Update app.py

app.py

@@ -1,23 +1,47 @@
+import gradio as gr
+import numpy as np
+import torch
+from transformers import BertTokenizer, AutoModel
 import tensorflow as tf
-from transformers import BertTokenizer
 
-# تحميل tokenizer من الملفات اللي في نفس المسار
-tokenizer = BertTokenizer.from_pretrained(".")
+# Load tokenizer and BERT model for embeddings extraction
+model_name = "aubmindlab/bert-base-arabertv02"
+tokenizer = BertTokenizer.from_pretrained(model_name)
+bert_model = AutoModel.from_pretrained(model_name)
+bert_model.eval()
 
-# تحميل الموديل
+# Load your trained RNN model
 model = tf.keras.models.load_model("rnn_Bi.h5")
 print("✅ Model loaded successfully!")
 
-# تجربة بسيطة لتنبؤ
-test_sentence = "I love this movie!"
-tokens = tokenizer(
-    test_sentence,
-    padding='max_length',
-    truncation=True,
-    max_length=128,
-    return_tensors="np"
+def get_bert_embedding(text, max_length=100):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=max_length)
+    with torch.no_grad():
+        outputs = bert_model(**inputs)
+    # Use CLS token embedding as sentence embedding
+    embedding = outputs.last_hidden_state[:, 0, :].numpy()
+    embedding = embedding.reshape(1, 1, 768)  # shape (1, 1, 768)
+    return embedding
+
+def predict_sentiment(text):
+    embedding = get_bert_embedding(text)
+    pred = model.predict(embedding)[0][0]
+    label = "Positive" if pred > 0.5 else "Negative"
+    confidence = pred if pred > 0.5 else 1 - pred
+    return label, f"Confidence: {confidence:.2f}"
+
+# Build Gradio interface
+iface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(lines=2, placeholder="اكتب الجملة هنا..."),
+    outputs=[
+        gr.Label(num_top_classes=2, label="Sentiment"),
+        gr.Textbox(label="Confidence Score")
+    ],
+    title="Arabic Sentiment Analysis",
+    description="اكتب جملة لتحليل المشاعر (إيجابي أو سلبي)",
+    theme="default"
 )
 
-input_ids = tokens["input_ids"]
-prediction = model.predict(input_ids)[0][0]
-print(f"Prediction: {prediction}")
+if __name__ == "__main__":
+    iface.launch()