app.py

import gradio as gr
from transformers import SegformerForSemanticSegmentation, SegformerImageProcessor
from PIL import Image
import torch
import numpy as np

# Load processor
processor = SegformerImageProcessor(do_reduce_labels=False)

# Load pretrained SegFormer structure
model = SegformerForSemanticSegmentation.from_pretrained(
    "nvidia/segformer-b0-finetuned-ade-512-512",
    num_labels=7,
    ignore_mismatched_sizes=True
)

# Load trained weights from file in same directory
model.load_state_dict(torch.load("trained_model.pt", map_location=torch.device("cpu")))
model.eval()

# Prediction function
def segment_image(input_image):
    inputs = processor(images=input_image, return_tensors="pt")

    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits

    pred_mask = torch.argmax(logits, dim=1)[0].cpu().numpy()
    normalized_mask = (pred_mask * (255 // 7)).astype(np.uint8)  # 7 classes

    output_image = Image.fromarray(normalized_mask)

    # Resize mask (3x)
    scale_factor = 3
    new_size = (output_image.width * scale_factor, output_image.height * scale_factor)
    bigger_output = output_image.resize(new_size, resample=Image.NEAREST)

    return bigger_output

# Gradio Interface with submit button
demo = gr.Interface(
    fn=segment_image,
    inputs=gr.Image(type="pil", label="Upload Blood Smear Image"),
    outputs=gr.Image(type="pil", label="Predicted Grayscale Mask"),
    title="Malaria Blood Smear Segmentation (Custom SegFormer)",
    description="Upload a blood smear image. This app uses a custom trained SegFormer model (7 malaria-specific classes) to segment it.",
    examples=["1.png", "2.png", "3.png"],  # examples in same directory
    live=False
)

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