Update README.md

README.md

@@ -1,6 +1,22 @@
 ---
 license: apache-2.0
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- traffic
+- dense
+- classification
 ---
+![dsfsdef.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/bpucqawIvBlE7i0YCG6ba.png)
+
+# **Traffic-Density-Classification**
+> **Traffic-Density-Classification** 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 images into **traffic density** categories using the **SiglipForImageClassification** architecture.  
+
+
 ```py
 Classification Report:
                 precision    recall  f1-score   support
@@ -16,3 +32,68 @@ medium-traffic     0.7785    0.6453    0.7057      1187
 ```
 
 ![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/xatFDNCVZo5jGHW8njTmz.png)
+
+The model categorizes images into the following 4 classes:  
+- **Class 0:** "high-traffic"  
+- **Class 1:** "low-traffic"  
+- **Class 2:** "medium-traffic"  
+- **Class 3:** "no-traffic"  
+
+# **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 transformers.image_utils import load_image
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/Traffic-Density-Classification"
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+def traffic_density_classification(image):
+    """Predicts traffic density category for an 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": "high-traffic", "1": "low-traffic", "2": "medium-traffic", "3": "no-traffic"
+    }
+    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
+    
+    return predictions
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=traffic_density_classification,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Prediction Scores"),
+    title="Traffic Density Classification",
+    description="Upload an image to classify it into one of the 4 traffic density categories."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+# **Intended Use:**  
+
+The **Traffic-Density-Classification** model is designed for traffic image classification. It helps categorize traffic density levels into predefined categories. Potential use cases include:  
+
+- **Traffic Monitoring:** Classifying images from traffic cameras to assess congestion levels.  
+- **Smart City Applications:** Assisting in traffic flow management and congestion reduction strategies.  
+- **Automated Traffic Analysis:** Helping transportation authorities analyze and optimize road usage.  
+- **AI Research:** Supporting computer vision-based traffic density classification models.