Update README.md

README.md

@@ -65,23 +65,20 @@ For a detailed example of how to load and use this model, please refer to the Co
 ## Ethical Considerations
 
 As with any language model, care should be taken when deploying this for real-world applications. Potential biases present in the training data could be reflected in the translations. It's important to monitor its output and ensure fair and accurate use.
-# ==============================
-# LinguaFlow – English to Arabic Translator
-# ==============================
+## 🚀 How to use
+
+```python
 from huggingface_hub import snapshot_download
 import tensorflow as tf
+import numpy as np
+import os
 from tensorflow.keras.preprocessing.text import tokenizer_from_json
 from tensorflow.keras.preprocessing.sequence import pad_sequences
-import os
-import numpy as np
 
-# ------------------------------
-# ------------------------------
 repo_id = "Ali0044/LinguaFlow"
-local_dir = snapshot_download(repo_id=repo_id)  # لو الريبو خاص، استخدم token="HF_TOKEN"
+local_dir = snapshot_download(repo_id=repo_id)
 
-model_path = os.path.join(local_dir, "Translation_model_for_hf.keras")
-model = tf.keras.models.load_model(model_path)
+model = tf.keras.models.load_model(os.path.join(local_dir, "Translation_model_for_hf.keras"))
 
 with open(os.path.join(local_dir, "tokenizer/eng_tokenizer.json"), "r", encoding="utf-8") as f:
     eng_tokenizer = tokenizer_from_json(f.read())
@@ -89,32 +86,18 @@ with open(os.path.join(local_dir, "tokenizer/eng_tokenizer.json"), "r", encoding
 with open(os.path.join(local_dir, "tokenizer/ar_tokenizer.json"), "r", encoding="utf-8") as f:
     ar_tokenizer = tokenizer_from_json(f.read())
 
-# ------------------------------
-# ------------------------------
-def encode_text(texts, tokenizer, max_len):
-    seq = tokenizer.texts_to_sequences(texts)
-    return pad_sequences(seq, maxlen=max_len, padding='post')
-
-def sequences_to_text(sequences, tokenizer):
-    texts = []
-    for seq in sequences:
-        temp = [tokenizer.index_word[idx] for idx in seq if idx != 0]
-        texts.append(' '.join(temp))
-    return texts
-
-# ------------------------------
-# ------------------------------
 def translate(sentences):
-    X_input = encode_text(sentences, eng_tokenizer, model.input_shape[1])
-    preds = model.predict(X_input)
+    seq = eng_tokenizer.texts_to_sequences(sentences)
+    padded = pad_sequences(seq, maxlen=model.input_shape[1], padding='post')
+    preds = model.predict(padded)
     preds = np.argmax(preds, axis=-1)
-    return sequences_to_text(preds, ar_tokenizer)
-
-# ------------------------------
-# ------------------------------
-sample_texts = ["Hello, how are you?", "I love machine learning!"]
-translations = translate(sample_texts)
-
-for en, ar in zip(sample_texts, translations):
-    print(f"English: {en}")
-    print(f"Arabic : {ar}")
+    
+    results = []
+    for s in preds:
+        text = [ar_tokenizer.index_word[i] for i in s if i != 0]
+        results.append(' '.join(text))
+    return results
+
+# Example
+print(translate(["Hello, how are you?"]))
+"""