Create app.py

tensorflow , sklearn

app.py

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+import tensorflow as tf
+import tensorflow_hub as hub
+import numpy as np
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from sklearn.preprocessing import LabelEncoder
+from tensorflow.keras.layers import LSTM, GRU, Embedding
+
+# تحميل نموذج BERT
+module_url = "https://tfhub.dev/google/bert_uncased_L-12_H-768_A-12/1"
+bert_layer = hub.KerasLayer(module_url, trainable=False)
+
+# بيانات تدريب متنوعة
+texts = ["I am happy", "I feel sad", "This is great", "I am angry", "I love this", "I am confused", "Feeling ecstatic today!"]
+labels = ["positive", "negative", "positive", "negative", "positive", "neutral", "positive"]
+
+# تحويل النصوص إلى أرقام باستخدام Tokenizer
+tokenizer = tf.keras.preprocessing.text.Tokenizer()
+tokenizer.fit_on_texts(texts)
+sequences = tokenizer.texts_to_sequences(texts)
+padded_sequences = pad_sequences(sequences, padding='post')
+
+# ترميز الأهداف (المشاعر)
+label_encoder = LabelEncoder()
+encoded_labels = label_encoder.fit_transform(labels)
+
+# بناء نموذج باستخدام BERT و LSTM
+model = tf.keras.Sequential([
+    bert_layer,
+    LSTM(128, return_sequences=False),  # إضافة LSTM
+    tf.keras.layers.Dense(64, activation='relu'),
+    tf.keras.layers.Dense(1, activation='sigmoid')
+])
+
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
+# تدريب النموذج
+model.fit(padded_sequences, np.array(encoded_labels), epochs=10)
+
+# اختبار النموذج
+test_text = ["I am feeling excited"]
+test_sequence = tokenizer.texts_to_sequences(test_text)
+test_padded = pad_sequences(test_sequence, padding='post', maxlen=10)
+
+# التنبؤ بالمشاعر
+pred = model.predict(test_padded)
+pred_label = label_encoder.inverse_transform([int(pred[0][0] > 0.5)])[0]
+
+print(f"Predicted Emotion: {pred_label}")