Update README.md

README.md

@@ -17,4 +17,51 @@ tags:
 - voice
 - nlp
 - real-time
----
+---
+
+## Usage
+
+```python
+import numpy as np
+import onnxruntime as ort
+from transformers import AutoTokenizer
+from huggingface_hub import hf_hub_download
+import time
+
+class SaudiEOU:
+    def __init__(self, repo_id="mohamedsamyy/Saudi-EOU"):
+        print(f"Loading model from repo: {repo_id}")
+        model_path = hf_hub_download(repo_id=repo_id, filename="Saudi_EOU.onnx")
+        self.tokenizer = AutoTokenizer.from_pretrained(repo_id)
+        self.session = ort.InferenceSession(model_path, providers=["CUDAExecutionProvider"])
+        self.max_length = 128
+        print("✅ Model and tokenizer loaded successfully.")
+
+    def predict(self, text: str) -> tuple:
+        inputs = self.tokenizer(text, truncation=True, max_length=self.max_length, return_tensors="np")
+        feed_dict = {"input_ids": inputs["input_ids"], "attention_mask": inputs["attention_mask"]}
+        start = time.perf_counter()
+        outputs = self.session.run(None, feed_dict)
+        logits = outputs[0][0]
+        confidence = self._sigmoid(logits[0])
+        end = time.perf_counter()
+        print(f"'{text}' -> latency: {end - start:.4f}s")
+        predicted_label = 1 if confidence >= 0.5 else 0
+        return predicted_label, confidence
+
+    def _sigmoid(self, x):
+        return 1 / (1 + np.exp(-x))
+
+# Example usage
+detector = SaudiEOU()
+sentences = ["حياك الله", "ممم", "اهلا", "يا هلا ", "السلام عليكم"]
+
+for sentence in sentences:
+    predicted_label, confidence = detector.predict(sentence)
+    result = "End of Turn" if predicted_label == 1 else "Not End of Turn"
+    print(f"'{sentence}' -> {result} (confidence: {confidence:.3f})")
+```
+
+
+This example shows how to load the **SaudiEOU** ONNX model from the Hugging Face Hub and predict if a sentence is an end-of-turn utterance.
+The model runs on GPU if available, and prints the latency per sentence.