app.py

import gradio as gr
import torch
import numpy as np
from PIL import Image
import torchvision.transforms as T
import segmentation_models_pytorch as smp

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

# Load model architecture
model = smp.Unet(
    encoder_name="resnet34",
    encoder_weights="imagenet",
    in_channels=1,  # Grayscale input
    classes=1       # Binary output
).to(device)

# Load trained weights
model.load_state_dict(torch.load("./trained-models/unet_fibril_seg_model.pth", map_location=device))
model.eval()

# Image preprocessing
transform = T.Compose([
    T.Resize((512, 512)),
    T.ToTensor(),
    T.Normalize(mean=(0.5,), std=(0.5,))
])

# Inference function
def segment_fibrils(image):
    image = image.convert("L")  # Grayscale
    input_tensor = transform(image).unsqueeze(0).to(device)

    with torch.no_grad():
        output = model(input_tensor)
        output = torch.sigmoid(output).squeeze().cpu().numpy()

    # Postprocess mask
    output_mask = (output > 0.5).astype(np.uint8) * 255
    return Image.fromarray(output_mask)

# Launch Gradio app
demo = gr.Interface(
    fn=segment_fibrils,
    inputs=gr.Image(type="pil", label="Upload Fibril Image"),
    outputs=gr.Image(type="pil", label="Predicted Segmentation Mask"),
    title="Fibril Segmentation Encoder (ResNet34) and Decoder (UNet)",
    description="Upload a grayscale fibril image to get the segmentation mask."
)

demo.launch(server_name="0.0.0.0", server_port=7860, share=True)