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app.py
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import os
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import re
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import uvicorn
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import pytz
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import httpx
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import io
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import json
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import base64
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import traceback
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from datetime import datetime
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from typing import List, Optional, Dict, Any, AsyncGenerator
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from fastapi import FastAPI, Request
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from fastapi.responses import JSONResponse, StreamingResponse
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from pydantic import BaseModel, Field
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from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
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from PIL import Image
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import torch
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app = FastAPI(title="ONYX Gateway - Syrian Identity")
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GEMMA_URL = "https://rawangassem-onyx-modle.hf.space/predict"
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base_identity = "مساعد ذكي متطور، صُنع ليكون رفيقاً رقمياً وفياً بلمسة سورية."
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# --- تحميل نموذج وصف الصور ---
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v_model_name = "nlpconnect/vit-gpt2-image-captioning"
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v_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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v_model = VisionEncoderDecoderModel.from_pretrained(v_model_name).to(v_device)
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v_feature_extractor = ViTImageProcessor.from_pretrained(v_model_name)
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v_tokenizer = AutoTokenizer.from_pretrained(v_model_name)
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class ChatRequest(BaseModel):
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messages: list
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temperature: float = 0.7
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timezone: str = "Asia/Damascus"
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class StoryCharacter(BaseModel):
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name: str
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gender: Optional[str] = ""
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identity: Optional[str] = ""
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description_private: Optional[str] = ""
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background_public: Optional[str] = ""
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greeting: Optional[str] = ""
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visibility: Optional[str] = ""
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class StoryGenerateRequest(BaseModel):
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story_title: str
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story_description: str = ""
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chapter_title: str
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chapter_number: int = 1
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language: str = "العربية"
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temperature: float = 0.8
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timezone: str = "Asia/Damascus"
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current_chapter_text: Optional[str] = ""
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characters: List[StoryCharacter] = Field(default_factory=list)
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edited_responses: Dict[str, Any] = Field(default_factory=dict)
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stream: bool = True
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# --- Helper Functions (اللي كانت ناقصة) ---
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def build_story_system_prompt(request: StoryGenerateRequest) -> str:
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def _gender_to_ar(g: Optional[str]) -> str:
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raw = (g or "").strip().lower()
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if raw in {"male", "m", "ذكر", "ولد"}: return "ذكر"
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if raw in {"female", "f", "أنثى", "انثى", "بنت"}: return "أنثى"
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return "غير معروف"
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chars_info = ""
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allowed = []
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for c in request.characters:
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name = (c.name or "").strip()
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if not name: continue
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allowed.append(name)
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chars_info += f"- {name} ({_gender_to_ar(c.gender)}): {c.identity or ''}. {c.description_private or ''}\n"
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allowed.append("الراوي")
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allowed_names_str = ", ".join(allowed)
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prompt = (
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"أنت كاتب روايات محترف.\n"
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f"عنوان القصة: {request.story_title}\n"
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"مهمتك: توليد حوار تفاعلي وديناميكي بين الشخصيات.\n\n"
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f"الشخصيات المتاحة (يجب استخدامها في الحوار):\n{chars_info}\n"
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"--- قواعد الإنتاج ---\n"
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"1. يجب أن يتضمن المشهد حواراً لعدة شخصيات (لا تكتفِ بشخصية واحدة).\n"
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"2. اجعل الشخصيات تتفاعل مع بعضها (ردود فعل، مقاطعة، إجابة).\n"
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f"3. الأسماء المسموحة فقط: [{allowed_names_str}].\n"
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"4. التزم بصيغة JSON حصراً.\n\n"
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"{\n"
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' "chapter_text": "وصف تفصيلي للجو العام وحركات الشخصيات"،\n'
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' "character_responses": [\n'
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' {"name": "اسم الشخصية", "response": "نص الحوار"},\n'
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' {"name": "شخصية أخرى", "response": "رد الشخصية الأخرى"}\n'
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' ]\n'
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"}"
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)
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return prompt
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def build_story_user_prompt(request: StoryGenerateRequest) -> str:
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# إضافة سياق القصة الأساسي بالـ User Prompt بقوي النتيجة
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context = f"وصف القصة الأساسي: {request.story_description}\n"
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if request.edited_responses:
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# (منخلي منطق التعديل مثل ما هو بس منقوي التعليمات)
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user_p = f"{context}\nأكمل من بعد التعديل: {request.edited_responses.get('text')}\nاجعل البقية يتفاعلون مع هذا التعديل."
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else:
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user_p = (
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f"{context}\n"
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f"الأحداث السابقة: {request.current_chapter_text}\n"
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"الآن، وبناءً على ما سبق، تابع كتابة المشهد. "
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"اجعل الشخصيات المتاحة تتبادل أطراف الحديث وتتحرك في المكان. "
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"أريد حواراً طويلاً وممتعاً بصيغة JSON."
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)
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return user_p
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def filter_story_characters(parsed: Dict[str, Any], allowed_names: set) -> Dict[str, Any]:
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try:
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if not isinstance(parsed, dict):
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return {"chapter_text": str(parsed), "character_responses": []}
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# تنظيف قائمة الأسماء المسموحة وتحويلها لـ lowercase للمقارنة
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allowed_map = {str(n).strip().lower(): str(n).strip() for n in (allowed_names or set()) if str(n).strip()}
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responses = parsed.get("character_responses", [])
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if not isinstance(responses, list):
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parsed["character_responses"] = []
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return parsed
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filtered = []
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for item in responses:
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if not isinstance(item, dict):
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continue
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name_raw = str(item.get("name", "")).strip()
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name_lower = name_raw.lower()
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# التأكد إذا الاسم موجود بالقائمة بغض النظر عن حالة الأحرف
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if not name_raw or name_lower not in allowed_map:
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continue
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# استخراج النص من أي مفتاح محتمل
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resp_raw = (
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item.get("response")
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or item.get("dialogue")
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or item.get("text")
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or item.get("content")
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)
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resp = str(resp_raw or "").strip()
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if not resp:
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continue
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# نستخدم الاسم الأصلي من allowed_map مشان يضل التنسيق متل ما بدك
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filtered.append({"name": allowed_map[name_lower], "response": resp})
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parsed["character_responses"] = filtered
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parsed["chapter_text"] = parsed.get("chapter_text", "")
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return parsed
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except Exception:
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# في حال حدوث خطأ كارثي، نرجع القاموس بالبيانات الخام بدل ما يضرب السيرفر
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return parsed
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async def stream_engine_text(payload: dict):
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async with httpx.AsyncClient(timeout=120.0) as client:
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async with client.stream("POST", GEMMA_URL, json=payload) as response:
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if response.status_code != 200:
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yield "خطأ في الاتصال بمحرك الذكاء الصناعي."
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return
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async for chunk in response.aiter_text():
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yield chunk
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async def collect_engine_text(payload: dict) -> str:
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full_text = ""
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async for chunk in stream_engine_text(payload):
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full_text += chunk
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return full_text
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def try_extract_json(text: str) -> Dict[str, Any]:
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try:
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# البحث عن أول { وآخر } في حال وجود نص زائد
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match = re.search(r'\{.*\}', text, re.DOTALL)
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if match:
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return json.loads(match.group())
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return json.loads(text)
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except:
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return {"chapter_text": text, "character_responses": []}
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@app.get("/")
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async def root():
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return {"status": "ONYX Gateway running"}
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def predict_image(image_data_base64: str) -> str:
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try:
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img_bytes = base64.decodebytes(image_data_base64.encode())
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image = Image.open(io.BytesIO(img_bytes))
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if image.mode != "RGB":
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image = image.convert("RGB")
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pixel_values = v_feature_extractor(images=[image], return_tensors="pt").pixel_values.to(v_device)
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output_ids = v_model.generate(pixel_values, max_length=16, num_beams=4)
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preds = v_tokenizer.batch_decode(output_ids, skip_special_tokens=True)
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return preds[0].strip()
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except Exception:
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return "صورة مرفقة"
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@app.post("/v1/chat/completions")
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async def chat_endpoint(request: ChatRequest):
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# 1. تجهيز الـ System Prompt والرسائل (نفس منطقك السابق)
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try:
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user_tz = pytz.timezone(request.timezone)
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now = datetime.now(user_tz)
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current_day = now.strftime("%A")
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current_time_str = now.strftime("%I:%M %p")
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except Exception:
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current_day = "اليوم"
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current_time_str = datetime.now().strftime("%I:%M %p")
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incoming_system = next((m for m in request.messages if m.get("role") == "system"), None)
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incoming_system_prompt = incoming_system["content"] if incoming_system else base_identity
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updated_system_prompt = (
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f"{incoming_system_prompt}\n\n"
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f"الوقت الحالي: {current_day}, {current_time_str}\n"
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"تحدث باللهجة السورية الشامية فقط. لا تكرر التحية."
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)
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engine_messages = [{"role": "system", "content": updated_system_prompt}]
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for msg in request.messages:
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if msg.get("role") == "system": continue
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content = msg.get("content", "")
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if isinstance(content, list):
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# معالجة الصور إذا وجدت
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text_parts = [item.get("text", "") for item in content if item.get("type") == "text"]
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for item in content:
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if item.get("type") == "image_url":
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img_data = item.get("image_url", {}).get("url", "")
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if "," in img_data:
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desc = predict_image(img_data.split(",")[-1])
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text_parts.append(f"[وصف الصورة: {desc}]")
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engine_messages.append({"role": msg.get("role"), "content": " ".join(text_parts)})
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else:
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engine_messages.append({"role": msg.get("role"), "content": str(content)})
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payload = {"messages": engine_messages, "temperature": request.temperature}
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# 2. وظيفة الـ Generator لجلب البيانات شقفة شقفة (Stream)
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async def stream_generator():
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async with httpx.AsyncClient(timeout=120.0) as client:
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async with client.stream("POST", GEMMA_URL, json=payload) as response:
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if response.status_code != 200:
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yield "خطأ في الاتصال بالمحرك."
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return
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async for chunk in response.aiter_text():
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# هون منمرر النص اللي عم يطلعه الـ Engine فوراً
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yield chunk
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return StreamingResponse(stream_generator(), media_type="text/plain")
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# =========================
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# Story streaming endpoint
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# =========================
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# ... (نفس الاستيرادات السابقة)
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@app.post("/v1/story/generate")
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async def story_generate_endpoint(request: StoryGenerateRequest):
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try:
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system_prompt = build_story_system_prompt(request)
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user_prompt = build_story_user_prompt(request)
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payload = {
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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],
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"temperature": request.temperature,
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}
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if request.stream:
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async def stream_generator():
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async with httpx.AsyncClient(timeout=120.0) as client:
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async with client.stream("POST", GEMMA_URL, json=payload) as response:
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if response.status_code != 200:
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yield json.dumps({"ok": False, "error": "Connection Error"}, ensure_ascii=False)
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return
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# منبعت النص شقفة شقفة متل ما عم يجي من الـ Engine
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async for chunk in response.aiter_text():
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# منمرر الـ chunk فوراً للفرونت
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yield chunk
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return StreamingResponse(stream_generator(), media_type="text/plain; charset=utf-8")
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# في حال مو ستريم (الوضع العادي)
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raw_text = await collect_engine_text(payload)
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parsed = try_extract_json(raw_text)
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allowed = set([c.name.strip() for c in request.characters if c.name] + ["الراوي"])
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parsed = filter_story_characters(parsed, allowed)
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return JSONResponse({"ok": True, "data": parsed})
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except Exception as e:
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traceback.print_exc()
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return JSONResponse(status_code=500, content={"ok": False, "error": str(e)})
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@app.post("/v1/story/generate-json")
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async def story_generate_json_endpoint(request: StoryGenerateRequest):
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try:
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system_prompt = build_story_system_prompt(request)
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user_prompt = build_story_user_prompt(request)
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payload = {
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"messages": [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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],
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"temperature": request.temperature,
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}
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raw_text = await collect_engine_text(payload)
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parsed = try_extract_json(raw_text)
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allowed = set([c.name for c in request.characters if c.name] + ["الراوي"])
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parsed = filter_story_characters(parsed, allowed)
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return JSONResponse({
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"ok": True,
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"chapter_text": parsed.get("chapter_text", ""),
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"character_responses": parsed.get("character_responses", []),
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"raw_text": raw_text
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})
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except Exception as e:
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traceback.print_exc()
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return JSONResponse(status_code=500, content={"ok": False, "error": str(e)})
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if __name__ == "__main__":
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uvicorn.run(app, host="0.0.0.0", port=7860)
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