Update README.md

README.md

@@ -2,7 +2,11 @@
 license: apache-2.0
 datasets:
 - prithivMLmods/WeatherNet-05
+library_name: transformers
 ---
+# Weather-Image-Classification
+
+> Weather-Image-Classification is a vision-language model fine-tuned from google/siglip2-base-patch16-224 for multi-class image classification. It is trained to recognize weather conditions from images using the SiglipForImageClassification architecture.
 
 ```py
 Classification Report:
@@ -20,3 +24,90 @@ cloudy/overcast     0.8493    0.8762    0.8625      6702
 ```
 
 ![download (1).png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/T3MuycHMZDoAhjp3V5Z0p.png)
+
+---
+
+## Label Space: 5 Classes
+
+The model classifies an image into one of the following weather categories:
+
+```json
+"id2label": {
+  "0": "cloudy/overcast",
+  "1": "foggy/hazy",
+  "2": "rain/storm",
+  "3": "snow/frosty",
+  "4": "sun/clear"
+}
+```
+
+---
+
+## Install Dependencies
+
+```bash
+pip install -q transformers torch pillow gradio
+```
+
+---
+
+## 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/Weather-Image-Classification"  # Replace with actual path
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Label mapping
+id2label = {
+    "0": "cloudy/overcast",
+    "1": "foggy/hazy",
+    "2": "rain/storm",
+    "3": "snow/frosty",
+    "4": "sun/clear"
+}
+
+def classify_weather(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_weather,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=5, label="Weather Condition"),
+    title="Weather-Image-Classification",
+    description="Upload an image to identify the weather condition (sun, rain, snow, fog, or clouds)."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## Intended Use
+
+Weather-Image-Classification is useful for:
+
+* Automated weather tagging for photography and media.
+* Enhancing dataset labeling in weather-related research.
+* Supporting smart surveillance and traffic systems.
+* Improving scene understanding in autonomous vehicles.