import io
import logging

# LATER: You will import PyTorch, TensorFlow, or OpenCV here
# from PIL import Image
# import torch

logger = logging.getLogger("uzoagro-diagnostics")


def run_botanical_diagnosis(image_bytes):
    """
    This is the wrapper for your friend's AI model.
    It takes raw image bytes from the API, processes them, and returns a JSON diagnosis.
    """
    try:
        # LATER: This is where your friend's code goes. Example:
        # image = Image.open(io.BytesIO(image_bytes))
        # tensor = my_preprocess_function(image)
        # prediction = my_model.predict(tensor)

        # FOR NOW: Simulated output to test the pipeline and UI
        return {
            "status": "success",
            "detected_disease": "Cassava Mosaic Disease (Simulated)",
            "confidence_score": "94%",
            "traditional_remedy": "Apply concentrated neem leaf extract spray directly to affected leaves at dawn. Isolate and burn severely infected stems to prevent vector spread.",
            "scientific_note": "Transmitted by whiteflies. Consider intercropping with non-host plants."
        }
    except Exception as e:
        logger.error(f"Image processing failed: {e}")
        return {"status": "error", "message": "The AI engine failed to process this image."}