test.py

import torch
import torchvision.transforms as transforms
from PIL import Image
import torch.nn as nn
import torchvision.models as models

class MRBEANVisionModel(nn.Module):
    def __init__(self, num_disaster_classes=6, num_severity_classes=3):
        super(MRBEANVisionModel, self).__init__()
        mobilenet = models.mobilenet_v3_large(weights=None)
        self.features = mobilenet.features
        self.pool = nn.AdaptiveAvgPool2d(1)
        self.flatten = nn.Flatten()

        self.disaster_head = nn.Sequential(
            nn.Linear(960, 256), nn.ReLU(), nn.Dropout(0.3), nn.Linear(256, num_disaster_classes)
        )
        self.severity_head = nn.Sequential(
            nn.Linear(960, 128), nn.ReLU(), nn.Dropout(0.3), nn.Linear(128, num_severity_classes)
        )

    def forward(self, x):
        x = self.features(x)
        x = self.pool(x)
        x = self.flatten(x)
        return self.disaster_head(x), self.severity_head(x)

disaster_labels = {0: 'Earthquake', 1: 'Fire', 2: 'Flood', 3: 'Hurricane', 4: 'Landslide', 5: 'Not a Disaster'}
severity_labels = {0: 'Little or None', 1: 'Mild', 2: 'Severe'}

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = MRBEANVisionModel().to(device)

print("Loading trained weights...")
model.load_state_dict(torch.load("best_multitask_vision_model.pth", map_location=device))
model.eval()

transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

test_image_path = "download.jpg"
image = Image.open(test_image_path).convert('RGB')
input_tensor = transform(image).unsqueeze(0).to(device)

with torch.no_grad():
    out_disaster, out_severity = model(input_tensor)
    _, pred_d = torch.max(out_disaster, 1)
    _, pred_s = torch.max(out_severity, 1)

print("\n--- MRBEAN ANALYSIS RESULTS ---")
print(f"Disaster Type: {disaster_labels[pred_d.item()]}")
print(f"Damage Severity: {severity_labels[pred_s.item()]}")