Delete app.py

app.py

@@ -1,451 +0,0 @@
-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
-import cv2  # Added for bilateral filtering
-import matplotlib.pyplot as plt
-from io import BytesIO
-
-# Constants
-IMG_SIZE = 128
-TIMESTEPS = 300  # From second code
-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) * -emb)  # From second code (no dtype specified)
-        self.register_buffer('embeddings', emb)
-
-    def forward(self, time):
-        device = time.device  # From second code
-        embeddings = self.embeddings.to(device)
-        embeddings = time[:, None] * embeddings[None, :]  # From second code
-        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)
-        )
-
-        # Encoder
-        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)
-
-        # Bottleneck
-        self.bottleneck = self.double_conv(512 + time_dim * 2, 1024)
-
-        # Decoder
-        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)
-
-        return self.outc(x)
-
-class DiffusionModel(nn.Module):
-    def __init__(self, model, timesteps=TIMESTEPS, time_dim=256):
-        super().__init__()
-        self.model = model
-        self.timesteps = timesteps
-        self.time_dim = time_dim
-
-        # Linear beta schedule with scaling from second code
-        scale = 1000 / timesteps
-        beta_start = scale * 0.0001
-        beta_end = scale * 0.02
-        self.betas = torch.linspace(beta_start, beta_end, timesteps, dtype=torch.float64)
-        self.alphas = 1. - self.betas
-        self.register_buffer('alpha_bars', torch.cumprod(self.alphas, dim=0).float())
-
-    def forward_diffusion(self, x_0, t, noise):
-        x_0 = x_0.float()
-        noise = noise.float()
-        alpha_bar_t = self.alpha_bars[t].view(-1, 1, 1, 1)
-        x_t = torch.sqrt(alpha_bar_t) * x_0 + torch.sqrt(1. - alpha_bar_t) * noise
-        return x_t
-
-    def forward(self, x_0, labels):
-        t = torch.randint(0, self.timesteps, (x_0.shape[0],), device=x_0.device).long()
-        noise = torch.randn_like(x_0)
-        x_t = self.forward_diffusion(x_0, t, noise)
-        predicted_noise = self.model(x_t, labels, t.float())
-        return predicted_noise, noise, t
-
-    @torch.no_grad()
-    def sample(self, num_images, img_size, num_classes, labels, device, progress_callback=None):
-        # Start with random noise
-        x_t = torch.randn(num_images, 3, img_size, img_size).to(device)
-
-        # Label handling (one-hot if needed)
-        if labels.ndim == 1:
-            labels_one_hot = torch.zeros(num_images, num_classes).to(device)
-            labels_one_hot[torch.arange(num_images), labels] = 1
-            labels = labels_one_hot
-        else:
-            labels = labels.to(device)
-
-        # REVERTED SAMPLING LOOP WITH NOISE REDUCTION
-        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)
-
-            # Calculate coefficients
-            beta_t = self.betas[t].to(device)
-            alpha_t = self.alphas[t].to(device)
-            alpha_bar_t = self.alpha_bars[t].to(device)
-
-            mean = (1 / torch.sqrt(alpha_t)) * (x_t - (beta_t / torch.sqrt(1 - alpha_bar_t)) * predicted_noise)
-            variance = beta_t
-
-            # Reduced noise injection with lower multiplier
-            if t > 0:
-                noise = torch.randn_like(x_t) * 0.8  # Reduced noise by 20%
-            else:
-                noise = torch.zeros_like(x_t)
-
-            x_t = mean + torch.sqrt(variance) * noise
-            
-            if progress_callback:
-                progress_callback((self.timesteps - t) / self.timesteps)
-
-        # Clamp and denormalize
-        x_0 = 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_0 = std * x_0 + mean
-        x_0 = torch.clamp(x_0, 0., 1.)
-
-        # ENHANCED SHARPENING
-        # First apply mild bilateral filtering to reduce noise while preserving edges
-        x_np = x_0.cpu().permute(0, 2, 3, 1).numpy()
-        filtered = []
-        for img in x_np:
-            img = (img * 255).astype(np.uint8)
-            filtered_img = cv2.bilateralFilter(img, d=5, sigmaColor=15, sigmaSpace=15)
-            filtered.append(filtered_img / 255.0)
-        x_0 = torch.tensor(np.array(filtered), device=device).permute(0, 3, 1, 2)
-        
-        # Then apply stronger unsharp masking
-        kernel = torch.ones(3, 1, 5, 5, device=device) / 75
-        kernel = kernel.to(x_0.dtype)
-        blurred = torch.nn.functional.conv2d(
-            x_0,
-            kernel,
-            padding=2,
-            groups=3
-        )
-        x_0 = torch.clamp(1.5 * x_0 - 0.5 * blurred, 0., 1.)  # Increased sharpening factor
-
-        return x_0
-
-def load_model(model_path, device):
-    unet_model = UNet(num_classes=NUM_CLASSES).to(device)
-    diffusion_model = DiffusionModel(unet_model, timesteps=TIMESTEPS).to(device)
-
-    if os.path.exists(model_path):
-        checkpoint = torch.load(model_path, map_location=device)
-        
-        if 'model_state_dict' in checkpoint:
-            # Handle training checkpoint format
-            state_dict = {
-                k[6:]: v for k, v in checkpoint['model_state_dict'].items()
-                if k.startswith('model.')
-            }
-            
-            # Load UNet weights
-            unet_model.load_state_dict(state_dict, strict=False)
-            
-            # Initialize diffusion model with loaded UNet
-            diffusion_model = DiffusionModel(unet_model, timesteps=TIMESTEPS).to(device)
-            
-            print(f"Loaded UNet weights from {model_path}")
-        else:
-            # Handle direct model weights format
-            try:
-                # First try loading full DiffusionModel
-                diffusion_model.load_state_dict(checkpoint)
-                print(f"Loaded full DiffusionModel from {model_path}")
-            except RuntimeError:
-                # If that fails, load just the UNet weights
-                unet_model.load_state_dict(checkpoint, strict=False)
-                diffusion_model = DiffusionModel(unet_model, timesteps=TIMESTEPS).to(device)
-                print(f"Loaded UNet weights only from {model_path}")
-    else:
-        print(f"Weights file not found at {model_path}")
-        print("Using randomly initialized weights")
-
-    diffusion_model.eval()
-    return diffusion_model
-
-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")
-
-    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 grayscale and apply bone colormap
-            img_np = img.cpu().permute(1, 2, 0).mean(dim=-1).numpy()
-            
-            # Normalize to 0-1
-            img_np = (img_np - img_np.min()) / (img_np.max() - img_np.min() + 1e-8)
-            
-            # Apply additional sharpening with OpenCV
-            img_np_uint8 = (img_np * 255).astype(np.uint8)
-            
-            # Apply unsharp mask for additional sharpness
-            blurred = cv2.GaussianBlur(img_np_uint8, (0, 0), 2.0)
-            sharpened = cv2.addWeighted(img_np_uint8, 1.5, blurred, -0.5, 0)
-            
-            # Apply bone colormap using matplotlib - FIXED APPROACH
-            # Create a simple bone-like colormap manually to avoid matplotlib issues
-            sharpened_normalized = sharpened / 255.0
-            # Simulate bone colormap: black to white with blueish tones
-            r = np.clip(sharpened_normalized * 1.2 - 0.1, 0, 1)
-            g = np.clip(sharpened_normalized * 1.1 - 0.05, 0, 1)
-            b = np.clip(sharpened_normalized * 1.0 + 0.1, 0, 1)
-            
-            # Combine channels and convert to uint8
-            bone_colored = np.stack([r, g, b], axis=-1)
-            bone_colored_uint8 = (bone_colored * 255).astype(np.uint8)
-            
-            # Create PIL image
-            pil_img = Image.fromarray(bone_colored_uint8)
-            
-            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()
-
-# Load 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}")
-    print("Creating dummy model for demonstration")
-    loaded_model = DiffusionModel(UNet(num_classes=NUM_CLASSES), timesteps=TIMESTEPS).to(device)
-
-# Gradio UI (from first code)
-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)