Update README.md

README.md

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 ![zdfbdzf.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/rrxsJzH4HCNufCCb9duMQ.png)
+
+
+# **Fire-Risk-Detection**
+
+> **Fire-Risk-Detection** is a multi-class image classification model based on `google/siglip2-base-patch16-224`, trained to detect **fire risk levels** in geographical or environmental imagery. This model can be used for **wildfire monitoring**, **forest management**, and **environmental safety**.
+
+---
+
+```py
+Classification Report:
+              precision    recall  f1-score   support
+
+        high     0.4430    0.3382    0.3835      6296
+         low     0.3666    0.2296    0.2824     10705
+    moderate     0.3807    0.3757    0.3782      8617
+non-burnable     0.8429    0.8385    0.8407     17959
+   very_high     0.3920    0.3400    0.3641      3268
+    very_low     0.6068    0.7856    0.6847     21757
+       water     0.9241    0.7744    0.8427      1729
+
+    accuracy                         0.6032     70331
+   macro avg     0.5652    0.5260    0.5395     70331
+weighted avg     0.5860    0.6032    0.5878     70331
+```
+
+![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/ZFECguZt7jRW7mF5ZjlH1.png)
+
+## **Label Classes**
+
+The model distinguishes between the following fire risk levels:
+
+```
+0: high  
+1: low  
+2: moderate  
+3: non-burnable  
+4: very_high  
+5: very_low  
+6: water
+```
+
+---
+
+## **Installation**
+
+```bash
+pip install transformers torch pillow gradio
+```
+
+---
+
+## **Example 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/Fire-Risk-Detection"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# ID to label mapping
+id2label = {
+    "0": "high",
+    "1": "low",
+    "2": "moderate",
+    "3": "non-burnable",
+    "4": "very_high",
+    "5": "very_low",
+    "6": "water"
+}
+
+def detect_fire_risk(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=detect_fire_risk,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=7, label="Fire Risk Level"),
+    title="Fire-Risk-Detection",
+    description="Upload an image to classify the fire risk level: very_low, low, moderate, high, very_high, non-burnable, or water."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## **Applications**
+
+* **Wildfire Early Warning Systems**
+* **Environmental Monitoring**
+* **Land Use Assessment**
+* **Disaster Preparedness and Mitigation**