app.py

import gradio as gr
import torch
import numpy as np
import cv2
from arch import SegFormerUNet
from albumentations import Compose, Resize, Normalize
from albumentations.pytorch import ToTensorV2

# Load model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = SegFormerUNet().to(device)
checkpoint = torch.load("model/segformer_unet_focal_loss_97_63.pth", map_location=device)
model.load_state_dict(checkpoint)
model.eval()

# Transform
transform = Compose([
    Resize(256, 256),
    Normalize(mean=[0.5], std=[0.5]),
    ToTensorV2()
])

def process_image(image):
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    transformed = transform(image=image)['image'].unsqueeze(0).to(device)
    with torch.no_grad():
        output = model(transformed)
        pred_mask = torch.sigmoid(output).squeeze().cpu().numpy()
        pred_mask = (pred_mask > 0.5).astype(np.uint8)
    area_m2 = np.sum(pred_mask) * (0.125 ** 2)
    energy_kwh = area_m2 * 0.19 * 1676.2 * 0.935 / 1000
    return pred_mask * 255, f"Estimated Solar Panel Area: {area_m2:.2f} m²", f"Estimated Energy Output: {energy_kwh:.2f} MWh per year"

# Gradio Interface
demo = gr.Interface(
    fn=process_image,
    inputs=gr.Image(type="numpy", value="PV08_315125_1194663.bmp"),  # <-- default image here
    outputs=[gr.Image(type="numpy"), gr.Text(), gr.Text()],
    title="Solar Panel Segmentation",
    description="Upload an image to detect solar panels and estimate energy output."
)

demo.launch()