Update app.py

app.py

@@ -3,6 +3,7 @@ import numpy as np
 import pandas as pd
 import re
 from tensorflow.keras.models import Sequential
+from tensorflow.keras.models import load_model
 from tensorflow.keras.layers import Dense
 from transformers import BertTokenizer, TFBertModel
 from sklearn.model_selection import train_test_split
@@ -18,9 +19,6 @@ nltk.download('stopwords')
 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):
@@ -33,65 +31,9 @@ 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")
+# Load the trained model
+classifier=load_model('movie_sentiment_model.h5')
 
 # Single input BERT embeddings
 def bert_embeddings(text, max_length=64):