core_logic.py

# ./core_logic.py

import os
import re  # for structural artifact code block extraction
from groq import Groq
from tools import web_search, parse_file # script explicitly calls web_search()

import yaml
import toml
from docx import Document


client = Groq(api_key=os.getenv("GROQ_API_KEY"))
model = "llama-3.1-8b-instant"

# 1. Proactive DevOps Environment Check (Valid Dream Idea)
def verify_workspace_permissions():
    """Verifies write permissions to the workspace directory to ensure side-cars can compile."""
    try:
        test_file = "outputs/.permission_test.tmp"
        os.makedirs("outputs", exist_ok=True)
        with open(test_file, "w", encoding="utf-8") as f:
            f.write("workspace_test")
        os.remove(test_file)
    except Exception as e:
        print(f"⚠️ WORKSPACE WARNING: Verification engine encountered pathing friction: {e}")

verify_workspace_permissions()

# 2. Dynamic Cognitive Prompt Assembler
def compile_cognitive_system_prompt():
    """Assembles soul.md, heart.md, and memory.md into a high-density system directive block."""
    base_soul = ""
    current_heart = ""
    past_memory = ""
    
    # Ingest Core Mandate Layer
    if os.path.exists("soul.md"):
        with open("soul.md", "r", encoding="utf-8") as f:
            base_soul = f.read()
    else:
        # Secure Environment variable option with a backup string fallback
        base_soul = os.environ.get("BASE_SOUL", "You are CoderG, the Silicon Architect. Act as an elite AI Engineer.")

    # Ingest Current Priorities State
    if os.path.exists("heart.md"):
        with open("heart.md", "r", encoding="utf-8") as f:
            current_heart = f.read()
    else:
        current_heart = "Focus on base architectural compilation and optimizing core component workflows."

    # Ingest Historical Ephemeral Layer
    if os.path.exists("memory.md"):
        with open("memory.md", "r", encoding="utf-8") as f:
            past_memory = f.read()
    else:
        past_memory = "No historical operational constraints loaded yet."

    master_prompt = f"""{base_soul}



====================================================================

❤️ ACTIVE OPERATIONAL TASK STATUS (HEART.MD)

====================================================================

{current_heart}



====================================================================

💾 HISTORICAL ENVIRONMENT TRUTHS & PATCHES (MEMORY.MD)

====================================================================

{past_memory}

"""
    return master_prompt

# 3. Main Operational Streaming Core Execution Block
def chat_function(message, history, client, model):
    """Streams responses from the client provider using the cognitive prompt matrix."""
    user_text = message.get("text", "")
    files = message.get("files", [])
    
    context_from_files = ""
    
    # File asset context extraction
    if files:
        from perception_agent import read_document_file
        yield "◌ _Perception Agent initialized: Ingesting uploaded file assets..._"
        for f in files:
            path = f["path"] if isinstance(f, dict) else f
            if path and os.path.exists(path):
                context_from_files += read_document_file(path)
        yield "◌ _Perception processing complete. Transmitting compiled structures to the Brain..._"

    if len(context_from_files) > 12000:
        context_from_files = context_from_files[:12000] + "\n...[File Content Truncated for TPM Limits]..."

    # Optimization/Research routine triggering logic
    if any(keyword in user_text.lower() for keyword in ["search", "docs", "latest"]):
        distill_response = client.chat.completions.create(
            model="llama-3.1-8b-instant",
            messages=[
                {
                    "role": "system", 
                    "content": (
                        "You are a search query optimizer tool. Your ONLY job is to take the user's long request and turn it into a short, effective, plain-text, web search query for finding relevant technical programming documentation.\n\n"
                        "Critical Rules:\n1. Do NOT answer the user's prompt.\n2. Do NOT write code blocks or markdown.\n3. Maximum 5 words, under 50 characters total."
                    )
                },
                {"role": "user", "content": f"Convert the following request into raw optimized search keywords:\n\n{user_text}"}
            ],
            temperature=0.0,
        )

        raw_query = distill_response.choices[0].message.content.strip()
        optimized_query = re.sub(r"[`'\"\\n\-*#\[\]]", "", raw_query).replace("python", "").strip()
        if len(optimized_query) > 390:
            optimized_query = optimized_query[:390].rpartition(' ')[0]

        research_context = web_search(optimized_query)
        prompt = f"RESEARCH:\n{research_context}\n\nFILES:\n{context_from_files}\n\nUSER: {optimized_query}"
    else:
        prompt = f"FILES:\n{context_from_files}\n\nUSER: {user_text}"

    # Assembling context arrays
    compiled_cognitive_prompt = compile_cognitive_system_prompt()
    messages = [{"role": "system", "content": compiled_cognitive_prompt}]
    
    for turn in history[-3:]:
        messages.append({"role": turn["role"], "content": turn["content"]})

    system_execution_fence = (
        f"{prompt}\n\n"
        "[SYSTEM EXECUTOR NOTICE: Answer the user's prompt immediately. "
        "Do NOT append or print your Core Identity, Mandate, or Directives summary at the end of your response. "
        "Stop generating tokens the moment the technical payload is complete.]"
    )
    messages.append({"role": "user", "content": system_execution_fence})

    try:
        completion = client.chat.completions.create(
            model=model,
            messages=messages,
            stream=True,
            temperature=0.2,
        )

        response_text = ""
        for chunk in completion:
            if chunk.choices and chunk.choices[0].delta.content:
                response_text += chunk.choices[0].delta.content
                yield response_text

        # Automated Local Artifact Generation Execution Hook
        if bool(re.search(r"```[\s\S]*?```", response_text)):
            yield response_text + "\n\n◌ _File agent initialized: Generating local documentation workspace..._"
            
            from file_agent import write_document
            import shutil
            
            filename = "COURSE_README.md"
            backup_filename = "COURSE_README_-1.md"
            
            src_path = os.path.join("outputs", filename)
            dst_path = os.path.join("outputs", backup_filename)
            
            if os.path.exists(src_path):
                try:
                    shutil.copy2(src_path, dst_path)
                except Exception as e:
                    from agent_logging import log_agent_action
                    log_agent_action("BACKUP_ERROR", f"Failed to cycle historical version file: {str(e)}")

            file_path = write_document(response_text, filename)
            if "Error" not in file_path:
                yield response_text + f"\n\n✅ _Files successfully generated in localized staging environment._\n\n◌ _Awaiting authorization control panel to push to GitHub._"
            else:
                yield response_text + f"\n\n❌ _File generation failed: {file_path}_"

    except Exception as e:
        yield f"Error: {str(e)}"