import json import logging import random import os import google.generativeai as genai import config logger = logging.getLogger(__name__) genai.configure(api_key=config.GEMINI_API_KEY) # Relax Gemini safety filters SAFETY_SETTINGS = [ {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE"}, {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE"}, {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_NONE"}, {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_NONE"}, ] # Load prompts from .ilang files (prompts/ if exists, else prompts_demo/) def _load_prompt(name): for d in ("prompts", "prompts_demo"): path = os.path.join(os.path.dirname(os.path.dirname(__file__)), d, name) if os.path.exists(path): with open(path, "r", encoding="utf-8") as f: content = f.read() logger.info("Loaded prompt: " + path + " (" + str(len(content)) + " chars)") return content logger.warning("Prompt not found: " + name) return "" SYSTEM_PROMPT = _load_prompt("persona.ilang") ANTISPAM_TEXT_PROMPT = _load_prompt("antispam.ilang") VISION_PROMPT = _load_prompt("vision.ilang") if not SYSTEM_PROMPT: SYSTEM_PROMPT = "You are TelegramGuard, a helpful AI assistant on Telegram. Reply concisely in the user's language. JSON format: {\"intent\": \"chat\", \"device\": null, \"reply\": \"your text\"}" logger.warning("Using fallback system prompt") GROUP_WELCOME = ( "I-Lang Guard is here\n\n" "I auto-clean spam. No config needed.\n" "@ me if you need anything.\n\n" "Just give me admin permissions (delete messages + ban users)." ) model = genai.GenerativeModel( config.GEMINI_MODEL, system_instruction=SYSTEM_PROMPT, safety_settings=SAFETY_SETTINGS ) vision_model = genai.GenerativeModel( config.GEMINI_MODEL, safety_settings=SAFETY_SETTINGS ) def _parse(raw): if not raw: return ("chat", None, "...") t = raw.strip() if t.startswith("```"): nl = t.find("\n") t = t[nl + 1:] if nl > 0 else t[3:] if t.endswith("```"): t = t[:-3].strip() try: d = json.loads(t) return (d.get("intent", "chat"), d.get("device"), d.get("reply", t)) except json.JSONDecodeError: pass last_brace = t.rfind("}") while last_brace >= 0: start = t.rfind("{", 0, last_brace) if start >= 0: try: d = json.loads(t[start:last_brace + 1]) return (d.get("intent", "chat"), d.get("device"), d.get("reply", t)) except json.JSONDecodeError: pass last_brace = t.rfind("}", 0, last_brace) for line in t.split("\n"): line = line.strip() if line and not line.startswith("{") and not line.startswith("taint") and not line.startswith("The "): return ("chat", None, line) return ("chat", None, "...") def _ctx(history, info): parts = [] if info: parts.append("[ctx] " + info) if history: for h in history[-8:]: r = "user" if h["role"] == "user" else "bot" parts.append(r + ": " + h["text"]) return "\n".join(parts) def _safe_text(response): """Safely extract text from Gemini response — r.text throws ValueError when blocked.""" try: if response.text: return response.text.strip() except (ValueError, AttributeError): pass # Try extracting from candidates try: if response.candidates: for c in response.candidates: if hasattr(c, 'content') and c.content and c.content.parts: return c.content.parts[0].text.strip() except Exception: pass return "" def _deflect(): lines = [ "That's a tough one. What else can I help with?", "Let's try a different angle. What do you need today?", "That's beyond my range. What else is on your mind?", ] return random.choice(lines) async def ai_text(text, history=None, context_info=""): try: c = _ctx(history, context_info) prompt = c + "\nuser: " + text if c else "user: " + text r = await model.generate_content_async(prompt, safety_settings=SAFETY_SETTINGS) raw = _safe_text(r) if not raw: feedback = "" if hasattr(r, 'prompt_feedback'): feedback = str(r.prompt_feedback) if hasattr(r, 'candidates') and r.candidates: for cand in r.candidates: if hasattr(cand, 'finish_reason'): feedback += " finish:" + str(cand.finish_reason) if hasattr(cand, 'safety_ratings'): feedback += " safety:" + str(cand.safety_ratings) logger.warning("AI empty response. feedback=" + feedback + " prompt_len=" + str(len(prompt))) return ("chat", None, _deflect()) logger.info("AI raw[" + str(len(raw)) + "]: " + raw[:200]) return _parse(raw) except Exception as e: logger.warning("AI text exception: " + str(e)) return ("chat", None, _deflect()) async def ai_vision(image_bytes, caption="", history=None, context_info=""): try: c = _ctx(history, context_info) prompt = VISION_PROMPT + "\n" + c if caption: prompt += "\nuser: " + caption r = await vision_model.generate_content_async([prompt, {"mime_type": "image/jpeg", "data": image_bytes}], safety_settings=SAFETY_SETTINGS) return _parse(_safe_text(r)) except Exception as e: logger.warning("AI vision: " + str(e)) return ("chat", None, "Couldn't read that image. Try another one?") async def ai_voice(audio_bytes, mime_type="audio/ogg", history=None, context_info=""): try: c = _ctx(history, context_info) prompt = SYSTEM_PROMPT + "\n" + c + "\nUser sent a voice message:" r = await vision_model.generate_content_async([prompt, {"mime_type": mime_type, "data": audio_bytes}], safety_settings=SAFETY_SETTINGS) return _parse(_safe_text(r)) except Exception as e: logger.warning("AI voice: " + str(e)) return ("chat", None, "Didn't catch that. Try again or type it out.") async def ai_judge_group_message(text): try: prompt = ANTISPAM_TEXT_PROMPT + "\n\nMessage content: " + text[:1000] r = await vision_model.generate_content_async(prompt, safety_settings=SAFETY_SETTINGS) result = (_safe_text(r) or "ok").lower() return "spam" in result except Exception: return False async def ai_judge_group_image(image_bytes, caption=""): try: prompt = ANTISPAM_TEXT_PROMPT + "\n\nJudge this image. Reply ONLY: spam or ok." if caption: prompt += "\nCaption: " + caption[:500] r = await vision_model.generate_content_async([prompt, {"mime_type": "image/jpeg", "data": image_bytes}], safety_settings=SAFETY_SETTINGS) result = (_safe_text(r) or "ok").lower() return "spam" in result except Exception: return False async def ai_group_vision(image_bytes, caption="", history=None): try: ctx = _ctx(history, "GROUP_CHAT: User shared an image and @mentioned you. Comment naturally in 1-2 sentences.") prompt = SYSTEM_PROMPT + "\n" + ctx if caption: prompt += "\nuser: " + caption else: prompt += "\nuser: [shared an image]" r = await vision_model.generate_content_async([prompt, {"mime_type": "image/jpeg", "data": image_bytes}], safety_settings=SAFETY_SETTINGS) raw = _safe_text(r) if not raw: return _deflect() intent, device, reply = _parse(raw) if reply in ("...", ""): return _deflect() return reply except Exception: return _deflect() async def ai_group_reply(text, history=None): try: ctx = _ctx(history, "GROUP_CHAT: You were @mentioned in a group. Reply directly, 1-2 sentences.") prompt = ctx + "\nuser: " + text r = await model.generate_content_async(prompt, safety_settings=SAFETY_SETTINGS) raw = _safe_text(r) if not raw: return _deflect() intent, device, reply = _parse(raw) if reply in ("...", ""): return _deflect() return reply except Exception: return _deflect()