"""Phase 2: Multi-Modal World Modeling.

Ingests multi-modal data (vision/audio) to build a spatial and temporal
understanding of Timmy's environment.
"""

import logging
import base64
from typing import List, Dict, Any
from agent.gemini_adapter import GeminiAdapter
from agent.symbolic_memory import SymbolicMemory

logger = logging.getLogger(__name__)

class WorldModeler:
    def __init__(self):
        self.adapter = GeminiAdapter()
        self.symbolic = SymbolicMemory()

    def analyze_environment(self, image_data: str, mime_type: str = "image/jpeg"):
        """Analyzes an image of the environment and updates the world model."""
        # In a real scenario, we'd use Gemini's multi-modal capabilities
        # For now, we'll simulate the vision-to-symbolic extraction
        prompt = f"""
Analyze the following image of Timmy's environment.
Identify all key objects, their spatial relationships, and any temporal changes.
Extract this into a set of symbolic triples for the Knowledge Graph.

Format: [{{"s": "subject", "p": "predicate", "o": "object"}}]
"""
        # Simulate multi-modal call (Gemini 3.1 Pro Vision)
        result = self.adapter.generate(
            model="gemini-3.1-pro-preview",
            prompt=prompt,
            system_instruction="You are Timmy's World Modeler. Build a high-fidelity spatial/temporal map of the environment.",
            response_mime_type="application/json"
        )
        
        triples = json.loads(result["text"])
        self.symbolic.ingest_text(json.dumps(triples))
        logger.info(f"Updated world model with {len(triples)} new spatial triples.")
        return triples