Update README.md

README.md

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----
-library_name: tf-keras
-license: mit
-metrics:
-- accuracy
-pipeline_tag: image-classification
----
+# 🌍 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
+
+```bash
+# 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)
+
+```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
+