# intent_recognition.py
from llm_router import LLMRouter

class ChainOfThoughtIntentRecognizer:
    def __init__(self, llm_router: LLMRouter):
        self.llm_router = llm_router
        self.cot_templates = self._load_cot_templates()
        
    async def recognize_intent(self, user_input: str, context: dict) -> dict:
        """
        Multi-step reasoning for intent recognition
        """
        # Step 1: Initial classification
        initial_analysis = await self._step1_initial_classification(user_input)
        
        # Step 2: Contextual refinement
        refined_analysis = await self._step2_contextual_refinement(
            user_input, initial_analysis, context
        )
        
        # Step 3: Confidence calibration
        final_intent = await self._step3_confidence_calibration(refined_analysis)
        
        return self._format_intent_output(final_intent)
    
    async def _step1_initial_classification(self, user_input: str) -> dict:
        cot_prompt = f"""
        Let's think step by step about the user's intent:
        
        User input: "{user_input}"
        
        Step 1: Identify key entities and actions mentioned
        Step 2: Map to common intent categories
        Step 3: Estimate confidence for each category
        
        Categories: [information_request, task_execution, creative_generation, 
                   analysis_research, casual_conversation, troubleshooting]
        """
        
        return await self.llm_router.route_inference(
            "intent_classification", cot_prompt
        )
    
    async def _step2_contextual_refinement(self, user_input: str, initial_analysis: dict, context: dict) -> dict:
        """
        Refine intent classification based on conversation context
        """
        # TODO: Implement contextual refinement using conversation history
        return initial_analysis
    
    async def _step3_confidence_calibration(self, analysis: dict) -> dict:
        """
        Calibrate confidence scores for final intent decision
        """
        # TODO: Implement confidence calibration logic
        return analysis
    
    def _format_intent_output(self, intent_data: dict) -> dict:
        """
        Format intent recognition output with confidence scores
        """
        # TODO: Implement output formatting
        return {
            "intent": intent_data.get("intent", "unknown"),
            "confidence": intent_data.get("confidence", 0.0),
            "reasoning_steps": intent_data.get("reasoning_steps", [])
        }
    
    def _load_cot_templates(self) -> dict:
        """
        Load Chain of Thought templates for different intent types
        """
        return {
            "information_request": """Let's analyze: {user_input}
Step 1: What information is the user seeking?
Step 2: Is it factual, procedural, or explanatory?
Step 3: What level of detail is appropriate?""",
            
            "task_execution": """Let's analyze: {user_input}
Step 1: What action does the user want to perform?
Step 2: What are the required parameters?
Step 3: Are there any constraints or preferences?""",
            
            "creative_generation": """Let's analyze: {user_input}
Step 1: What type of creative content is needed?
Step 2: What style, tone, and format?
Step 3: What constraints or guidelines apply?"""
        }