import torch
from PIL import Image
from classes.genomic_plip_model import GenomicPLIPModel 
from classes.binary_neural_classifier import SimpleNN
from transformers import CLIPImageProcessor

def load_and_preprocess_image(image_path, clip_processor_path):
    clip_processor = CLIPImageProcessor.from_pretrained(clip_processor_path)
    image = Image.open(image_path).convert("RGB")
    inputs = clip_processor(images=[image], return_tensors="pt")
    image_tensor = inputs['pixel_values']
    return image_tensor

def genomic_plip_predictions(image_tensor, model_path):
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    gmodel = GenomicPLIPModel.from_pretrained(model_path).to(device)
    gmodel.eval()
    with torch.no_grad():
        pred_data = gmodel(image_tensor.to(device))
    return pred_data

def classify_tiles(pred_data, model_path):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = SimpleNN().to(device)
    model.load_state_dict(torch.load(model_path, map_location=device))
    model.eval()
    with torch.no_grad():
        output = model(pred_data).mean()
    return output.item()