Update app.py

app.py

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+import tensorflow as tf
+import numpy as np
+import gradio as gr
+from transformers import BertTokenizer
+
+# تحميل الـ Tokenizer
+tokenizer = BertTokenizer.from_pretrained(".")
+
+# تحميل الموديل
+model = tf.keras.models.load_model("rnn_Bi.h5")
+
+# دالة التنبؤ
+def predict_sentiment(text):
+    if not text.strip():
+        return "⛔ Please enter some text."
+
+    # تجهيز الإدخال
+    tokens = tokenizer(
+        text,
+        padding='max_length',
+        truncation=True,
+        max_length=128,
+        return_tensors="np"
+    )
+    input_ids = tokens["input_ids"]
+    attention_mask = tokens["attention_mask"]
+
+    # التنبؤ
+    prediction = model.predict([input_ids, attention_mask])[0][0]
+    label = int(prediction > 0.5)
+
+    # تحويل النتيجة لنص
+    if label == 1:
+        return "🌟 Positive Sentiment 😊"
+    else:
+        return "😞 Negative Sentiment"
+
+# واجهة Gradio
+interface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(lines=3, placeholder="Write your sentence here...", label="Enter Text"),
+    outputs=gr.Textbox(label="Prediction"),
+    title="Sentiment Analysis - RNN BiLSTM",
+    description="This model predicts whether the input sentence has a positive or negative sentiment.",
+    theme="soft",
+    examples=[
+        ["I love this product!"],
+        ["I am very disappointed with the service."],
+        ["It was okay, not bad."],
+        ["Absolutely fantastic experience!"]
+    ]
+)
+
+interface.launch()