Update README.md

README.md

@@ -17,6 +17,10 @@ tags:
 
 ![sdsffsdf.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/RZ0c2zsJESPWMy4avejVY.png)
 
+# **Rice-Leaf-Disease** 🌾  
+
+> **Rice-Leaf-Disease** is an image classification model fine-tuned from **google/siglip2-base-patch16-224** for detecting and categorizing diseases in rice leaves. It is built using the **SiglipForImageClassification** architecture and helps in early identification of plant diseases for better crop management.
+> 
 ```py
 Classification Report:
                  precision    recall  f1-score   support
@@ -33,3 +37,74 @@ Bacterialblight     0.8853    0.9596    0.9210      1585
 ```
 
 ![download (1).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/iuXCriQpPXJmLeMy--WJr.png)
+
+### **Disease Categories:**  
+- **Class 0:** Bacterial Blight  
+- **Class 1:** Blast  
+- **Class 2:** Brown Spot  
+- **Class 3:** Healthy  
+- **Class 4:** Tungro  
+
+---
+
+# **Run with Transformers 🤗**  
+
+```python
+!pip install -q transformers torch pillow gradio
+```  
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor, SiglipForImageClassification
+from transformers.image_utils import load_image
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/Rice-Leaf-Disease"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def classify_leaf_disease(image):
+    """Predicts the disease type in a rice leaf 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()
+    
+    labels = {
+        "0": "Bacterial Blight",
+        "1": "Blast",
+        "2": "Brown Spot",
+        "3": "Healthy",
+        "4": "Tungro"
+    }
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=classify_leaf_disease,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Rice Leaf Disease Classification 🌾",
+    description="Upload an image of a rice leaf to identify if it is healthy or affected by diseases like Bacterial Blight, Blast, Brown Spot, or Tungro."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+# **Intended Use:**  
+
+The **Rice-Leaf-Disease** model helps in detecting and classifying rice leaf diseases early, supporting:  
+✅ **Farmers & Agriculturists:** Quick disease detection for better crop management.  
+✅ **Agricultural Research:** Monitoring and analyzing plant disease patterns.  
+✅ **AI & Machine Learning Projects:** Applying AI to real-world agricultural challenges.