import gradio as gr
import torch
import numpy as np
from PIL import Image
import yaml
import os
from models.unet import UNet

# Configuration
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Map dataset names to config/model paths
CONFIG_PATHS = {
    'isic': './configs/isic/isic2018_unet.yaml',
    'segpc': './configs/segpc/segpc2021_unet.yaml'
}
MODEL_PATHS = {
    'isic': './saved_models/isic2018_unet/best_model_state_dict.pt',
    'segpc': './saved_models/segpc2021_unet/best_model_state_dict.pt'
}

def load_config(config_path):
    with open(config_path, 'r') as f:
        return yaml.safe_load(f)

def load_model(dataset_name):
    config = load_config(CONFIG_PATHS[dataset_name])
    model = UNet(
        in_channels=config['model']['params']['in_channels'],
        out_channels=config['model']['params']['out_channels']
    )
    model_path = MODEL_PATHS[dataset_name]
    if os.path.exists(model_path):
        state_dict = torch.load(model_path, map_location=DEVICE)
        model.load_state_dict(state_dict)
        print(f"Loaded model for {dataset_name} from {model_path}")
    else:
        print(f"Warning: Model weights not found for {dataset_name} at {model_path}")
    
    model.to(DEVICE)
    model.eval()
    return model

# Load models once (cache them)
models = {}
for ds in ['isic', 'segpc']:
    try:
        models[ds] = load_model(ds)
    except Exception as e:
        print(f"Error loading model {ds}: {e}")

def predict(image, dataset_choice):
    if image is None:
        return None
    
    if dataset_choice not in models:
        return None
    
    model = models[dataset_choice]
    
    # Preprocess
    # Resize to 224x224 as per config
    img_resized = image.resize((224, 224))
    img_np = np.array(img_resized).astype(np.float32) / 255.0
    
    # Handle channels
    if dataset_choice == 'isic':
        # ISIC: 3 channels (RGB)
        if img_np.shape[-1] == 4:
            img_np = img_np[:, :, :3]
        img_tensor = torch.from_numpy(img_np).permute(2, 0, 1).unsqueeze(0).float()
    else:
        # SegPC: 4 channels (BMP input often loaded as RGB, need to assume/check)
        if img_np.shape[-1] == 3:
             # Create fake 4th channel
             padding = np.zeros((224, 224, 1), dtype=np.float32)
             img_np = np.concatenate([img_np, padding], axis=-1)
             
        img_tensor = torch.from_numpy(img_np).permute(2, 0, 1).unsqueeze(0).float()

    img_tensor = img_tensor.to(DEVICE)
    
    with torch.no_grad():
        output = model(img_tensor)
        probs = torch.sigmoid(output)
        pred_mask = (probs > 0.5).float().cpu().numpy()[0, 0]
    
    # Post-process for visualization
    # Create an overlay
    base_img = np.array(img_resized)
    overlay = base_img.copy()
    
    # Green mask
    mask_bool = pred_mask > 0
    overlay[mask_bool] = [0, 255, 0] # Make Green
    
    # Blend
    final_img = (0.6 * base_img + 0.4 * overlay).astype(np.uint8)
    
    return final_img

# Interface
iface = gr.Interface(
    fn=predict,
    inputs=[
        gr.Image(type="pil", label="Input Image"),
        gr.Radio(["isic", "segpc"], label="Dataset Model", value="isic")
    ],
    outputs=gr.Image(type="numpy", label="Prediction Overlay"),
    title="Medical Image Segmentation (Awesome-U-Net)",
    description="Upload an image to segment skin lesions (ISIC) or cells (SegPC).",
    examples=[
        # Add example paths if available
        # ["dataset_examples/isic_sample.jpg", "isic"]
    ]
)

if __name__ == "__main__":
    iface.launch()