Update src/chimera_core.py

src/chimera_core.py

@@ -5,10 +5,10 @@ from openai import OpenAI
 
 class Chimera:
     def __init__(self):
-        # 1. SETUP GEMINI (Google)
+        # 1. SETUP GEMINI (The Draftsman)
         self.gemini_key = os.getenv("GEMINI_API_KEY")
         if not self.gemini_key:
-            # Fallback for local testing if env var is missing
+            # Fallback for local testing
             try:
                 import config
                 self.gemini_key = config.API_KEY
@@ -18,29 +18,69 @@ class Chimera:
         if self.gemini_key:
             self.gemini_client = genai.Client(api_key=self.gemini_key)
             self.gemini_model = "gemini-2.5-flash"
-            print(f"🦁 Gemini Core: ONLINE [{self.gemini_model}]")
+            print(f"🦁 Gemini Draftsman: ONLINE [{self.gemini_model}]")
         else:
             raise ValueError("❌ CRITICAL: GEMINI_API_KEY missing in Secrets!")
 
-        # 2. SETUP GPT-4o (OpenAI)
+        # 2. SETUP GPT-4o (The Refiner)
         self.openai_key = os.getenv("OPENAI_API_KEY")
         self.openai_client = None
         if self.openai_key:
             self.openai_client = OpenAI(api_key=self.openai_key)
-            print("🟢 OpenAI Core: ONLINE [gpt-4o]")
+            print("🟢 OpenAI Refiner: ONLINE [gpt-4o]")
         else:
-            print("⚠️ OpenAI Key not found. GPT-4o disabled.")
+            print("⚠️ OpenAI Key not found. Dual-Core mode disabled.")
+
+    def _pipeline_code(self, user_prompt):
+        """
+        THE DUAL-CORE PIPELINE:
+        Step 1: Gemini generates a draft.
+        Step 2: GPT-4o refines and perfects it.
+        """
+        if not self.openai_client:
+            return "⚠️ OpenAI Key missing. Cannot run refinement pipeline."
+
+        print("⚡ Starting Dual-Core Pipeline...")
+        
+        # Step 1: Gemini Draft
+        print("   ↳ Phase 1: Gemini Drafting...")
+        draft_prompt = f"Write a Python solution for this task. Be verbose and include comments.\nTask: {user_prompt}"
+        draft_response = self.gemini_client.models.generate_content(
+            model=self.gemini_model,
+            contents=draft_prompt
+        )
+        draft_code = draft_response.text
+
+        # Step 2: GPT-4o Refinement
+        print("   ↳ Phase 2: GPT-4o Refining...")
+        refine_prompt = f"""
+        You are a Senior Principal Engineer. Review the following Junior Developer's code.
+        
+        GOALS:
+        1. Fix any potential bugs.
+        2. Optimize for speed and memory.
+        3. Make variable names professional and clear.
+        4. Add robust error handling.
+        
+        JUNIOR CODE DRAFT:
+        {draft_code}
+        
+        Output ONLY the final, perfect code (with brief explanations).
+        """
+        
+        final_response = self.openai_client.chat.completions.create(
+            model="gpt-4o",
+            messages=[{"role": "user", "content": refine_prompt}]
+        )
+        
+        return final_response.choices[0].message.content
 
     def _route_task(self, prompt):
-        """ The Router: Decides between Gemini (Fast) and GPT-4o (Smart) """
-        # We use Gemini for routing because it's cheaper/faster
+        # Route to ASM (Code) automatically if it looks like code
         routing_prompt = f"""
         Classify this task.
-        [ASM] - Coding/Math (Best for GPT-4o)
-        [SFE] - Data/Science (Best for Gemini)
-        [CSM] - Creative/Story (Best for Gemini)
-        [CHAT] - Casual (Best for Gemini)
-
+        [ASM] - Coding, Math, Python, Algorithms.
+        [CHAT] - Everything else.
         User Task: "{prompt}"
         Reply ONLY with the tag.
         """
@@ -56,48 +96,23 @@ class Chimera:
 
     def process_request(self, user_message, history, manual_role="Auto"):
         # 1. Determine Role
-        if manual_role and manual_role != "Auto":
-            role = manual_role
-        else:
-            role = self._route_task(user_message)
-        
+        role = manual_role if manual_role != "Auto" else self._route_task(user_message)
         print(f"👉 Routing to: [{role}]")
 
-        # 2. Assign Persona & Model
-        system_instruction = ""
-        model_to_use = "Gemini"
-
-        if role == "ASM":
-            system_instruction = "You are the ASM (Abstract Symbology Module). You are an expert Python Developer. Write efficient code."
-            # ASM (Coding) is better with GPT-4o if available
-            if self.openai_client:
-                model_to_use = "OpenAI"
-        
-        elif role == "SFE":
-            system_instruction = "You are the SFE (Sensory Fusion Engine). You are a Data Scientist. Analyze facts objectively."
-        
-        elif role == "CSM":
-            system_instruction = "You are the CSM (Creative Synthesis Module). You are a Novelist. Write with creativity."
-        
-        else:
-            system_instruction = "You are Project Chimera."
-
-        # 3. Execute
+        # 2. EXECUTE
         try:
-            if model_to_use == "OpenAI" and self.openai_client:
-                # Call GPT-4o
-                response = self.openai_client.chat.completions.create(
-                    model="gpt-4o",
-                    messages=[
-                        {"role": "system", "content": system_instruction},
-                        {"role": "user", "content": user_message}
-                    ]
-                )
-                return response.choices[0].message.content, f"{role} (GPT-4o)"
+            # IF IT IS CODE (ASM), USE THE 2-AI PIPELINE
+            if role == "ASM" and self.openai_client:
+                final_code = self._pipeline_code(user_message)
+                return final_code, "ASM (Dual-Core)"
             
+            # OTHERWISE, JUST USE GEMINI (Faster/Cheaper)
             else:
-                # Call Gemini
-                full_prompt = f"System Instruction: {system_instruction}\n\nUser Message: {user_message}"
+                system_instruction = "You are Project Chimera."
+                if role == "SFE": system_instruction = "You are a Data Scientist."
+                if role == "CSM": system_instruction = "You are a Creative Writer."
+
+                full_prompt = f"System: {system_instruction}\nUser: {user_message}"
                 response = self.gemini_client.models.generate_content(
                     model=self.gemini_model,
                     contents=full_prompt