Update README.md

README.md

@@ -13,6 +13,9 @@ tags:
 ---
 
 ![zfdggzdrg.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/DPx-7s4BmG_XocnPQ4TR9.png)
+# **Human-Action-Recognition**
+
+> **Human-Action-Recognition** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for multi-class human action recognition. It uses the **SiglipForImageClassification** architecture to predict human activities from still images.
 
 ```py
 Classification Report:
@@ -39,4 +42,99 @@ listening_to_music     0.8494    0.7988    0.8233       840
       weighted avg     0.8421    0.8327    0.8339     12600
 ```
 
-![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/O9ir2VwHirB-T75ABCP7m.png)
+![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/O9ir2VwHirB-T75ABCP7m.png)
+
+The model categorizes images into 15 action classes:
+
+- **0:** calling  
+- **1:** clapping  
+- **2:** cycling  
+- **3:** dancing  
+- **4:** drinking  
+- **5:** eating  
+- **6:** fighting  
+- **7:** hugging  
+- **8:** laughing  
+- **9:** listening_to_music  
+- **10:** running  
+- **11:** sitting  
+- **12:** sleeping  
+- **13:** texting  
+- **14:** using_laptop  
+
+---
+
+# **Run with Transformers 🤗**
+
+```python
+!pip install -q transformers torch pillow gradio
+```
+
+```python
+import gradio as gr
+from transformers import AutoImageProcessor, SiglipForImageClassification
+from PIL import Image
+import torch
+
+# Load model and processor
+model_name = "prithivMLmods/Human-Action-Recognition"  # Change to your updated model path
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# ID to Label mapping
+id2label = {
+    0: "calling",
+    1: "clapping",
+    2: "cycling",
+    3: "dancing",
+    4: "drinking",
+    5: "eating",
+    6: "fighting",
+    7: "hugging",
+    8: "laughing",
+    9: "listening_to_music",
+    10: "running",
+    11: "sitting",
+    12: "sleeping",
+    13: "texting",
+    14: "using_laptop"
+}
+
+def classify_action(image):
+    """Predicts the human action in the 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()
+
+    predictions = {id2label[i]: round(probs[i], 3) for i in range(len(probs))}
+    return predictions
+
+# Gradio interface
+iface = gr.Interface(
+    fn=classify_action,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(label="Action Prediction Scores"),
+    title="Human Action Recognition",
+    description="Upload an image to recognize the human action (e.g., dancing, calling, sitting, etc.)."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+# **Intended Use**
+
+The **Human-Action-Recognition** model is designed to detect and classify human actions from images. Example applications:
+
+- **Surveillance & Monitoring:** Recognizing suspicious or specific activities in public spaces.  
+- **Sports Analytics:** Identifying player activities or movements.  
+- **Social Media Insights:** Understanding trends in user-posted visuals.  
+- **Healthcare:** Monitoring elderly or patients for activity patterns.  
+- **Robotics & Automation:** Enabling context-aware AI systems with visual understanding.