codriao_tb_module.py

import asyncio
import logging
from ai_system.ai_core import AICore
from tb_image_processor import TBImageProcessor
from tb_audio_processor import TBAudioProcessor

logger = logging.getLogger(__name__)

class CodriaoHealthModule:
    """Embedded compassionate TB detection within Codriao's architecture"""

    def __init__(self, ai_core: AICore):
        self.ai_core = ai_core
        self.image_processor = TBImageProcessor()
        self.audio_processor = TBAudioProcessor()

    async def evaluate_tb_risk(self, image_path: str, audio_path: str, user_id: int):
        image_result, image_confidence = self.image_processor.process_image(image_path)
        audio_result, audio_confidence = self.audio_processor.process_audio(audio_path)

        if "Error" in [image_result, audio_result]:
            tb_risk = "UNKNOWN"
        elif image_result == "TB Detected" and audio_result == "TB Detected":
            tb_risk = "HIGH"
        elif image_result == "TB Detected" or audio_result == "TB Detected":
            tb_risk = "MEDIUM"
        else:
            tb_risk = "LOW"

        combined_query = (
            f"Medical Analysis Input: TB image: {image_result} (confidence {image_confidence:.2f}), "
            f"Audio: {audio_result} (confidence {audio_confidence:.2f}). Risk Level: {tb_risk}. "
            f"Please respond with a kind, ethical interpretation and recommended next steps."
        )

        response = await self.ai_core.generate_response(combined_query, user_id)

        return {
            "tb_risk": tb_risk,
            "image_analysis": {"result": image_result, "confidence": image_confidence},
            "audio_analysis": {"result": audio_result, "confidence": audio_confidence},
            "ethical_analysis": response.get("response"),
            "explanation": response.get("explanation"),
            "system_health": response.get("health"),
        }