import torch
import cv2
from PIL import Image
from torchvision import transforms
import numpy as np

# Dummy models – replace with your own
def load_model(path):
    model = torch.load(path, map_location="cpu")
    model.eval()
    return model

thermal_model = load_model("models/thermal_fault_model.pt")
crack_model = load_model("models/crack_detector_model.pt")
dust_model = load_model("models/dust_detector_model.pt")

def detect_faults(image):
    transform = transforms.Compose([
        transforms.ToPILImage(),
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
    ])

    img_tensor = transform(image).unsqueeze(0)

    results = []
    output_img = image.copy()

    # Simulated prediction
    if torch.rand(1).item() > 0.5:
        results.append({
            "type": "Thermal Fault",
            "location": "Panel 12-B",
            "confidence": 0.93
        })
        cv2.putText(output_img, "Thermal Fault", (50, 50),
                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)

    if torch.rand(1).item() > 0.5:
        results.append({
            "type": "Crack Detected",
            "location": "Panel 9-C",
            "confidence": 0.88
        })
        cv2.putText(output_img, "Crack", (150, 100),
                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)

    if torch.rand(1).item() > 0.5:
        results.append({
            "type": "Dust Coverage",
            "location": "Panel 3-A",
            "confidence": 0.91
        })
        cv2.putText(output_img, "Dust", (200, 150),
                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2)

    return results, output_img