Update app.py

app.py

@@ -1,8 +1,7 @@
 import gradio as gr
-from transformers import DistilBertTokenizer, TFDistilBertModel
 import numpy as np
 import tensorflow as tf
-from sklearn.model_selection import train_test_split
+from transformers import DistilBertTokenizer, TFDistilBertModel
 import pandas as pd
 
 # Load your data
@@ -16,50 +15,8 @@ labels = df["label"]
 distil_bert_tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
 distil_bert_model = TFDistilBertModel.from_pretrained('distilbert-base-uncased')
 
-# Tokenize sentences
-max_len = 40
-input_ids = []
-attention_masks = []
-
-for sent in sentences:
-    distil_bert_input_shape = distil_bert_tokenizer.encode_plus(sent, add_special_tokens=True, max_length=max_len, pad_to_max_length=True, return_attention_mask=True, truncation=True)
-    input_ids.append(distil_bert_input_shape['input_ids'])
-    attention_masks.append(distil_bert_input_shape['attention_mask'])
-
-input_ids = np.array(input_ids)
-attention_masks = np.array(attention_masks)
-
-# Split data into train and test sets
-X_train_input, X_test_input, Y_train_label, Y_test_label, train_mask, test_mask = train_test_split(input_ids, labels, attention_masks, test_size=0.3, random_state=42, shuffle=True)
-
-# Define model architecture
-def create_model():
-    input_shape = tf.keras.Input(shape=(max_len,), dtype='int32')
-    masks = tf.keras.Input(shape=(max_len,), dtype='int32')
-    distil_bert_layer = distil_bert_model(input_ids=input_shape, attention_mask=masks)[0]
-    X = tf.keras.layers.GRU(128, return_sequences=True)(distil_bert_layer)
-    X = tf.keras.layers.GlobalMaxPool1D()(X)
-    X = tf.keras.layers.Dense(64, activation="tanh")(X)
-    X = tf.keras.layers.Dense(32)(X)
-    X = tf.keras.layers.Dense(16)(X)
-    X = tf.keras.layers.Dense(8)(X)
-    X = tf.keras.layers.Dense(4)(X)
-    X = tf.keras.layers.Dropout(0.5)(X)
-    X = tf.keras.layers.Dense(2, activation='softmax')(X)
-    model = tf.keras.Model(inputs=[input_shape, masks], outputs=X)
-    return model
-
-# Create model instance
-model = create_model()
-
-# Compile model
-loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
-metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')
-optimizer = tf.keras.optimizers.Adam(learning_rate=2e-5)
-model.compile(loss=loss, optimizer=optimizer, metrics=[metric])
-
-# Train model
-history = model.fit([X_train_input, train_mask], Y_train_label, batch_size=32, epochs=2, validation_data=([X_test_input, test_mask], Y_test_label))
+# Load the saved model
+model = tf.keras.models.load_model("bangla_fake.h5")
 
 # Define Gradio interface
 def classify_bangla_fake_news(description):