🌍 Disaster Prediction Model

This repository contains a deep learning model for disaster classification from images, capable of identifying six disaster-related categories including fire, water damage, infrastructure damage, and more. The model is built using ResNet50 and trained for image classification tasks.

🧠 Model Details

Architecture: ResNet50
Trained On: Kaggle Disaster Dataset
Image Size: 256x256
Input: RGB image
Output: Disaster category
License: MIT
Pipeline Tag: image-classification
Main Metric: Accuracy

📦 Installation & Cloning

# Install Git LFS (if not already installed)
git lfs install

# Clone the repository
git clone https://huggingface.co/Luwayy/disaster-prediction

🔍 Classes

The model predicts one of the following disaster categories:

ID	Class Name
0	Damaged_Infrastructure
1	Fire_Disaster
2	Human_Damage
3	Land_Disaster
4	Non_Damage
5	Water_Disaster

📸 Example Usage (Python)

import keras
import numpy as np
from PIL import Image
import requests
from io import BytesIO

# Load the model
model = keras.layers.TFSMLayer(
    "disaster-prediction/kaggle/working/disaster_model",
    call_endpoint="serving_default"
)

# Load and preprocess image
url = 'https://www.spml.co.in/Images/blog/wdt&c-152776632.jpg'
response = requests.get(url)
img = Image.open(BytesIO(response.content)).convert("RGB").resize((256, 256))
img_array = np.array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0).astype(np.float32)

# Predict
output = model(img_array)
preds = list(output.values())[0].numpy()
pred_index = np.argmax(preds)

# Class labels
labels = [
    "Damaged_Infrastructure",
    "Fire_Disaster",
    "Human_Damage",
    "Land_Disaster",
    "Non_Damage",
    "Water_Disaster"
]

print("Predicted class:", labels[pred_index])

⚙️ Preprocessing

Resize: (256, 256)
Scale: Normalize pixel values by dividing by 255