app.py

import torch
import torch.nn as nn
import gradio as gr
from PIL import Image
import numpy as np
import math
import os
from threading import Event
import traceback

# Constants
IMG_SIZE = 128
TIMESTEPS = 500
NUM_CLASSES = 2

# Global Cancellation Flag
cancel_event = Event()

# Device Configuration
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# --- Model Definitions ---
class SinusoidalPositionEmbeddings(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.dim = dim
        half_dim = dim // 2
        emb = math.log(10000) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
        self.register_buffer('embeddings', emb)

    def forward(self, time):
        embeddings = self.embeddings.to(time.device)
        embeddings = time.float()[:, None] * embeddings[None, :]
        return torch.cat([embeddings.sin(), embeddings.cos()], dim=-1)

class UNet(nn.Module):
    def __init__(self, in_channels=3, out_channels=3, num_classes=2, time_dim=256):
        super().__init__()
        self.num_classes = num_classes
        self.label_embedding = nn.Embedding(num_classes, time_dim)

        self.time_mlp = nn.Sequential(
            SinusoidalPositionEmbeddings(time_dim),
            nn.Linear(time_dim, time_dim),
            nn.ReLU(),
            nn.Linear(time_dim, time_dim)
        )

        self.inc = self.double_conv(in_channels, 64)
        self.down1 = self.down(64 + time_dim * 2, 128)
        self.down2 = self.down(128 + time_dim * 2, 256)
        self.down3 = self.down(256 + time_dim * 2, 512)

        self.bottleneck = self.double_conv(512 + time_dim * 2, 1024)

        self.up1 = nn.ConvTranspose2d(1024, 256, kernel_size=2, stride=2)
        self.upconv1 = self.double_conv(256 + 256 + time_dim * 2, 256)

        self.up2 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
        self.upconv2 = self.double_conv(128 + 128 + time_dim * 2, 128)

        self.up3 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
        self.upconv3 = self.double_conv(64 + 64 + time_dim * 2, 64)

        self.outc = nn.Conv2d(64, out_channels, kernel_size=1)

    def double_conv(self, in_channels, out_channels):
        return nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.ReLU(inplace=True)
        )

    def down(self, in_channels, out_channels):
        return nn.Sequential(
            nn.MaxPool2d(2),
            self.double_conv(in_channels, out_channels)
        )

    def forward(self, x, labels, time):
        label_indices = torch.argmax(labels, dim=1)
        label_emb = self.label_embedding(label_indices)
        t_emb = self.time_mlp(time)

        combined_emb = torch.cat([t_emb, label_emb], dim=1)
        combined_emb = combined_emb.unsqueeze(-1).unsqueeze(-1)

        x1 = self.inc(x)
        x1_cat = torch.cat([x1, combined_emb.repeat(1, 1, x1.shape[-2], x1.shape[-1])], dim=1)

        x2 = self.down1(x1_cat)
        x2_cat = torch.cat([x2, combined_emb.repeat(1, 1, x2.shape[-2], x2.shape[-1])], dim=1)

        x3 = self.down2(x2_cat)
        x3_cat = torch.cat([x3, combined_emb.repeat(1, 1, x3.shape[-2], x3.shape[-1])], dim=1)

        x4 = self.down3(x3_cat)
        x4_cat = torch.cat([x4, combined_emb.repeat(1, 1, x4.shape[-2], x4.shape[-1])], dim=1)

        x5 = self.bottleneck(x4_cat)

        x = self.up1(x5)
        x = torch.cat([x, x3], dim=1)
        x = torch.cat([x, combined_emb.repeat(1, 1, x.shape[-2], x.shape[-1])], dim=1)
        x = self.upconv1(x)

        x = self.up2(x)
        x = torch.cat([x, x2], dim=1)
        x = torch.cat([x, combined_emb.repeat(1, 1, x.shape[-2], x.shape[-1])], dim=1)
        x = self.upconv2(x)

        x = self.up3(x)
        x = torch.cat([x, x1], dim=1)
        x = torch.cat([x, combined_emb.repeat(1, 1, x.shape[-2], x.shape[-1])], dim=1)
        x = self.upconv3(x)

        output = self.outc(x)
        return output

class DiffusionModel(nn.Module):
    def __init__(self, model, timesteps=TIMESTEPS):
        super().__init__()
        self.model = model
        self.timesteps = timesteps
        
        # Use the exact same noise schedule as Colab
        beta_start = 0.0001
        beta_end = 0.02
        self.betas = torch.linspace(beta_start, beta_end, timesteps, dtype=torch.float32)
        self.alphas = 1. - self.betas
        self.register_buffer('alpha_bars', torch.cumprod(self.alphas, dim=0))

    @torch.no_grad()
    def sample(self, num_images, img_size, num_classes, labels, device, progress_callback=None):
        """Identical implementation to Colab version"""
        # Start with random noise (same scale)
        x_t = torch.randn((num_images, 3, img_size, img_size), device=device)
        
        # Identical label handling
        if labels.ndim == 1:
            labels = torch.zeros(num_images, num_classes, device=device).scatter_(1, labels.unsqueeze(1), 1)
        labels = labels.to(device)

        # Same sampling loop
        for t in reversed(range(self.timesteps)):
            if cancel_event.is_set():
                return None
                
            t_tensor = torch.full((num_images,), t, device=device, dtype=torch.float)
            predicted_noise = self.model(x_t, labels, t_tensor)

            # Identical coefficients calculation
            beta_t = self.betas[t].to(device)
            alpha_t = self.alphas[t].to(device)
            alpha_bar_t = self.alpha_bars[t].to(device)

            # Same mean/variance calculation
            mean = (1 / torch.sqrt(alpha_t)) * (x_t - (beta_t / torch.sqrt(1 - alpha_bar_t)) * predicted_noise)
            variance = beta_t

            if t > 0:
                noise = torch.randn_like(x_t)
            else:
                noise = torch.zeros_like(x_t)

            x_t = mean + torch.sqrt(variance) * noise
            
            if progress_callback:
                progress_callback((self.timesteps - t) / self.timesteps)

        # Identical denormalization
        x_t = torch.clamp(x_t, -1., 1.)
        mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(device)
        std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(device)
        x_t = std * x_t + mean
        x_t = torch.clamp(x_t, 0., 1.)
        
        return x_t

    def _post_process(self, images):
        """Apply post-processing to reduce noise and enhance contrast"""
        # Normalize to [0,1]
        images = torch.clamp(images, -1, 1)
        images = (images + 1) / 2
        
        # Apply mild blur (convert NHWC to NCHW for conv2d)
        if images.dim() == 4 and images.shape[-1] != 3:  # NCHW format
            images = images.permute(0, 2, 3, 1)
            
        x = images.permute(0, 3, 1, 2)  # NHWC to NCHW
        x = torch.nn.functional.conv2d(x, self.blur_kernel, padding=1, groups=3)
        images = x.permute(0, 2, 3, 1)  # NCHW to NHWC
        
        # Contrast adjustment
        mean_val = images.mean(dim=(1,2,3), keepdim=True)
        images = (images - mean_val) * 1.2 + mean_val
        
        return torch.clamp(images, 0, 1)
    
def load_model(model_path, device):
    unet = UNet(num_classes=NUM_CLASSES).to(device)
    diffusion_model = DiffusionModel(unet).to(device)
    
    if os.path.exists(model_path):
        try:
            checkpoint = torch.load(model_path, map_location=device)
            
            # Handle both full model and state_dict loading
            if 'model_state_dict' in checkpoint:
                state_dict = checkpoint['model_state_dict']
            else:
                state_dict = checkpoint
            
            # Handle both prefixed and non-prefixed state dicts
            if all(k.startswith('model.') for k in state_dict.keys()):
                state_dict = {k[6:]: v for k, v in state_dict.items()}
            
            unet.load_state_dict(state_dict, strict=False)
            print("Model loaded successfully")
            
            # Verify model loading
            test_input = torch.randn(1, 3, IMG_SIZE, IMG_SIZE).to(device)
            test_labels = torch.zeros(1, NUM_CLASSES).to(device)
            test_time = torch.tensor([1]).to(device)
            output = unet(test_input, test_labels, test_time)
            print(f"Model test output shape: {output.shape}")
            
        except Exception as e:
            traceback.print_exc()
            raise ValueError(f"Error loading model: {str(e)}")
    else:
        raise FileNotFoundError(f"Model weights not found at {model_path}")
    
    diffusion_model.eval()
    return diffusion_model

MODEL_NAME = "model_weights.pth"
model_path = MODEL_NAME
print("Loading model...")
try:
    loaded_model = load_model(model_path, device)
    print("Model loaded successfully!")
except Exception as e:
    print(f"Failed to load model: {e}")
    # Create a dummy model if loading fails
    print("Creating dummy model for demonstration")
    loaded_model = DiffusionModel(UNet(num_classes=NUM_CLASSES)).to(device)

def cancel_generation():
    cancel_event.set()
    return "Generation cancelled"

def generate_images(label_str, num_images, progress=gr.Progress()):
    global loaded_model
    cancel_event.clear()
    
    if num_images < 1 or num_images > 10:
        raise gr.Error("Number of images must be between 1 and 10")
    
    label_map = {'Pneumonia': 0, 'Pneumothorax': 1}
    if label_str not in label_map:
        raise gr.Error("Invalid condition selected")

    # Create one-hot encoded labels
    labels = torch.zeros(num_images, NUM_CLASSES)
    labels[:, label_map[label_str]] = 1

    try:
        def progress_callback(progress_val):
            progress(progress_val, desc="Generating...")
            if cancel_event.is_set():
                raise gr.Error("Generation was cancelled by user")

        with torch.no_grad():
            images = loaded_model.sample(
                num_images=num_images,
                img_size=IMG_SIZE,
                num_classes=NUM_CLASSES,
                labels=labels,
                device=device,
                progress_callback=progress_callback
            )
        
        if images is None:
            return None, None
            
        processed_images = []
        for img in images:
            # Convert to numpy and permute dimensions (C,H,W) -> (H,W,C)
            img_np = img.cpu().permute(1, 2, 0).numpy()
            img_np = (img_np * 255).clip(0, 255).astype(np.uint8)
            pil_img = Image.fromarray(img_np)
            processed_images.append(pil_img)
        
        if num_images == 1:
            return processed_images[0], processed_images
        else:
            return None, processed_images

    except Exception as e:
        traceback.print_exc()
        raise gr.Error(f"Generation failed: {str(e)}")
    finally:
        torch.cuda.empty_cache()

# Gradio UI
with gr.Blocks(theme=gr.themes.Soft(
    primary_hue="violet",
    neutral_hue="slate",
    font=[gr.themes.GoogleFont("Poppins")],
    text_size="md"
)) as demo:
    gr.Markdown("""
    <center>
    <h1>Synthetic X-ray Generator</h1>
    <p><em>Generate synthetic chest X-rays conditioned on pathology</em></p>
    </center>
    """)
    
    with gr.Row():
        with gr.Column(scale=1):
            condition = gr.Dropdown(
                ["Pneumonia", "Pneumothorax"],
                label="Select Condition",
                value="Pneumonia",
                interactive=True
            )
            num_images = gr.Slider(
                1, 10, value=1, step=1,
                label="Number of Images",
                interactive=True
            )
            
            with gr.Row():
                submit_btn = gr.Button("Generate", variant="primary")
                cancel_btn = gr.Button("Cancel", variant="stop")
            
            gr.Markdown("""
            <div style="text-align: center; margin-top: 10px;">
                <small>Note: Generation may take several seconds per image</small>
            </div>
            """)
        
        with gr.Column(scale=2):
            with gr.Tabs():
                with gr.TabItem("Output", id="output_tab"):
                    single_image = gr.Image(
                        label="Generated X-ray",
                        height=400,
                        visible=True
                    )
                    gallery = gr.Gallery(
                        label="Generated X-rays",
                        columns=3,
                        height="auto",
                        object_fit="contain",
                        visible=False
                    )
    
    def update_ui_based_on_count(num_images):
        if num_images == 1:
            return {
                single_image: gr.update(visible=True),
                gallery: gr.update(visible=False)
            }
        else:
            return {
                single_image: gr.update(visible=False),
                gallery: gr.update(visible=True)
            }
    
    num_images.change(
        fn=update_ui_based_on_count,
        inputs=num_images,
        outputs=[single_image, gallery]
    )
    
    submit_btn.click(
        fn=generate_images,
        inputs=[condition, num_images],
        outputs=[single_image, gallery]
    )
    
    cancel_btn.click(
        fn=cancel_generation,
        outputs=None
    )

    demo.css = """
    .gradio-container {
        background: linear-gradient(135deg, #f5f7fa 0%, #e4e8f0 100%);
    }
    .gallery-container {
        background-color: white !important;
    }
    """

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)