"""Prompt yardımcısı — Ollama LLM ile Türkçe→İngilizce çeviri ve prompt iyileştirme.""" import os import requests from typing import Optional # Docker network için OLLAMA_HOST env var ile override edilebilir # Örn: OLLAMA_HOST=http://ollama:11434 (compose service ismi) OLLAMA_URL = os.environ.get("OLLAMA_HOST", "http://localhost:11434").rstrip("/") # qwen3:8b düz yanıt verir (gpt-oss:20b reasoning model — token harcar) DEFAULT_MODEL = os.environ.get("OLLAMA_MODEL", "qwen3:8b") FALLBACK_MODELS = ["llama3.1:8b", "qwen2.5:7b", "mistral:7b", "gpt-oss:20b"] # Büyük modeller (20B) yavaş olabilir — timeout uzun TIMEOUT_LLM = int(os.environ.get("OLLAMA_TIMEOUT", "120")) def ollama_var_mi() -> bool: """Ollama servisi açık mı?""" try: r = requests.get(f"{OLLAMA_URL}/api/tags", timeout=3) return r.status_code == 200 except Exception: return False def en_iyi_model() -> Optional[str]: """Mevcut modellerden en iyiyi seç.""" try: r = requests.get(f"{OLLAMA_URL}/api/tags", timeout=3) if r.status_code != 200: return None modeller = [m["name"] for m in r.json().get("models", [])] for tercih in [DEFAULT_MODEL] + FALLBACK_MODELS: for m in modeller: if tercih in m or m == tercih: return m return modeller[0] if modeller else None except Exception: return None def llm_calistir(prompt: str, sistem: str = "", max_token: int = 500) -> str: """Ollama'da LLM çağrısı yap, sonucu string olarak döner. Türkçe karakterler için requests json= UTF-8 kullanır (varsayılan). Reasoning modelleri (gpt-oss) için /no_think direktifi eklenir.""" model = en_iyi_model() if not model: print("[Prompt LLM] Ollama modeli bulunamadı") return "" # Reasoning modelleri için thinking'i devre dışı bırak is_reasoning = "gpt-oss" in model.lower() or "thinking" in model.lower() if is_reasoning: # Daha çok token ver, reasoning + cevap için max_token = max(max_token, 1500) try: body = { "model": model, "prompt": prompt, "system": sistem, "stream": False, "options": { "num_predict": max_token, "temperature": 0.7, }, } # qwen3 ve diğer reasoning modeller için /think disable if "qwen3" in model.lower() or is_reasoning: body["think"] = False r = requests.post( f"{OLLAMA_URL}/api/generate", json=body, timeout=TIMEOUT_LLM, ) if r.status_code != 200: print(f"[Prompt LLM] HTTP {r.status_code}: {r.text[:200]}") return "" data = r.json() cevap = (data.get("response") or "").strip() # Boş response + thinking varsa, thinking'in son kısmını kullan (fallback) if not cevap and data.get("thinking"): print(f"[Prompt LLM] {model} reasoning'de takıldı, thinking'den çek") think = data.get("thinking", "") # Son satırdaki cümleyi al satirlar = [s.strip() for s in think.split("\n") if s.strip()] if satirlar: cevap = satirlar[-1] return cevap except requests.Timeout: print(f"[Prompt LLM] Timeout ({TIMEOUT_LLM}sn) — daha küçük model dene") return "" except Exception as e: print(f"[Prompt LLM] hata: {e}") return "" def turkce_to_ingilizce(turkce: str) -> str: """Türkçe açıklamayı SDXL için optimize edilmiş İngilizce prompt'a çevir.""" if not turkce.strip(): return "" sistem = ( "You are an expert SDXL prompt engineer. Convert Turkish descriptions " "to high-quality English prompts for image generation. Focus on visual " "details: subject, composition, lighting, mood, style. Use comma-separated " "keywords. Add quality boosters like 'professional photography, " "8k, masterpiece, sharp focus, detailed'. Keep it under 100 words. " "Return ONLY the English prompt, no explanation." ) return llm_calistir(turkce, sistem, max_token=200) def prompt_gelistir(eksik_prompt: str, kategori: str = "genel") -> str: """Kısa/eksik İngilizce prompt'u uzunlat ve detaylandır. kategori: 'karakter', 'mekan', 'urun', 'genel'.""" if not eksik_prompt.strip(): return "" sistem_basla = { "karakter": ( "You are an SDXL prompt engineer specializing in character/portrait " "generation. Enhance this prompt by adding: lighting (soft natural, " "studio, golden hour), facial expression details, pose, clothing " "specifics, background, professional quality boosters. Keep coherent." ), "mekan": ( "You are an SDXL prompt engineer specializing in scene/environment " "generation. Enhance with: lighting, atmosphere, time of day, " "weather, architectural details, mood, perspective, depth, color palette." ), "urun": ( "You are an SDXL prompt engineer specializing in product/commercial " "photography. Enhance with: studio lighting, white/branded background, " "shadows, reflections, material details, clean composition, " "advertising quality." ), "genel": ( "You are an SDXL prompt engineer. Enhance this prompt by adding " "professional details: lighting, composition, quality boosters, " "art direction. Keep it visual and specific." ), } sistem = sistem_basla.get(kategori, sistem_basla["genel"]) + ( " Output ONLY the enhanced English prompt, no explanation. Under 120 words." ) return llm_calistir(eksik_prompt, sistem, max_token=300) def sihirbaz_prompt_olustur(girdi: dict, kategori: str) -> str: """Sihirbaz adımlarından dolu girdiyi profesyonel prompt'a çevir. girdi: {'isim': '...', 'yas': '25', 'sac': 'kahverengi', 'kiyafet': 'kırmızı elbise', ...} kategori: 'karakter' | 'mekan' | 'urun'. """ sistem_basla = { "karakter": ( "Convert this character description (in Turkish/mixed) to a " "professional English SDXL prompt. Order: physical traits, clothing, " "expression, pose, background, lighting, quality. Comma-separated." ), "mekan": ( "Convert this scene description to a professional English SDXL prompt. " "Order: location type, time/atmosphere, lighting, weather, key objects, " "perspective, mood, quality." ), "urun": ( "Convert this product description to a professional English SDXL prompt. " "Order: product type, material/color, setting (studio/lifestyle), " "lighting, composition, advertising quality boosters." ), } sistem = sistem_basla.get(kategori, sistem_basla["karakter"]) + ( " Output ONLY the prompt, comma-separated, under 100 words." ) # Girdiyi düz metin'e çevir girdi_str = "\n".join(f"{k}: {v}" for k, v in girdi.items() if v) return llm_calistir(girdi_str, sistem, max_token=300)