Update README.md

README.md

@@ -10,4 +10,100 @@ tags:
 - text-generation-inference
 ---
 
-![2.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/CPSV18EisjG36vLoxsavm.png)
+![2.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/CPSV18EisjG36vLoxsavm.png)
+
+# **Face-Confidence-SigLIP2**
+
+> **Face-Confidence-SigLIP2** is a vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for **binary image classification**. It is trained to distinguish between images of **confident faces** and **unconfident faces** using the **SiglipForImageClassification** architecture.
+
+```py
+Classification report:
+
+              precision    recall  f1-score   support
+
+   confident     0.8468    0.8179    0.8321      4872
+ unconfident     0.8691    0.8909    0.8799      6611
+
+    accuracy                         0.8600     11483
+   macro avg     0.8580    0.8544    0.8560     11483
+weighted avg     0.8596    0.8600    0.8596     11483
+```
+
+![download.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/MotjWiRrjx_5sMpR5LGZK.png)
+
+## **Label Space: 2 Classes**
+
+The model classifies each image into one of the following categories:
+
+```
+Class 0: "confident"
+Class 1: "unconfident"
+```
+
+---
+
+## **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/Face-Confidence-SigLIP2"  # Replace with your model path if different
+model = SiglipForImageClassification.from_pretrained(model_name)
+processor = AutoImageProcessor.from_pretrained(model_name)
+
+# Label mapping
+id2label = {
+    "0": "confident",
+    "1": "unconfident"
+}
+
+def classify_face_confidence(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_face_confidence,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=2, label="Face Confidence Classification"),
+    title="Face-Confidence-SigLIP2",
+    description="Upload an image to detect if a face looks confident or unconfident."
+)
+
+if __name__ == "__main__":
+    iface.launch()
+```
+
+---
+
+## **Intended Use**
+
+**Face-Confidence-SigLIP2** can be used for:
+
+* **Behavioral Analysis** – Detect confidence levels in facial expressions.
+* **Education & Training** – Assess learner engagement or self-confidence.
+* **HR & Recruitment** – Analyze non-verbal cues during interviews.
+* **Dataset Curation** – Separate confident vs unconfident facial images for training.