Update README.md

README.md

@@ -9,6 +9,61 @@ library_name: transformers
 tags:
 - fire-detection
 ---
+#  **Fire-Detection-Siglip2**  
+
+**Fire-Detection-Siglip2** 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 detect fire, smoke, or normal conditions using the SiglipForImageClassification architecture.  
+
+The model categorizes images into three classes:  
+- **Class 0:** "Fire" – The image shows active fire.  
+- **Class 1:** "Normal" – The image depicts a normal, fire-free environment.  
+- **Class 2:** "Smoke" – The image contains visible smoke, indicating potential fire risk.  
+
+```python
+!pip install -q transformers torch pillow gradio
+```
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor
+from transformers import SiglipForImageClassification
+from transformers.image_utils import load_image
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/Fire-Detection-Siglip2"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def fire_detection(image):
+    """Classifies an image as fire, smoke, or normal conditions."""
+    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 = model.config.id2label
+    predictions = {labels[i]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=fire_detection,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Detection Result"),
+    title="Fire Detection Model",
+    description="Upload an image to determine if it contains fire, smoke, or a normal condition."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
     Classification report:
     
                   precision    recall  f1-score   support
@@ -19,4 +74,14 @@ tags:
     
         accuracy                         0.9941      3030
        macro avg     0.9941    0.9941    0.9941      3030
-    weighted avg     0.9941    0.9941    0.9941      3030
+    weighted avg     0.9941    0.9941    0.9941      3030
+
+# **Intended Use:**  
+
+The **Fire-Detection-Siglip2** model is designed to classify images into three categories: **fire, smoke, or normal conditions**. It helps in early fire detection and environmental monitoring.  
+
+### Potential Use Cases:  
+- **Fire Safety Monitoring:** Detecting fire and smoke in surveillance footage.  
+- **Early Warning Systems:** Helping in real-time fire hazard detection in public and private areas.  
+- **Disaster Prevention:** Assisting emergency response teams by identifying fire-prone areas.  
+- **Smart Home & IoT Integration:** Enhancing automated fire alert systems in smart security setups.