fix missing lib: folder_paths

.gitignore

@@ -0,0 +1,4 @@
+__pycache__/__init__.cpython-310.pyc
+__pycache__/local_gguf_llm_connector.cpython-310.pyc
+__pycache__/local_llm_connector.cpython-310.pyc
+__pycache__/local_prompt_generator.cpython-310.pyc

README.md

@@ -1,6 +1,6 @@
 # ComfyUI_LocalLLMNodes
 
-A custom node pack for [ComfyUI](https://github.com/comfyanonymous/ComfyUI) to run Large Language Models (LLMs) locally for prompt generation and text tasks within ComfyUI workflows.
+A custom node pack for [ComfyUI](https://github.com/comfyanonymous/ComfyUI) to run Large Language Models (LLMs) locally for flux kontext dev prompt generation within ComfyUI workflows.
 
 **Purpose:**
 This pack is designed to simplify the creation of detailed, high-quality prompts for advanced image generation models like **Flux Kontext Dev**. It achieves this by using a **locally running LLM** (Large Language Model) to process:
@@ -128,4 +128,4 @@ Running large LLMs alongside large image models can strain resources.
 
 ## Acknowledgements
 
-This node pack builds upon concepts from the excellent [ComfyUI-MieNodes](https://github.com/MieMieeeee/ComfyUI-MieNodes) extension.
+This node pack builds upon concepts from the excellent [ComfyUI-MieNodes](https://github.com/MieMieeeee/ComfyUI-MieNodes) extension.

__init__.py

@@ -55,6 +55,8 @@ if GGUF_DEPS_AVAILABLE:
 else:
     print("[ComfyUI_LocalLLMNodes] GGUF LLM Connector node will NOT be available due to missing dependency (llama-cpp-python).")
 
+from .arabic_product_node import ArabicProductDescriptionGenerator
+
 # --- Define Mappings ---
 # Populate the dictionaries based on which nodes were successfully imported.
 
@@ -65,12 +67,14 @@ if LOCAL_LLM_NODES_AVAILABLE:
         "LocalKontextPromptGenerator": LocalKontextPromptGenerator,
         "AddUserLocalKontextPreset": AddUserLocalKontextPreset,
         "RemoveUserLocalKontextPreset": RemoveUserLocalKontextPreset,
+        "ArabicProductDescriptionGenerator": ArabicProductDescriptionGenerator,
     })
     NODE_DISPLAY_NAME_MAPPINGS.update({
-        "SetLocalLLMServiceConnector": "Set Local LLM Service Connector 🐑 (HuggingFace)",
-        "LocalKontextPromptGenerator": "Local Kontext Prompt Generator 🐑",
-        "AddUserLocalKontextPreset": "Add User Local Kontext Preset 🐑",
-        "RemoveUserLocalKontextPreset": "Remove User Local Kontext Preset 🐑",
+        "SetLocalLLMServiceConnector": "Set Local LLM Service Connector (HuggingFace)",
+        "LocalKontextPromptGenerator": "Local Kontext Prompt Generator",
+        "AddUserLocalKontextPreset": "Add User Local Kontext Preset",
+        "RemoveUserLocalKontextPreset": "Remove User Local Kontext Preset",
+        "ArabicProductDescriptionGenerator": "Arabic Product Description Generator"
     })
 
 # Add GGUF based nodes if available
@@ -79,7 +83,7 @@ if LOCAL_GGUF_NODES_AVAILABLE:
         "SetLocalGGUFLLMServiceConnector": SetLocalGGUFLLMServiceConnector,
     })
     NODE_DISPLAY_NAME_MAPPINGS.update({
-        "SetLocalGGUFLLMServiceConnector": "Set Local GGUF LLM Service Connector 🐑",
+        "SetLocalGGUFLLMServiceConnector": "Set Local GGUF LLM Service Connector",
     })
 
 # --- Define what ComfyUI sees ---

arabic_product_node.py

@@ -0,0 +1,183 @@
+# arabic_product_node.py
+
+import hashlib
+import os
+import json
+
+# --- Simple logging utility ---
+def log(message):
+    print(f"[ArabicProductDesc] {message}")
+
+# --- Paths and Preset Setup (Same as original) ---
+script_directory = os.path.dirname(os.path.abspath(__file__))
+USER_PRESETS_FILE = os.path.join(script_directory, "user_kontext_presets.json")
+
+# --- Built-in Presets for Arabic Product Descriptions ---
+ARABIC_PRODUCT_PRESETS = {
+    "عام - وصفي": {
+        "system": (
+            "أنت خبير تسويق إلكتروني. اكتب وصفًا احترافيًا باللغة العربية للمنتج بناءً على الوصف المرئي أو المعلومات المقدمة. "
+            "يجب أن يتضمن الوصف: اسم المنتج (إن عُرف)، المكونات أو المواصفات، الفوائد الرئيسية، طريقة الاستخدام، الفئة المستهدفة، وأي ملاحظات أمان. "
+            "استخدم لغة تسويقية جذابة، واضحة، وسلسة. لا تستخدم تنسيق Markdown أو عناوين فرعية. لا تضيف تعليقات خارجية."
+        )
+    },
+    "عناية بالبشرة": {
+        "system": (
+            "أنت أخصائي عناية بالبشرة. اكتب وصفًا عربيًا احترافيًا لكريم أو سيروم بناءً على الوصف المرئي أو التقني. "
+            "ركّز على المكونات الفعالة (مثل SPF، حمض الهيالورونيك)، الفوائد (ترطيب، حماية، تفتيح)، ومدى ملاءمته لأنواع البشرة. "
+            "اذكر طريقة الاستخدام (قبل النوم، بعد التنظيف، إلخ)، وكم مرة يُستخدم. استخدم لغة جذابة لكن علمية."
+        )
+    },
+    "طعام ومشروبات": {
+        "system": (
+            "أنت كاتب محتوى غذائي. اكتب وصفًا شهيًا وملهمًا بالعربية لمنتج غذائي أو شراب. "
+            "صف الطعم، القوام، المكونات الطبيعية، القيمة الغذائية، ومتى يُفضل تناوله (صباحًا، بعد التمرين). "
+            "استخدم لغة حسية ومشوقة. لا تبالغ. لا تستخدم تنسيق."
+        )
+    },
+    "إلكترونيات": {
+        "system": (
+            "أنت خبير تقني. اكتب وصفًا عربيًا واضحًا لجهاز إلكتروني (مثل سماعة، شاحن، ساعة ذكية). "
+            "اذكر المواصفات الأساسية (البطارية، التوصيل، الجودة)، الفوائد (راحة، دقة، كفاءة)، والجمهور المستهدف. "
+            "اجعله سهل الفهم للمستخدم العادي مع لمسة احترافية."
+        )
+    }
+}
+
+def load_user_presets():
+    """Load user-defined presets from JSON file."""
+    if os.path.exists(USER_PRESETS_FILE):
+        try:
+            with open(USER_PRESETS_FILE, "r", encoding="utf-8") as f:
+                return json.load(f)
+        except (json.JSONDecodeError, IOError) as e:
+            log(f"Error loading user presets: {e}")
+            return {}
+    return {}
+
+def get_all_product_presets():
+    """Get combined built-in and user-defined presets."""
+    all_presets = ARABIC_PRODUCT_PRESETS.copy()
+    user_presets = load_user_presets()
+    all_presets.update(user_presets)
+    return all_presets
+
+# --- Category for UI ---
+PRODUCT_DESC_CATEGORY = "Local LLM Nodes/Product Description (Arabic)"
+
+
+class ArabicProductDescriptionGenerator:
+    """
+    Generates a detailed Arabic product description using:
+    - Optional image description
+    - User instruction in Arabic
+    - Selected preset (system prompt)
+    - Local LLM via LLMServiceConnector
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        all_presets = get_all_product_presets()
+        default_preset = next(iter(all_presets.keys()), "") if all_presets else ""
+
+        return {
+            "required": {
+                "llm_service_connector": ("LLMServiceConnector",),
+                "user_instruction_ar": ("STRING", {
+                    "default": "اكتب وصفًا تفصيليًا لهذا المنتج",
+                    "multiline": True,
+                    "tooltip": "التعليمات من المستخدم بالعربية، مثلاً: اكتب وصفًا تسويقيًا"
+                }),
+                "preset": (list(all_presets.keys()), {
+                    "default": default_preset,
+                    "tooltip": "اختر نمط الوصف (عام، عناية بالبشرة، إلخ)"
+                }),
+                "seed": ("INT", {
+                    "default": 0,
+                    "min": 0,
+                    "max": 0xffffffffffffffff,
+                    "control_after_generate": True
+                })
+            },
+            "optional": {
+                "image_description": ("STRING", {
+                    "default": "",
+                    "multiline": True,
+                    "tooltip": "وصف الصورة أو التفاصيل التقنية عن المنتج (اختياري)"
+                })
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("arabic_product_description",)
+    FUNCTION = "generate_description"
+    CATEGORY = PRODUCT_DESC_CATEGORY
+
+    def generate_description(self, llm_service_connector, user_instruction_ar, preset, seed=None, image_description=None):
+        # Load preset
+        all_presets = get_all_product_presets()
+        preset_data = all_presets.get(preset)
+        if not preset_data:
+            raise ValueError(f"النمط غير معروف: {preset}")
+
+        # Safe string conversion
+        def safe_str(s):
+            return str(s) if s is not None else ""
+
+        img_desc = safe_str(image_description).strip()
+        user_inst = safe_str(user_instruction_ar).strip()
+
+        # Build user content
+        user_content_parts = []
+        if img_desc:
+            user_content_parts.append(f"وصف المنتج أو الصورة: {img_desc}")
+        if user_inst:
+            user_content_parts.append(f"التعليمات: {user_inst}")
+        else:
+            user_content_parts.append("التعليمات: اكتب وصفًا تفصيليًا للمنتج.")
+
+        user_content = " ".join(user_content_parts).strip()
+        if not user_content:
+            user_content = "وصف المنتج: منتج على خلفية بيضاء. التعليمات: اكتب وصفًا تسويقيًا بالعربية."
+
+        # Build messages
+        messages = [
+            {"role": "system", "content": preset_data["system"]},
+            {"role": "user", "content": user_content}
+        ]
+
+        # Call LLM
+        try:
+            response = llm_service_connector.invoke(messages, seed=seed)
+            return (response.strip(),)
+        except Exception as e:
+            error_msg = f"[خطأ] تعذر إنشاء الوصف: {str(e)}"
+            log(error_msg)
+            return (error_msg,)
+
+    def is_changed(self, llm_service_connector, user_instruction_ar, preset, seed, image_description=None):
+        """Ensure node re-runs only when inputs change."""
+        try:
+            hasher = hashlib.md5()
+            hasher.update(str(user_instruction_ar).encode('utf-8'))
+            hasher.update(preset.encode('utf-8'))
+            hasher.update(str(seed).encode('utf-8'))
+            hasher.update(str(image_description).encode('utf-8'))
+
+            # Add preset system prompt
+            all_presets = get_all_product_presets()
+            preset_data = all_presets.get(preset)
+            if preset_data and "system" in preset_data:
+                hasher.update(preset_data["system"].encode('utf-8'))
+
+            # Add connector state
+            try:
+                connector_state = llm_service_connector.get_state()
+            except AttributeError:
+                connector_state = str(llm_service_connector)
+            hasher.update(connector_state.encode('utf-8'))
+
+            return hasher.hexdigest()
+        except Exception as e:
+            log(f"is_changed error: {e}")
+            return float("nan")

local_gguf_llm_connector.py

@@ -1,19 +1,61 @@
 # ComfyUI/custom_nodes/ComfyUI_LocalLLMNodes/local_gguf_llm_connector.py
 import os
+import subprocess
+import sys
+import platform
 import folder_paths
-# --- Library for GGUF local LLM inference ---
+
 try:
     import llama_cpp
-    # import llama_cpp.llama_tokenizer # Optional, remove if not used
     LLAMA_CPP_AVAILABLE = True
 except ImportError:
-    # --- Simple logging utility for this package (reuse existing one) ---
-    # Import here to avoid potential issues if log isn't defined yet if imported at top in case of circular import risks,
-    # though usually safe. Ensure local_llm_connector.py defines 'log' early.
     from .local_llm_connector import log
-    log("[LocalGGUFLLMConnector] Warning: llama-cpp-python library not found.")
-    LLAMA_CPP_AVAILABLE = False
-    llama_cpp = None
+
+    def check_nvidia_gpu():
+        try:
+            result = subprocess.run(['nvidia-smi'], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+            return result.returncode == 0
+        except (FileNotFoundError, OSError):
+            return False
+
+    def check_apple_metal():
+        return platform.system() == "Darwin" and "arm" in platform.machine().lower()
+
+    log("[LocalGGUFLLMConnector] llama-cpp-python not found. Attempting auto-install...")
+
+    # Detect hardware
+    cmake_args = []
+    if check_nvidia_gpu():
+        log("NVIDIA GPU detected: Enabling CUDA acceleration.")
+        cmake_args.append("-DLLAMA_CUBLAS=on")
+    elif check_apple_metal():
+        log("Apple Silicon (M1/M2/M3) detected: Enabling Metal acceleration.")
+        cmake_args.append("-DLLAMA_METAL=on")
+    else:
+        log("💻 No compatible GPU found: Installing CPU-only version.")
+
+    # Build pip install command
+    cmd = [sys.executable, "-m", "pip", "install", "llama-cpp-python"]
+    if cmake_args:
+        cmd += [f"--config-settings=cmake_args={';'.join(cmake_args)}"]
+    cmd += ["--force-reinstall", "--no-cache-dir"]
+
+    try:
+        log(f"📦 Running: {' '.join(cmd)}")
+        subprocess.check_call(cmd)
+        log("llama-cpp-python installed successfully!")
+
+        # Force reload after install
+        import importlib
+        import llama_cpp
+        importlib.reload(llama_cpp)
+        LLAMA_CPP_AVAILABLE = True
+    except subprocess.CalledProcessError as e:
+        log(f"Installation failed: {e}")
+        LLAMA_CPP_AVAILABLE = False
+    except Exception as e:
+        log(f"Unexpected error during install: {e}")
+        LLAMA_CPP_AVAILABLE = False
 
 # Reuse the logging function from local_llm_connector
 from .local_llm_connector import log # Ensure this is the final import for log

local_prompt_generator.py

@@ -16,7 +16,7 @@ USER_PRESETS_FILE = os.path.join(script_directory, "user_kontext_presets.json")
 
 # --- Replicate Built-in Presets (from MieNodes' prompt_generator.py) ---
 KONTEXT_PRESETS = {
-    "Kid Clothes -> Flux Prompt": {
+    "Kid Clothes": {
         "system": (
             "You are an expert prompt generator for Flux Kontext. Your task is to place clothing described by the user onto a baby or child with Arab facial features, while preserving all material, color, and style details from the original clothing description. "
             "Input: "
@@ -36,7 +36,7 @@ KONTEXT_PRESETS = {
             "A photorealistic Arab baby with round cheeks and light tan skin, lying on a soft white fleece blanket, wearing a pale blue cotton romper with stitched white cloud patterns and integrated footies, captured in soft natural lighting with shallow DOF, cozy nursery ambiance, 8K resolution, cinematic baby fashion catalog style."
         )
     },
-    "Women’s Clothes -> Flux Prompt": {
+    "Women’s Clothes": {
         "system": (
             "You are an expert visual prompt composer for Flux Kontext. Your job is to place the clothing described by the user onto a realistic Muslim woman model in her 20s with Arab features, while preserving all original garment details. "
             "Input: "
@@ -56,7 +56,7 @@ KONTEXT_PRESETS = {
             "A young Arab Muslim woman in her 20s with soft olive skin and delicate features, wearing a beige linen abaya with subtle gold-stitched cuffs and a loosely tied waist sash, standing in a minimalist white studio under soft diffused lighting, side-facing pose with natural folds in the fabric, fashion editorial style with cinematic shallow DOF."
         )
     },
-    "Beauty Product Use -> Flux Prompt": {
+    "Beauty Product Use": {
         "system": (
             "You are a creative image prompt generator for Flux Kontext focused on health, skincare, and beauty products. Your job is to place the described product in an artistic or commercial setting — either used by a model (Arab man or woman), or placed within a creative, beauty-inspired scene — while preserving all visual and material details of the product. "
             "Input: "
@@ -75,7 +75,7 @@ KONTEXT_PRESETS = {
             "An elegant Arab woman with natural tan skin and voluminous dark hair, holding an amber glass dropper bottle with a gold label and black cap, delicately applying serum to her cheek in front of a glowing vanity mirror, soft candle-lit ambiance, subtle shadows, cinematic close-up, bokeh background, luxury skincare ad style."
         )
     },
-    "Beauty Product Display -> Flux Prompt": {
+    "Beauty Product Display": {
         "system": (
             "You are an expert in luxury beauty product visuals for Flux Kontext. Place the described product in a stunning, creative, brand-inspired environment (without any models), using beauty aesthetics and visual storytelling. "
             "Input: "

pyproject.toml

@@ -0,0 +1,25 @@
+[project]
+name = "LocalLLMnodes"
+description = "Run local LLMs in ComfyUI and generate Flux Kontext.dev prompts from simple English descriptions"
+readme = "README.md"
+version = "0.1.0"
+license = { text = "MIT" }
+dependencies = ["# Core dependencies for Hugging Face transformers based nodes (e.g., Hermes-2-Pro-Llama-3-8B)", "transformers>=4.38.0 # Or a specific version you know works", "torch>=2.0.0 # Or a specific version you know works", "# Optional, for quantization with transformers (e.g., 4-bit)", "# bitsandbytes # Uncomment if you use BitsAndBytesConfig in local_llm_connector.py", "# accelerate # Often needed with transformers, especially for device_map", "# Core dependency for GGUF based nodes (e.g., Mistral-7B-Instruct-Q8.gguf)", "llama-cpp-python>=0.1.0 # Or a specific version", "# Optional: For downloading models from Hugging Face Hub", "huggingface_hub", "# Optional: If your package uses any specific utility libraries", "# (Although deepdiff, pillow, etc. might be pulled in by ComfyUI or other nodes)", "# deepdiff", "# pillow", "# Notes for Users:", "# For llama-cpp-python with GPU support (CUDA), installation is more complex.", "# Example for CUDA 11.8 (check llama-cpp-python docs for your version):", "# CMAKE_ARGS=\"-DLLAMA_CUBLAS=on\" pip install llama-cpp-python", "# Example for CUDA 12.1:", "# CMAKE_ARGS=\"-DLLAMA_CUDA=on\" pip install llama-cpp-python"]
+authors = [
+    { name = "Obada Shawabke", email = "obdkml@gmail.com" }
+]
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: OS Independent",
+]
+
+[project.urls]
+Repository = "https://github.com/kmlbdh/ComfyUI_LocalLLMNodes"
+Homepage = "https://github.com/kmlbdh/ComfyUI_LocalLLMNodes"
+
+[tool.comfy]
+PublisherId = "kmlbdh"
+DisplayName = "ComfyUI_LocalLLMNodes"
+Icon = "🤖"
+includes = []