Update README.md

README.md

@@ -2,8 +2,28 @@
 license: apache-2.0
 datasets:
 - TheNetherWatcher/DisasterClassification
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-512
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- SigLIP2
+- Flood-Detection
+- Disaster-Detection
+- climate
 ---
 
+![2.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/kBMZ3tkdVCN8O0z-FkNuO.png)
+
+# Flood-Image-Detection
+
+> Flood-Image-Detection is a vision-language encoder model fine-tuned from `google/siglip2-base-patch16-512` for **binary image classification**. It is trained to detect whether an image contains a **flooded scene** or **non-flooded** environment. The model uses the `SiglipForImageClassification` architecture.
+
+> [!note]
+SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features :  https://arxiv.org/pdf/2502.14786
+
 ```py
 Classification Report:
                precision    recall  f1-score   support
@@ -17,3 +37,82 @@ Flooded Scene     0.9172    0.9458    0.9313       609
  ```
 
 ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/T-KTVwt2YWoEjg6cB_rgh.png)
+
+
+---
+
+## Label Space: 2 Classes
+
+```
+Class 0: Flooded Scene  
+Class 1: Non Flooded
+```
+
+---
+
+## Install Dependencies
+
+```bash
+pip install -q transformers torch pillow gradio hf_xet
+```
+
+---
+
+## Inference Code
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor, SiglipForImageClassification
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/flood-image-detection"  # Update with actual model name on Hugging Face
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Updated label mapping
+id2label = {
+    "0": "Flooded Scene",
+    "1": "Non Flooded"
+}
+
+def classify_image(image):
+    image = Image.fromarray(image).convert("RGB")
+    inputs = processor(images=image, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
+
+    prediction = {
+        id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
+    }
+
+    return prediction
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=2, label="Flood Detection"),
+    title="Flood-Image-Detection",
+    description="Upload an image to detect whether the scene is flooded or not."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## Intended Use
+
+`Flood-Image-Detection` is designed for:
+
+* **Disaster Monitoring** – Rapid detection of flood-affected areas from imagery.
+* **Environmental Analysis** – Track flooding patterns across regions using image datasets.
+* **Crisis Response** – Assist emergency services in identifying critical zones.
+* **Surveillance and Safety** – Monitor infrastructure or locations for flood exposure.
+* **Smart Alert Systems** – Integrate with IoT or camera feeds for automated flood alerts.