Update README.md

README.md

@@ -16,6 +16,9 @@ tags:
 - Siglip2
 - ViT
 ---
+# **Coral-Health**
+
+> **Coral-Health** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a single-label classification task. It is designed to classify coral reef images into two health conditions using the **SiglipForImageClassification** architecture.
 
 ```py
 Classification Report:
@@ -30,3 +33,73 @@ Bleached Corals     0.8677    0.7561    0.8081      4850
 ```
 
 ![download (1).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/KMlOnMf0JTq1-5_7qGhjL.png)
+
+The model categorizes images into two classes:
+
+- **Class 0:** Bleached Corals  
+- **Class 1:** Healthy Corals  
+
+---
+
+# **Run with Transformers 🤗**
+
+```python
+!pip install -q transformers torch pillow gradio
+```
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor
+from transformers import SiglipForImageClassification
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/Coral-Health"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Updated labels
+labels = {
+    "0": "Bleached Corals",
+    "1": "Healthy Corals"
+}
+
+def coral_health_detection(image):
+    """Predicts the health condition of coral reefs in the 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()
+
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=coral_health_detection,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Coral Health Detection",
+    description="Upload an image of coral reefs to classify their condition as Bleached or Healthy."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+# **Intended Use:**
+
+The **Coral-Health** model is designed to support marine conservation and environmental monitoring. Potential use cases include:
+
+- **Coral Reef Monitoring:** Helping scientists and conservationists track coral bleaching events.  
+- **Environmental Impact Assessment:** Analyzing reef health in response to climate change and pollution.  
+- **Educational Tools:** Raising awareness about coral reef health in classrooms and outreach programs.  
+- **Automated Drone/ROV Analysis:** Enhancing automated underwater monitoring workflows.