updated core_logic

core_logic.py

@@ -1,10 +1,10 @@
 
-# ./core_logic.py -> Token-safe
+# ./core_logic.py
 
 import os
-import re  # Added for structural artifact code block extraction
+import re  # for structural artifact code block extraction
 from groq import Groq
-from tools import web_search, parse_file
+from tools import web_search, parse_file # script explicitly calls web_search()
 
 import yaml
 import toml
@@ -14,52 +14,49 @@ from docx import Document
 client = Groq(api_key=os.getenv("GROQ_API_KEY"))
 model = "llama-3.1-8b-instant"
 
-# Verify write permissions to 'outputs' directory
-def verify_permissions():
-    test_file = "permission_test.txt"
+# 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:
-        with open(test_file, "w") as f:
-            f.write("test")
+        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)
-        print("✅ Write permissions verified.")
     except Exception as e:
-        print(f"❌ PERMISSION ERROR: {e}")
-
-verify_permissions()
+        print(f"⚠️ WORKSPACE WARNING: Verification engine encountered pathing friction: {e}")
 
+verify_workspace_permissions()
 
+# 2. Dynamic Cognitive Prompt Assembler
 def compile_cognitive_system_prompt():
-    """
-    Cognitive Compilation Layer - Dynamically constructs the master system prompt
-    by assembling soul.md, heart.md, and memory.md side-car layers.
-    """
+    """Assembles soul.md, heart.md, and memory.md into a high-density system directive block."""
     base_soul = ""
     current_heart = ""
     past_memory = ""
     
-    # 1. Gather Soul Directive
+    # 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:
-        # Emergency hardcoded fallback matching your architectural profile
-        base_soul = "You are CoderG, the Silicon Architect. Act as an elite Full-stack AI Engineer."
+        # 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.")
 
-    # 2. Gather Heart State
+    # 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."
 
-    # 3. Gather Memory Graph
+    # 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."
 
-    # Combine all layers into a structural system context map
     master_prompt = f"""{base_soul}
 
 ====================================================================
@@ -74,149 +71,85 @@ def compile_cognitive_system_prompt():
 """
     return master_prompt
 
-
-def chat_function(message, history):
+# 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 Aggregator Buffer for all multi-format assets
     context_from_files = ""
     
-    # 1. Process Multimodal and Extended Multi-format Files via Perception Agent
+    # 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:
-            # Gradio 6 handles file entries either as dictionaries with a 'path' key or flat strings
             path = f["path"] if isinstance(f, dict) else f
             if path and os.path.exists(path):
-                file_content = read_document_file(path)
-                context_from_files += file_content
-                
+                context_from_files += read_document_file(path)
         yield "◌ _Perception processing complete. Transmitting compiled structures to the Brain..._"
 
-    
-    # TRUNCATE FILE CONTEXT: Max ~3000 tokens (approx 12,000 chars)
     if len(context_from_files) > 12000:
         context_from_files = context_from_files[:12000] + "\n...[File Content Truncated for TPM Limits]..."
 
-    # 2. Research Trigger
+    # Optimization/Research routine triggering logic
     if any(keyword in user_text.lower() for keyword in ["search", "docs", "latest"]):
-        # Use a fast micro-turn to distill the massive user prompt into optimized keywords
         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:\n"
-                    "1. Do NOT answer the user's prompt.\n"
-                    "2. Do NOT write code blocks, code explanations, tasks, or JSON data structures.\n"
-                    "3. Your entire output must be a single sentence under 50 characters.\n"
-                    "4. If the user provides a code file or raw data logs, ignore the text content and generate a query searching for the underlying concept (e.g., 'Scapy network sniffing documentation python').\n"
-                    "5. Output ONLY raw keywords.\n"
-                    "6. NEVER use markdown, backticks, or code blocks.\n"
-                    "7. NEVER wrap your output in single or double quotes.\n"
-                    "8. Maximum 5 words, under 50 characters total."
-                )
-            },
-            {
-                "role": "user", 
-                "content": f"Convert the following request into raw optimized search keywords based on your system rules:\n\n{user_text}"
-            }
-        ],
-        temperature=0.0,
-    )
+            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,
+        )
 
-        # Extract and aggressively sanitize the string programmatically
         raw_query = distill_response.choices[0].message.content.strip()
-        # Strip away any lingering quotes, backticks, or markdown syntax characters
-        optimized_query = re.sub(r"[`'\"\\n\-*#\[\]]", "", raw_query)
-
-        # Defensive Guardrail: Ensure query fits under Tavily's 400-character ceiling
+        optimized_query = re.sub(r"[`'\"\\n\-*#\[\]]", "", raw_query).replace("python", "").strip()
         if len(optimized_query) > 390:
-            # Option 1: Extract just the first line or clip the characters safely
-            optimized_query = optimized_query[:390].rpartition(' ')[0] 
-            
-        # Clean up any residual markdown symbols the model leaked
-        optimized_query = optimized_query.replace("`", "").replace("python", "").strip()
-
-        print(f"\nlen optimized_query: {len(optimized_query)}")  # Debug log for query length
-        print(f"\nOptimized Search Query: '{optimized_query}'")  # Debug log for the optimized query
+            optimized_query = optimized_query[:390].rpartition(' ')[0]
 
-        # Executing clean, highly target web search under the 400-character cap
         research_context = web_search(optimized_query)
-
-        #print(f"\nResearch Context Retrieved: {research_context[:500]}...")  
-        print(f"\nResearch Context Retrieved: {research_context}...")  # Debug log for research context snippet
-
         prompt = f"RESEARCH:\n{research_context}\n\nFILES:\n{context_from_files}\n\nUSER: {optimized_query}"
-        #research_context = web_search(user_text)
-        #prompt = f"RESEARCH:\n{research_context}\n\nFILES:\n{context_from_files}\n\nUSER: {user_text}"
     else:
         prompt = f"FILES:\n{context_from_files}\n\nUSER: {user_text}"
 
-    # ====================================================================
-    # 🧠 COGNITIVE INJECTION ENGINE LAYER
-    # ====================================================================
-    # Dynamically read and compile soul.md, heart.md, and memory.md combined 
-    # seamlessly with your complete legacy systemic directives.
+    # Assembling context arrays
     compiled_cognitive_prompt = compile_cognitive_system_prompt()
-    
-    # Build Messages with Dynamic Context Compilations
     messages = [{"role": "system", "content": compiled_cognitive_prompt}]
     
-    # ONLY KEEP LAST 3 TURNS: This is the 'Master Stroke' for staying under 6k TPM
     for turn in history[-3:]:
         messages.append({"role": turn["role"], "content": turn["content"]})
 
-    #messages.append({"role": "user", "content": prompt})
-
     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})
 
-    # =============================================================================================
-    # 🎯DIAGNOSTICS FOR THE LENGTH OF LIST PAYLOAD BEING SENT TO THE PROVIDER, WHICH IT CAN HANDLE
-    # =============================================================================================
-    print("\n==================================================")
-    print(f"📊 Sending {len(messages)} raw message blocks to the {model}.")
-    print("==================================================\n")
-    # ====================================================================
-
     try:
         completion = client.chat.completions.create(
             model=model,
             messages=messages,
             stream=True,
             temperature=0.2,
-            #max_tokens=1024 # Limit response size to prevent mid-stream cuts
         )
 
         response_text = ""
-
-        # Step 1: Stream the raw LLM output token by token to the user
         for chunk in completion:
             if chunk.choices and chunk.choices[0].delta.content:
-                token = chunk.choices[0].delta.content
-                response_text += token
+                response_text += chunk.choices[0].delta.content
                 yield response_text
 
-        # ARTIFACT CHECK: Scan the response text for any code block structures
-        # This matches strings enclosed within triple backticks ```
-        has_code_blocks = bool(re.search(r"```[\s\S]*?```", response_text))
-
-        if has_code_blocks:
-            # ONLY execute file creation and staging alerts if an artifact is detected
-            
-            # Step 2: Transition seamlessly to Local File Generation
+        # 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
@@ -225,9 +158,9 @@ def chat_function(message, history):
             filename = "COURSE_README.md"
             backup_filename = "COURSE_README_-1.md"
             
-            # Proactively manage historical backup copy before writing fresh file state
             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)
@@ -235,18 +168,11 @@ def chat_function(message, history):
                     from agent_logging import log_agent_action
                     log_agent_action("BACKUP_ERROR", f"Failed to cycle historical version file: {str(e)}")
 
-            # Write fresh incoming file generation
             file_path = write_document(response_text, filename)
-            
-            print(f"\nGenerated file at: {file_path}")
-            
-            # Step 3: Inform the UI that the material is staged and ready for the GitHub authorization layer
             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)}"
-
-
+        yield f"Error: {str(e)}"

core_logic_03.py

@@ -0,0 +1,252 @@
+
+# ./core_logic.py -> Token-safe
+
+import os
+import re  # Added for structural artifact code block extraction
+from groq import Groq
+from tools import web_search, parse_file
+
+import yaml
+import toml
+from docx import Document
+
+
+client = Groq(api_key=os.getenv("GROQ_API_KEY"))
+model = "llama-3.1-8b-instant"
+
+# Verify write permissions to 'outputs' directory
+def verify_permissions():
+    test_file = "permission_test.txt"
+    try:
+        with open(test_file, "w") as f:
+            f.write("test")
+        os.remove(test_file)
+        print("✅ Write permissions verified.")
+    except Exception as e:
+        print(f"❌ PERMISSION ERROR: {e}")
+
+verify_permissions()
+
+
+def compile_cognitive_system_prompt():
+    """
+    Cognitive Compilation Layer - Dynamically constructs the master system prompt
+    by assembling soul.md, heart.md, and memory.md side-car layers.
+    """
+    base_soul = ""
+    current_heart = ""
+    past_memory = ""
+    
+    # 1. Gather Soul Directive
+    if os.path.exists("soul.md"):
+        with open("soul.md", "r", encoding="utf-8") as f:
+            base_soul = f.read()
+    else:
+        # Emergency hardcoded fallback matching your architectural profile
+        base_soul = "You are CoderG, the Silicon Architect. Act as an elite Full-stack AI Engineer."
+
+    # 2. Gather Heart 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."
+
+    # 3. Gather Memory Graph
+    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."
+
+    # Combine all layers into a structural system context map
+    master_prompt = f"""{base_soul}
+
+====================================================================
+❤️ ACTIVE OPERATIONAL TASK STATUS (HEART.MD)
+====================================================================
+{current_heart}
+
+====================================================================
+💾 HISTORICAL ENVIRONMENT TRUTHS & PATCHES (MEMORY.MD)
+====================================================================
+{past_memory}
+"""
+    return master_prompt
+
+
+def chat_function(message, history):
+    user_text = message.get("text", "")
+    files = message.get("files", [])
+    
+    # Context Aggregator Buffer for all multi-format assets
+    context_from_files = ""
+    
+    # 1. Process Multimodal and Extended Multi-format Files via Perception Agent
+    if files:
+        from perception_agent import read_document_file
+        yield "◌ _Perception Agent initialized: Ingesting uploaded file assets..._"
+        
+        for f in files:
+            # Gradio 6 handles file entries either as dictionaries with a 'path' key or flat strings
+            path = f["path"] if isinstance(f, dict) else f
+            if path and os.path.exists(path):
+                file_content = read_document_file(path)
+                context_from_files += file_content
+                
+        yield "◌ _Perception processing complete. Transmitting compiled structures to the Brain..._"
+
+    
+    # TRUNCATE FILE CONTEXT: Max ~3000 tokens (approx 12,000 chars)
+    if len(context_from_files) > 12000:
+        context_from_files = context_from_files[:12000] + "\n...[File Content Truncated for TPM Limits]..."
+
+    # 2. Research Trigger
+    if any(keyword in user_text.lower() for keyword in ["search", "docs", "latest"]):
+        # Use a fast micro-turn to distill the massive user prompt into optimized keywords
+        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:\n"
+                    "1. Do NOT answer the user's prompt.\n"
+                    "2. Do NOT write code blocks, code explanations, tasks, or JSON data structures.\n"
+                    "3. Your entire output must be a single sentence under 50 characters.\n"
+                    "4. If the user provides a code file or raw data logs, ignore the text content and generate a query searching for the underlying concept (e.g., 'Scapy network sniffing documentation python').\n"
+                    "5. Output ONLY raw keywords.\n"
+                    "6. NEVER use markdown, backticks, or code blocks.\n"
+                    "7. NEVER wrap your output in single or double quotes.\n"
+                    "8. Maximum 5 words, under 50 characters total."
+                )
+            },
+            {
+                "role": "user", 
+                "content": f"Convert the following request into raw optimized search keywords based on your system rules:\n\n{user_text}"
+            }
+        ],
+        temperature=0.0,
+    )
+
+        # Extract and aggressively sanitize the string programmatically
+        raw_query = distill_response.choices[0].message.content.strip()
+        # Strip away any lingering quotes, backticks, or markdown syntax characters
+        optimized_query = re.sub(r"[`'\"\\n\-*#\[\]]", "", raw_query)
+
+        # Defensive Guardrail: Ensure query fits under Tavily's 400-character ceiling
+        if len(optimized_query) > 390:
+            # Option 1: Extract just the first line or clip the characters safely
+            optimized_query = optimized_query[:390].rpartition(' ')[0] 
+            
+        # Clean up any residual markdown symbols the model leaked
+        optimized_query = optimized_query.replace("`", "").replace("python", "").strip()
+
+        print(f"\nlen optimized_query: {len(optimized_query)}")  # Debug log for query length
+        print(f"\nOptimized Search Query: '{optimized_query}'")  # Debug log for the optimized query
+
+        # Executing clean, highly target web search under the 400-character cap
+        research_context = web_search(optimized_query)
+
+        #print(f"\nResearch Context Retrieved: {research_context[:500]}...")  
+        print(f"\nResearch Context Retrieved: {research_context}...")  # Debug log for research context snippet
+
+        prompt = f"RESEARCH:\n{research_context}\n\nFILES:\n{context_from_files}\n\nUSER: {optimized_query}"
+        #research_context = web_search(user_text)
+        #prompt = f"RESEARCH:\n{research_context}\n\nFILES:\n{context_from_files}\n\nUSER: {user_text}"
+    else:
+        prompt = f"FILES:\n{context_from_files}\n\nUSER: {user_text}"
+
+    # ====================================================================
+    # 🧠 COGNITIVE INJECTION ENGINE LAYER
+    # ====================================================================
+    # Dynamically read and compile soul.md, heart.md, and memory.md combined 
+    # seamlessly with your complete legacy systemic directives.
+    compiled_cognitive_prompt = compile_cognitive_system_prompt()
+    
+    # Build Messages with Dynamic Context Compilations
+    messages = [{"role": "system", "content": compiled_cognitive_prompt}]
+    
+    # ONLY KEEP LAST 3 TURNS: This is the 'Master Stroke' for staying under 6k TPM
+    for turn in history[-3:]:
+        messages.append({"role": turn["role"], "content": turn["content"]})
+
+    #messages.append({"role": "user", "content": prompt})
+
+    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})
+
+    # =============================================================================================
+    # 🎯DIAGNOSTICS FOR THE LENGTH OF LIST PAYLOAD BEING SENT TO THE PROVIDER, WHICH IT CAN HANDLE
+    # =============================================================================================
+    print("\n==================================================")
+    print(f"📊 Sending {len(messages)} raw message blocks to the {model}.")
+    print("==================================================\n")
+    # ====================================================================
+
+    try:
+        completion = client.chat.completions.create(
+            model=model,
+            messages=messages,
+            stream=True,
+            temperature=0.2,
+            #max_tokens=1024 # Limit response size to prevent mid-stream cuts
+        )
+
+        response_text = ""
+
+        # Step 1: Stream the raw LLM output token by token to the user
+        for chunk in completion:
+            if chunk.choices and chunk.choices[0].delta.content:
+                token = chunk.choices[0].delta.content
+                response_text += token
+                yield response_text
+
+        # ARTIFACT CHECK: Scan the response text for any code block structures
+        # This matches strings enclosed within triple backticks ```
+        has_code_blocks = bool(re.search(r"```[\s\S]*?```", response_text))
+
+        if has_code_blocks:
+            # ONLY execute file creation and staging alerts if an artifact is detected
+            
+            # Step 2: Transition seamlessly to Local File Generation
+            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"
+            
+            # Proactively manage historical backup copy before writing fresh file state
+            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)}")
+
+            # Write fresh incoming file generation
+            file_path = write_document(response_text, filename)
+            
+            print(f"\nGenerated file at: {file_path}")
+            
+            # Step 3: Inform the UI that the material is staged and ready for the GitHub authorization layer
+            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)}"
+
+