from(bytes.subarray(i, i + chunk)) as any,\n );\n }\n console.log(\"[IMG] Layer 7 (HuggingFace) OK\");\n return {\n p: `🤗 HuggingFace ${hfModel.split(\"/\").pop()}`,\n pk,\n a: \"![תמונה](data:image/png;base64,\" + btoa(binary) + \")\",\n ms: 0,\n };\n }\n }\n console.log(\"[IMG] Layer 7 failed status:\", r.status);\n } catch (e) {\n console.log(\"[IMG] Layer 7 error:\", String(e));\n }\n }\n\n // LAYER 8: Cloudflare FLUX\n if (acc && tok) {\n try {\n const url = \"https://api.cloudflare.com/client/v4/accounts/\" +\n encodeURIComponent(acc) + \"/ai/run/\" + CF_IMAGE_MODEL;\n const r = await fr(url, {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n Authorization: \"Bearer \" + tok,\n },\n body: JSON.stringify({\n prompt: enhanced,\n num_steps: steps,\n guidance: 7.5,\n }),\n }, 0);\n if (r.ok) {\n const buf = await r.arrayBuffer();\n if (buf.byteLength > 5000) {\n const bytes = new Uint8Array(buf);\n let binary = \"\";\n const chunk = 8192;\n for (let i = 0; i < bytes.length; i += chunk) {\n binary += String.fromCharCode.apply(\n null,\n Array.from(bytes.subarray(i, i + chunk)) as any,\n );\n }\n console.log(\"[IMG] Layer 8 (Cloudflare) OK\");\n return {\n p: \"☁️ Cloudflare FLUX\",\n pk,\n a: \"![תמונה](data:image/png;base64,\" + btoa(binary) + \")\",\n ms: 0,\n };\n }\n }\n console.log(\"[IMG] Layer 8 failed status:\", r.status);\n } catch (e) {\n console.log(\"[IMG] Layer 8 error:\", String(e));\n }\n }\n\n // LAYER 9: URL fallback\n console.log(\"[IMG] Layer 9 (URL fallback) - all layers failed\");\n const proUrl = \"https://image.pollinations.ai/prompt/\" + enc + \"?width=\" +\n width + \"&height=\" + height +\n \"&model=flux-pro&enhance=true&nologo=true&seed=\" + seed;\n return {\n p: \"🔗 Pollinations URL\",\n pk,\n a: \"![תמונה](\" + proUrl + \")\",\n e: \"רק fallback עבד - בדוק מפתחות\",\n };\n}\n\n// ============================================\n// PROVIDERS\n// ============================================\nasync function askGemini(\n prompt: string,\n k: string,\n model: string = MODELS.gemini,\n): Promise {\n const pk = \"gemini\", p = PL[pk];\n if (!k) return { p, pk, a: \"\", e: \"לא מוגדר\" };\n const s = Date.now();\n return wt(\n (async () => {\n try {\n const url = \"https://generativelanguage.googleapis.com/v1beta/models/\" +\n model + \":generateContent?key=\" + encodeURIComponent(k);\n const r = await fr(url, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n systemInstruction: { parts: [{ text: ANON }] },\n contents: [{ parts: [{ text: prompt }] }],\n generationConfig: { temperature: 0.7, maxOutputTokens: MAX_TOKENS },\n }),\n });\n if (!r.ok) {\n await dbHealth(pk, false);\n return { p, pk, a: \"\", e: \"שגיאה \" + r.status, ms: Date.now() - s };\n }\n const d = await r.json();\n const a = d?.candidates?.[0]?.content?.parts?.[0]?.text?.trim() || \"\";\n if (!a) {\n await dbHealth(pk, false);\n return { p, pk, a: \"\", e: \"ריק\", ms: Date.now() - s };\n }\n await dbHealth(pk, true);\n return { p, pk, a, ms: Date.now() - s, ti: et(prompt), to: et(a) };\n } catch (e) {\n await dbHealth(pk, false);\n return { p, pk, a: \"\", e: String(e).slice(0, 120), ms: Date.now() - s };\n }\n })(),\n P_MS,\n { p, pk, a: \"\", e: \"פסק זמן\", ms: 0 },\n );\n}\nasync function askCompat(\n prompt: string,\n k: string,\n pk: string,\n endpoint: string,\n model: string,\n): Promise {\n const p = PL[pk];\n if (!k) return { p, pk, a: \"\", e: \"לא מוגדר\" };\n const s = Date.now();\n return wt(\n (async () => {\n try {\n const h: any = {\n \"Content-Type\": \"application/json\",\n Authorization: \"Bearer \" + k,\n };\n if (pk === \"openrouter\") {\n h[\"HTTP-Referer\"] = \"https://multi-ai-chat.com\";\n h[\"X-Title\"] = \"Multi-AI\";\n }\n const r = await fr(endpoint, {\n method: \"POST\",\n headers: h,\n body: JSON.stringify({\n model,\n messages: [{ role: \"system\", content: ANON }, {\n role: \"user\",\n content: prompt,\n }],\n temperature: 0.7,\n max_tokens: MAX_TOKENS,\n }),\n });\n if (!r.ok) {\n await dbHealth(pk, false);\n return { p, pk, a: \"\", e: \"שגיאה \" + r.status, ms: Date.now() - s };\n }\n const d = await r.json();\n const a = d?.choices?.[0]?.message?.content?.trim() || \"\";\n if (!a) {\n await dbHealth(pk, false);\n return { p, pk, a: \"\", e: \"ריק\", ms: Date.now() - s };\n }\n await dbHealth(pk, true);\n return { p, pk, a, ms: Date.now() - s, ti: et(prompt), to: et(a) };\n } catch (e) {\n await dbHealth(pk, false);\n return { p, pk, a: \"\", e: String(e).slice(0, 120), ms: Date.now() - s };\n }\n })(),\n P_MS,\n { p, pk, a: \"\", e: \"פסק זמן\", ms: 0 },\n );\n}\nlet groqIdx = 0;\nasync function askGroqDual(\n prompt: string,\n k1: string,\n k2: string,\n): Promise {\n const pk = \"groq\", p = PL[pk];\n const keys = [k1, k2].filter(Boolean);\n if (keys.length === 0) return { p, pk, a: \"\", e: \"לא מוגדר\" };\n const eps = \"https://api.groq.com/openai/v1/chat/completions\";\n const errors: string[] = [];\n for (let attempt = 0; attempt < keys.length; attempt++) {\n const idx = (groqIdx + attempt) % keys.length;\n const r = await askCompat(prompt, keys[idx], pk, eps, MODELS[pk]);\n if (r.a && !r.e) {\n groqIdx = (idx + 1) % keys.length;\n if (idx !== 0) r.p = PL[pk] + \" (key\" + (idx + 1) + \")\";\n return r;\n }\n errors.push(\"key\" + (idx + 1) + \": \" + (r.e || \"unknown\"));\n if (!(r.e && (r.e.includes(\"429\") || r.e.includes(\"402\")))) return r;\n }\n groqIdx = (groqIdx + 1) % keys.length;\n return { p, pk, a: \"\", e: \"כל המפתחות נכשלו → \" + errors.join(\" | \") };\n}\nasync function askProvider(\n prompt: string,\n keys: any,\n pk: string,\n): Promise {\n if (pk === \"gemini\") return askGemini(prompt, keys.gemini);\n if (pk === \"groq\") return askGroqDual(prompt, keys.groq, keys.groq2);\n const eps: Record = {\n cerebras: \"https://api.cerebras.ai/v1/chat/completions\",\n nvidia: \"https://integrate.api.nvidia.com/v1/chat/completions\",\n mistral: \"https://api.mistral.ai/v1/chat/completions\",\n openrouter: \"https://openrouter.ai/api/v1/chat/completions\",\n cohere: \"https://api.cohere.ai/compatibility/v1/chat/completions\",\n zai: \"https://open.bigmodel.cn/api/paas/v4/chat/completions\",\n dahl: \"https://inference.dahl.global/v1/chat/completions\",\n };\n return askCompat(prompt, keys[pk], pk, eps[pk], MODELS[pk]);\n}\nasync function cfCall(\n messages: any[],\n acc: string,\n tok: string,\n): Promise {\n try {\n const url = \"https://api.cloudflare.com/client/v4/accounts/\" +\n encodeURIComponent(acc) + \"/ai/run/\" + CF_MODEL;\n const r = await fr(url, {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n Authorization: \"Bearer \" + tok,\n },\n body: JSON.stringify({ messages, max_tokens: MAX_TOKENS }),\n });\n if (!r.ok) return \"\";\n const d = await r.json();\n return String(d?.result?.response || \"\").trim();\n } catch {\n return \"\";\n }\n}\n\n// ============================================\n// JUDGE\n// ============================================\nasync function runJudge(\n jp: string,\n acc: string,\n tok: string,\n geminiKey: string,\n): Promise {\n if (geminiKey) {\n const g = await askGemini(jp, geminiKey, JUDGE_MODEL);\n if (g.a && !g.e) return g.a;\n }\n const cf = await wt(\n cfCall(\n [{ role: \"system\", content: JSP }, { role: \"user\", content: jp }],\n acc,\n tok,\n ),\n J_MS,\n \"\",\n );\n if (cf) return cf;\n if (geminiKey) {\n const g2 = await askGemini(jp, geminiKey, MODELS.gemini);\n if (g2.a) return g2.a;\n }\n return \"\";\n}\nfunction buildJP(\n q: string,\n answers: AIR[],\n hist: Msg[],\n searchCtx: string,\n): string {\n const valid = answers.filter((a) =>\n a.a && a.a.length > 20 && !a.a.includes(\"המערכת פותחה על ידי מוטי כץ\")\n );\n const fb = valid.length === 0\n ? answers.filter((a) => a.a && a.a.length > 0)\n : valid;\n const txt = fb.map((a) => \"--- DRAFT ---\\n\" + tr(a.a, MAX_JA)).join(\"\\n\\n\");\n const histTxt = hist.length\n ? \"History:\\n\" + hist.map((m) =>\n (m.role === \"user\" ? \"User: \" : \"Assistant: \") + tr(m.content, 500)\n ).join(\"\\n\") + \"\\n\\n\"\n : \"\";\n const searchBlock = searchCtx\n ? \"\\n\\nWEB SEARCH (FRESH \" + new Date().toLocaleDateString(\"he-IL\") +\n \"):\\n\" + tr(searchCtx, 6000) + \"\\n\"\n : \"\";\n return JSP + \"\\n\\n\" + histTxt + \"User Question: \" + q + \"\\n\" + searchBlock +\n \"\\nDrafts:\\n\" + txt + \"\\n\\nFinal answer:\";\n}\nfunction parseJP(out: string) {\n const s = String(out || \"\");\n const a = s.match(/ANSWER:\\s*([\\s\\S]*)/i);\n return { answer: a ? a[1].trim() : s.trim() };\n}\n\n// ============================================\n// MAIN HANDLER\n// ============================================\nexport default async function (req: Request): Promise {\n const url = new URL(req.url);\n const GEMINI_API_KEY = Deno.env.get(\"GEMINI_API_KEY\") || \"\";\n const GROQ_API_KEY = Deno.env.get(\"GROQ_API_KEY\") || \"\";\n const GROQ_API_KEY_2 = Deno.env.get(\"GROQ_API_KEY_2\") || \"\";\n const CEREBRAS_API_KEY = Deno.env.get(\"CEREBRAS_API_KEY\") || \"\";\n const NVIDIA_API_KEY = Deno.env.get(\"NVIDIA_API_KEY\") || \"\";\n const MISTRAL_API_KEY = Deno.env.get(\"MISTRAL_API_KEY\") || \"\";\n const OPENROUTER_API_KEY = Deno.env.get(\"OPENROUTER_API_KEY\") || \"\";\n const COHERE_API_KEY = Deno.env.get(\"COHERE_API_KEY\") || \"\";\n const ZAI_API_KEY = Deno.env.get(\"ZAI_API_KEY\") || \"\";\n const DAHL_API_KEY = Deno.env.get(\"DAHL_API_KEY\") || \"\";\n const CLOUDFLARE_ACCOUNT_ID = Deno.env.get(\"CLOUDFLARE_ACCOUNT_ID\") || \"\";\n const CLOUDFLARE_API_TOKEN = Deno.env.get(\"CLOUDFLARE_API_TOKEN\") || \"\";\n const TAVILY_API_KEY = Deno.env.get(\"TAVILY_API_KEY\") || \"\";\n const POLLINATIONS_API_KEY = Deno.env.get(\"POLLINATIONS_API_KEY\") ||\n Deno.env.get(\"POLLINATIONS_KEY\") || \"\";\n const LUMENFALL_API_KEY = Deno.env.get(\"LUMENFALL_API_KEY\") || \"\";\n const HUGGINGFACE_API_KEY = Deno.env.get(\"HUGGINGFACE_API_KEY\") || \"\";\n const APP_SECRET = Deno.env.get(\"APP_SECRET\") || \"\";\n await dbInit();\n\n // === DELETE ===\n if (req.method === \"DELETE\" && url.searchParams.get(\"action\") === \"delete\") {\n const ok = await dbDel(url.searchParams.get(\"c\") || \"\");\n return new Response(JSON.stringify({ ok }), {\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n // === RENAME ===\n if (req.method === \"POST\" && url.searchParams.get(\"action\") === \"rename\") {\n try {\n const fd = await req.formData();\n const ok = await dbRen(\n String(fd.get(\"id\") || \"\"),\n String(fd.get(\"title\") || \"\"),\n );\n return new Response(JSON.stringify({ ok }), {\n headers: { \"Content-Type\": \"application/json\" },\n });\n } catch {\n return new Response(JSON.stringify({ ok: false }), {\n status: 400,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n }\n // === EXPORT FULL ===\n if (\n req.method === \"GET\" && url.searchParams.get(\"action\") === \"export_full\"\n ) {\n const cid = url.searchParams.get(\"c\") || \"\";\n if (!cid) return new Response(\"חסר c\", { status: 400 });\n const full = await dbFull(cid);\n const raws = await dbGetRaw(cid);\n let md = \"# שיחה מלאה - v34 FINAL\\n\\n---\\n\\n\";\n const questions = full.filter((m) => m.role === \"user\");\n for (let i = 0; i < questions.length; i++) {\n const q = questions[i];\n md += \"## 👤 שאלה \" + (i + 1) + \"\\n\\n\" + q.content.slice(0, 50000) +\n \"\\n\\n\";\n const relatedRaw = raws.filter((r) => r.question === q.content);\n for (const r of relatedRaw) {\n md += \"### \" + r.label + \" (\" + r.provider + \")\\n\\n\" +\n r.answer.slice(0, 50000) + \"\\n\\n\";\n }\n const nextUserIdx = full.findIndex((m, idx) =>\n idx > full.indexOf(q) && m.role === \"user\"\n );\n const finalAns = full.slice(\n full.indexOf(q) + 1,\n nextUserIdx === -1 ? undefined : nextUserIdx,\n ).find((m) => m.role === \"assistant\");\n if (finalAns) {\n md += \"### 🤖 תשובה סופית\\n\\n\" + finalAns.content.slice(0, 50000) +\n \"\\n\\n---\\n\\n\";\n }\n }\n return new Response(md, {\n headers: {\n \"Content-Type\": \"text/markdown; charset=utf-8\",\n \"Content-Disposition\": \"attachment; filename=chat-full-\" + Date.now() +\n \".md\",\n },\n });\n }\n // === EXPORT JSON ===\n if (\n req.method === \"GET\" && url.searchParams.get(\"action\") === \"export_json\"\n ) {\n const cid = url.searchParams.get(\"c\") || \"\";\n if (!cid) return new Response(\"חסר c\", { status: 400 });\n const full = await dbFull(cid);\n const raws = await dbGetRaw(cid);\n return new Response(\n JSON.stringify(\n {\n conversation_id: cid,\n messages: full,\n raw_answers: raws,\n exported_at: Date.now(),\n version: \"v34\",\n },\n null,\n 2,\n ),\n {\n headers: {\n \"Content-Type\": \"application/json; charset=utf-8\",\n \"Content-Disposition\": \"attachment; filename=chat-\" + Date.now() +\n \".json\",\n },\n },\n );\n }\n // === POLLINATIONS MODELS ===\n if (\n req.method === \"GET\" &&\n url.searchParams.get(\"action\") === \"pollinations_models\"\n ) {\n const catalog = await fetchPollinationsCatalog();\n return new Response(JSON.stringify(catalog), {\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n\n // === POST ===\n if (req.method === \"POST\") {\n let q = \"\", cid = \"\", prov = \"\", fileText = \"\";\n let forceSearchParam = \"auto\", forceRecalcParam = false;\n let imgStyle = \"photorealistic\", imgQuality = \"hd\";\n try {\n const fd = await req.formData();\n q = String(fd.get(\"q\") || \"\").trim();\n cid = String(fd.get(\"c\") || \"\");\n prov = String(fd.get(\"p\") || \"\");\n forceSearchParam = String(fd.get(\"forceSearch\") || \"auto\");\n forceRecalcParam = String(fd.get(\"forceRecalc\") || \"\") === \"true\";\n imgStyle = String(fd.get(\"imgStyle\") || \"photorealistic\");\n imgQuality = String(fd.get(\"imgQuality\") || \"hd\");\n for (const [key, val] of fd.entries()) {\n if (key.startsWith(\"file_\") && val instanceof File) {\n try {\n fileText += \"\\n\\n[קובץ: \" + val.name + \"]\\n\" +\n (await val.text()).slice(0, MAX_FILE) + \"\\n\";\n } catch {}\n }\n }\n } catch {\n return new Response(JSON.stringify({ error: \"בקשה לא תקינה\" }), {\n status: 400,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n if (fileText) q = (q + \"\\n\\n--- קבצים ---\\n\" + fileText).slice(0, MAX_Q);\n if (!q) {\n return new Response(JSON.stringify({ error: \"חסרה שאלה\" }), {\n status: 400,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n if (q.length > MAX_Q) {\n return new Response(JSON.stringify({ error: \"ארוכה מדי\" }), {\n status: 400,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n if (!cid) {\n cid = \"conv_\" + Date.now() + \"_\" + Math.random().toString(36).slice(2, 6);\n }\n if (!checkRateLimit(cid)) {\n return new Response(JSON.stringify({ error: \"יותר מדי בקשות\" }), {\n status: 429,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n\n // === IMAGE REQUEST DETECTION ===\n const qNormalized = q.normalize(\"NFC\").trim();\n const trimmedQ = qNormalized.toLowerCase();\n const isImageReq = /^\\/image\\s/i.test(qNormalized) ||\n /^\\/תמונה\\s/i.test(qNormalized) ||\n /^\\/ציור\\s/i.test(qNormalized) ||\n /^(תיצור|צור|תעשה|תייצר|תכין|תאר|תן לי|אני רוצה|אפשר)\\s+(לי\\s+)?(תמונה|ציור|איור)/\n .test(qNormalized) ||\n /^(צייר לי|צייר|תצייר)\\s/.test(qNormalized) ||\n qNormalized.startsWith(\"תמונה של \") ||\n qNormalized.startsWith(\"🎨\") ||\n /^\\[(photorealistic|anime|3d|fast|draft|hd|ultra)\\]/i.test(qNormalized);\n\n if (isImageReq) {\n // === FIX #4: PROMPT CLEANUP מורחב ===\n let rawPrompt = qNormalized\n .replace(/^\\/image\\s*/i, \"\")\n .replace(/^\\/תמונה\\s*/i, \"\")\n .replace(/^\\/ציור\\s*/i, \"\")\n // ניקוי \"תמונה יפה של\" / \"תמונה מדהימה של\" וכו'\n .replace(\n /^תמונה\\s+(יפה|יפה מאוד|מגניבה|מקצועית|מרהיבה|מדהימה|חמודה|מושלמת|איכותית)\\s+של\\s+/i,\n \"\",\n )\n .replace(\n /^תמונה\\s+(יפה|יפה מאוד|מגניבה|מקצועית|מרהיבה|מדהימה|חמודה|מושלמת|איכותית)\\s+/i,\n \"\",\n )\n // ניקוי \"תיצור/אני רוצה תמונה של\"\n .replace(\n /^(תיצור|צור|תעשה|תייצר|תכין|תאר|תן לי|אני רוצה|אפשר)\\s+(לי\\s+)?(תמונה|ציור|איור)(\\s+באמצעות\\s+\\S+)?\\s*/i,\n \"\",\n )\n .replace(/^(צייר לי|צייר|תצייר)\\s+/, \"\")\n .replace(/^תמונה של\\s+/i, \"\")\n // 🔄 FIX #4: ניקוי \"שהתמונה תעשה על ידי X\"\n .replace(\n /\\s+שהתמונה\\s+(תעשה|תיווצר|תיבנה|תוצג|תוצא|תיראה)\\s+על\\s+ידי\\s+\\S+/gi,\n \"\",\n )\n // 🔄 FIX #4: ניקוי \"באמצעות ננו בננה / flux / DALL-E וכו'\" בסוף\n .replace(\n /\\s+באמצעות\\s+(ננו[\\s-]?בננה|nano[\\s-]?banana|flux|dall[\\s-]?e|midjourney|gemini|ה-?AI|AI)/gi,\n \"\",\n )\n // 🔄 FIX #4: ניקוי \"על ידי ננו בננה וכו'\"\n .replace(\n /\\s+על\\s+ידי\\s+(ננו[\\s-]?בננה|nano[\\s-]?banana|flux|dall[\\s-]?e|midjourney|gemini)/gi,\n \"\",\n )\n .replace(/🎨/g, \"\")\n .trim();\n const allBrackets = [...rawPrompt.matchAll(/\\[([a-z0-9-]+)\\]/gi)].map((\n m,\n ) => m[1].toLowerCase());\n let styleFromQ = imgStyle, qualityFromQ = imgQuality;\n for (const b of allBrackets) {\n if ([\"photorealistic\", \"anime\", \"3d\", \"fast\"].includes(b)) {\n styleFromQ = b;\n }\n if ([\"draft\", \"hd\", \"ultra\"].includes(b)) qualityFromQ = b;\n }\n rawPrompt = rawPrompt.replace(/\\[.*?\\]/g, \"\").trim();\n if (!rawPrompt) rawPrompt = \"beautiful landscape\";\n console.log(\"[IMG] Hebrew prompt:\", rawPrompt);\n const enPrompt = await translateToEnglishSafe(rawPrompt, GEMINI_API_KEY);\n console.log(\"[IMG] English prompt:\", enPrompt);\n const imgRes = await genImage(\n enPrompt,\n CLOUDFLARE_ACCOUNT_ID,\n CLOUDFLARE_API_TOKEN,\n styleFromQ,\n qualityFromQ,\n GEMINI_API_KEY,\n POLLINATIONS_API_KEY,\n LUMENFALL_API_KEY,\n HUGGINGFACE_API_KEY,\n );\n await dbSave(cid, \"user\", q);\n await dbSave(cid, \"assistant\", imgRes.a);\n const t = await generateTitle(q, GEMINI_API_KEY);\n await dbMeta(cid, t);\n return new Response(\n JSON.stringify({\n answer: imgRes.a,\n raw: [imgRes],\n memory: 0,\n search: false,\n direct: true,\n totalTokens: 0,\n conversationId: cid,\n }),\n { headers: { \"Content-Type\": \"application/json; charset=utf-8\" } },\n );\n }\n\n // === CHAT FLOW ===\n const qh = qHash(q);\n const skipCache = isNewsQuery(q) || forceSearchParam === \"true\" ||\n forceRecalcParam;\n\n if (!skipCache) {\n const cached = await getCache(qh);\n if (cached) {\n await dbSave(cid, \"user\", q);\n await dbSave(cid, \"assistant\", cached.answer);\n const t = await generateTitle(q, GEMINI_API_KEY);\n await dbMeta(cid, t);\n return new Response(\n JSON.stringify({\n answer: cached.answer,\n raw: cached.raw,\n memory: 0,\n search: false,\n direct: false,\n totalTokens: 0,\n conversationId: cid,\n cached: true,\n }),\n { headers: { \"Content-Type\": \"application/json; charset=utf-8\" } },\n );\n }\n }\n\n let enabled = prov\n ? prov.split(\",\").map((s) => s.trim()).filter((s) => ALL.includes(s))\n : ALL.slice();\n const keys: any = {\n gemini: GEMINI_API_KEY,\n groq: GROQ_API_KEY,\n groq2: GROQ_API_KEY_2,\n cerebras: CEREBRAS_API_KEY,\n nvidia: NVIDIA_API_KEY,\n mistral: MISTRAL_API_KEY,\n openrouter: OPENROUTER_API_KEY,\n cohere: COHERE_API_KEY,\n zai: ZAI_API_KEY,\n dahl: DAHL_API_KEY,\n };\n enabled = enabled.filter((p) =>\n keys[p] || (p === \"groq\" && (keys.groq || keys.groq2))\n );\n if (enabled.length === 0) {\n return new Response(JSON.stringify({ error: \"אין מנועים\" }), {\n status: 400,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n\n // === STREAMING RESPONSE ===\n const stream = new ReadableStream({\n async start(controller) {\n const enc = new TextEncoder();\n const send = (obj: any) => {\n try {\n controller.enqueue(enc.encode(JSON.stringify(obj) + \"\\n\"));\n } catch {}\n };\n\n try {\n send({\n type: \"start\",\n total: enabled.length,\n providers: enabled.map((pk) => ({ pk, label: PL[pk] })),\n });\n\n const hist = await dbHist(cid);\n let shouldSearch = false;\n if (TAVILY_API_KEY && !isMetaQuestion(q)) {\n if (forceSearchParam === \"true\") shouldSearch = true;\n else if (forceSearchParam === \"false\") {\n shouldSearch = isNewsQuery(q);\n } else shouldSearch = isNewsQuery(q) || q.length > 10;\n }\n\n if (shouldSearch) {\n send({ type: \"status\", message: \"🔍 מחפש באינטרנט...\" });\n }\n\n const searchCtx = shouldSearch\n ? await wt(webSearch(q, TAVILY_API_KEY), 7000, \"\")\n : \"\";\n const fullPrompt = ctx(hist, q) +\n (searchCtx ? \"\\n\\nWeb search results:\\n\" + searchCtx : \"\");\n\n const round1 = await Promise.all(enabled.map(async (pk) => {\n const r = await askProvider(fullPrompt, keys, pk);\n send({\n type: \"done\",\n pk,\n label: r.p,\n ok: !!(r.a && !r.e),\n ms: r.ms || 0,\n e: r.e || \"\",\n preview: r.a ? r.a.slice(0, 100) : \"\",\n });\n return r;\n }));\n\n const valid = round1.filter((a) =>\n a.a && a.a.length > 20 &&\n !a.a.includes(\"המערכת פותחה על ידי מוטי כץ\")\n );\n const fb = valid.length > 0\n ? valid\n : round1.filter((a) => a.a && a.a.length > 0);\n\n let finalAnswer = \"\";\n\n if (fb.length === 0) {\n send({\n type: \"status\",\n message: \"⚠ כל המנועים נכשלו, מנסה שופט בלבד...\",\n });\n if (searchCtx) {\n const jpF = buildJP(q, [], hist, searchCtx);\n const jrF = await runJudge(\n jpF,\n CLOUDFLARE_ACCOUNT_ID,\n CLOUDFLARE_API_TOKEN,\n GEMINI_API_KEY,\n );\n const parsed = parseJP(jrF || \"\");\n if (validateAnswer(parsed.answer)) {\n finalAnswer = parsed.answer;\n } else {\n finalAnswer =\n \"מצטער, לא הצלחתי להפיק תשובה מהמנועים. נסה שוב בעוד רגע.\";\n }\n } else {\n finalAnswer = \"כל המנועים נכשלו כרגע. נסה שוב בעוד רגע.\";\n }\n } else if (fb.length === 1) {\n send({\n type: \"status\",\n message: \"🎯 מנוע אחד הצליח, משתמש בתשובתו...\",\n });\n finalAnswer = fb[0].a;\n } else {\n send({ type: \"judging\", count: fb.length, total: round1.length });\n const jp = buildJP(q, round1, hist, searchCtx);\n const jr = await runJudge(\n jp,\n CLOUDFLARE_ACCOUNT_ID,\n CLOUDFLARE_API_TOKEN,\n GEMINI_API_KEY,\n );\n const parsed = parseJP(jr || \"\");\n if (validateAnswer(parsed.answer)) {\n finalAnswer = parsed.answer;\n } else {\n finalAnswer = fb[0].a;\n }\n }\n\n await dbSave(cid, \"user\", q);\n await dbSaveRaw(cid, q, round1.filter((r) => r.a));\n await dbSave(cid, \"assistant\", finalAnswer);\n const title = await generateTitle(q, GEMINI_API_KEY);\n await dbMeta(cid, title);\n if (!isNewsQuery(q)) await setCache(qh, q, finalAnswer, round1);\n const totalTokens = round1.reduce(\n (s, a) => s + (a.ti || 0) + (a.to || 0),\n 0,\n );\n\n send({\n type: \"final\",\n answer: finalAnswer,\n raw: round1,\n memory: hist.length,\n search: !!searchCtx,\n searchContent: searchCtx,\n direct: fb.length === 1,\n totalTokens,\n conversationId: cid,\n cached: false,\n });\n } catch (e) {\n send({ type: \"error\", message: String(e).slice(0, 200) });\n } finally {\n try {\n controller.close();\n } catch {}\n }\n },\n });\n\n return new Response(stream, {\n headers: {\n \"Content-Type\": \"application/x-ndjson; charset=utf-8\",\n \"Cache-Control\": \"no-store\",\n \"X-Accel-Buffering\": \"no\",\n },\n });\n }\n\n // === GET (Page) ===\n const cid = url.searchParams.get(\"c\") || \"\";\n const convs = await dbList();\n const msgs = cid ? await dbFull(cid) : [];\n const health = await dbGetHealth();\n const avail: string[] = [];\n if (GEMINI_API_KEY) avail.push(\"gemini\");\n if (GROQ_API_KEY || GROQ_API_KEY_2) avail.push(\"groq\");\n if (CEREBRAS_API_KEY) avail.push(\"cerebras\");\n if (NVIDIA_API_KEY) avail.push(\"nvidia\");\n if (MISTRAL_API_KEY) avail.push(\"mistral\");\n if (OPENROUTER_API_KEY) avail.push(\"openrouter\");\n if (COHERE_API_KEY) avail.push(\"cohere\");\n if (ZAI_API_KEY) avail.push(\"zai\");\n if (DAHL_API_KEY) avail.push(\"dahl\");\n return renderPage(cid, convs, msgs, avail, health);\n}\n\n// === END OF FILE 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Diff · v117