Create Movie-Review-Sentiment.py

Movie-Review-Sentiment.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+import re
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from transformers import BertTokenizer, TFBertModel
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import confusion_matrix, classification_report
+from nltk.corpus import stopwords
+import tensorflow as tf
+import nltk
+
+# Download stopwords
+nltk.download('stopwords')
+
+# Load tokenizer and model
+tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+bert_model = TFBertModel.from_pretrained("bert-base-uncased")
+
+# Load dataset
+file_path = "https://raw.githubusercontent.com/alexvatti/full-stack-data-science/main/NLP-Exercises/Movie-Review/IMDB%20Dataset.csv"
+movies_df = pd.read_csv(file_path)
+
+# Clean text
+def remove_tags(txt):
+    result = re.sub(r'<[^>]+>', '', txt)
+    result = re.sub(r'https?://\S+', '', result)
+    result = re.sub(r'[^a-zA-Z0-9\s]', ' ', result)
+    return result.lower()
+
+def remove_stop_words(txt):
+    stop_words = set(stopwords.words('english'))
+    return ' '.join([word for word in txt.split() if word not in stop_words])
+
+movies_df['review'] = movies_df['review'].apply(remove_tags)
+movies_df['review'] = movies_df['review'].apply(remove_stop_words)
+movies_df['Category'] = movies_df['sentiment'].apply(lambda x: 1 if x == 'positive' else 0)
+
+# Train-test split
+X_train, X_test, y_train, y_test = train_test_split(movies_df['review'], movies_df['Category'], test_size=0.2, random_state=42)
+
+# Convert labels to TensorFlow format
+y_train = tf.convert_to_tensor(y_train.values, dtype=tf.float32)
+y_test = tf.convert_to_tensor(y_test.values, dtype=tf.float32)
+
+# Compute BERT embeddings
+def bert_embeddings_batch(texts, batch_size=32, max_length=64):
+    embeddings = []
+    for i in range(0, len(texts), batch_size):
+        batch_texts = texts[i:i + batch_size]
+        inputs = tokenizer(
+            batch_texts.tolist(),
+            return_tensors="tf",
+            padding=True,
+            truncation=True,
+            max_length=max_length
+        )
+        outputs = bert_model(inputs['input_ids'], attention_mask=inputs['attention_mask'])
+        cls_embeddings = outputs.last_hidden_state[:, 0, :]
+        embeddings.append(cls_embeddings.numpy())
+    return np.vstack(embeddings)
+
+# Compute embeddings
+X_train_embeddings = bert_embeddings_batch(X_train)
+X_test_embeddings = bert_embeddings_batch(X_test)
+
+# Define classifier
+classifier = Sequential([
+    Dense(128, activation='relu', input_shape=(768,)),
+    Dense(1, activation='sigmoid')
+])
+
+classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
+# Train classifier
+classifier.fit(X_train_embeddings, y_train, epochs=5, batch_size=32, validation_split=0.1)
+
+# Evaluate
+test_loss, test_accuracy = classifier.evaluate(X_test_embeddings, y_test)
+print(f"Test Accuracy: {test_accuracy}")
+
+# Predictions and confusion matrix
+y_pred = (classifier.predict(X_test_embeddings) > 0.5).astype("int32")
+conf_matrix = confusion_matrix(y_test.numpy(), y_pred)
+class_report = classification_report(y_test.numpy(), y_pred)
+
+print("Confusion Matrix:")
+print(conf_matrix)
+print("\nClassification Report:")
+print(class_report)
+
+# Save the trained model to a file
+classifier.save("movie_sentiment_model.h5")
+