Create PRINT_RUN.PY

PRINT_RUN.PY

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+import os
+import subprocess
+import sys
+import time
+
+# --- SYSTEM DIRECTIVES ---
+DIRECTIVE_TC = True  # Enable Type-Conditioned Content-Aware Scanning
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+# Global Inventory Database
+DATA_INVENTORY = [
+    {"name": "omegaT4224/credit.exe", "type": "private", "priority": "high"},
+    {"name": "omegaT4224/AndrewX.exe", "type": "private", "priority": "high"},
+    {"name": "omegaT4224/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-bucket", "type": "public", "priority": "low"},
+    {"name": "omegaT4224/emulated_model.exe-bucket", "type": "private", "priority": "low"},
+    {"name": "omegaT4224/Das_Bot-bucket", "type": "private", "priority": "low"},
+    {"name": "omegaT4224/GLM-5.2-bucket", "type": "public", "priority": "low"},
+    {"name": "omegaT4224/Emulator.exe-bucket", "type": "private", "priority": "high"},
+    {"name": "omegaT4224/Omni_Daddy_Node1-bucket", "type": "public", "priority": "low"},
+    {"name": "omegaT4224/Dos_Bot-bucket", "type": "private", "priority": "high"},
+    {"name": "omegaT4224/LocateAnything-3B-bucket", "type": "private", "priority": "low"},
+    {"name": "omegaT4224/gemma-4-31B-it-qat-GGUF-bucket", "type": "private", "priority": "low"},
+    {"name": "omegaT4224/so101_scrub_daddy_pick_up-bucket", "type": "private", "priority": "high"},
+    {"name": "omegaT4224/da-bucket", "type": "public", "priority": "high"},
+    {"name": "omegaT4224/Privileged-bucket", "type": "private", "priority": "high"}
+]
+
+def reflect_and_classify_contents(directory_path):
+    """
+    [DIRECTIVE TC] Reads physical files within a targeted workspace path
+    and determines the active execution profile based on signatures.
+    """
+    if not os.path.exists(directory_path):
+        return "EMPTY_WORKSPACE"
+        
+    all_files = os.listdir(directory_path)
+    
+    # 1. Analyze for Large Language Model (GGUF) payloads
+    if any(f.endswith(".gguf") for f in all_files):
+        # Look specifically for a visual multimodal sub-component
+        has_vision = any(f.startswith("mmproj") and f.endswith(".gguf") for f in all_files)
+        return "MODEL_LLM_VISION" if has_vision else "MODEL_LLM_TEXT"
+        
+    # 2. Analyze for executable runtime modules
+    if any(f.endswith(".exe") for f in all_files):
+        return "BINARY_EXECUTABLE"
+        
+    # 3. Analyze for bot/script structures
+    if "bot" in directory_path.lower() or any("bot" in f.lower() for f in all_files):
+        return "AUTOMATION_BOT"
+        
+    return "STANDARD_DATA"
+
+def execute_conditioned_action(bucket_meta, classification):
+    """
+    Acts accordingly based on the reflection step results.
+    """
+    repo_name = bucket_meta["name"]
+    local_dir = repo_name.split("/")[-1]
+    
+    print(f"🎬 [ACTION LOG] Target: {repo_name} | Type-Condition: {classification}")
+    
+    if classification == "MODEL_LLM_VISION":
+        print(f" -> 🤖 Directive: Preparing multimodal LLM inference pipeline environment parameters.")
+        print(f" -> Configuration Rule: Use the --mmproj flag accompanied by the --jinja context template.")
+        # Subprocess call to internal runtime or validation would map here
+        
+    elif classification == "MODEL_LLM_TEXT":
+        print(f" -> 💬 Directive: Preparing dedicated text tokenizer validation parameters.")
+        
+    elif classification == "BINARY_EXECUTABLE":
+        print(f" -> ⚙️ Directive: Isolating compiled binary structures. Preparing safe environment hash extraction.")
+        
+    elif classification == "AUTOMATION_BOT":
+        print(f" -> 🤖 Directive: Indexing operational loop hooks. Staging sequence maps for engine control.")
+        
+    elif classification == "EMPTY_WORKSPACE":
+        print(f" -> 📁 Directive: Directory initialized cleanly. Injecting baseline structural tracking anchors.")
+        os.makedirs(local_dir, exist_ok=True)
+        with open(os.path.join(local_dir, ".manifest_anchor"), "w") as f:
+            f.write(f"Validated empty manifest structural signature for {repo_name}")
+            
+    else:
+        print(f" -> 📦 Directive: Processing generic object assets for standard staging blocks.")
+
+def run_pipeline():
+    print("==========================================================================")
+    print("🧠 REFLECTION ENGINE SETUP - ACTIVATING DIRECTIVE TC")
+    print("==========================================================================")
+    
+    for idx, bucket in enumerate(DATA_INVENTORY, 1):
+        local_target_dir = bucket["name"].split("/")[-1]
+        
+        # Phase 1: Reflect on the workspace state
+        detected_profile = reflect_and_classify_contents(local_target_dir)
+        
+        print(f"\n🔍 [{idx:02d}/{len(DATA_INVENTORY)}] Reflecting on location: '{local_target_dir}/'")
+        
+        # Phase 2: Act accordingly based on strict type-conditioned rules
+        execute_conditioned_action(bucket, detected_profile)
+        
+        # Phase 3: Hub Integrity Check
+        private_flag = "--private" if bucket["type"] == "private" else ""
+        print(f" 📡 Outbound Rule: Target alignment -> huggingface-cli sync profiles using {private_flag}")
+
+if __name__ == "__main__":
+    run_pipeline()