Create codriao_tb_module.py

codriao_tb_module.py

@@ -0,0 +1,45 @@
+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"),
+        }