Update app.py

app.py

@@ -4,8 +4,8 @@ import tensorflow as tf
 from transformers import DistilBertTokenizer, TFDistilBertModel
 import pandas as pd
 
-# Load your data
-df = pd.read_csv("new_bangla.csv")
+# Load your data from new_bangla.csv
+df = pd.read_csv("new_bangla.csv")  # Update the filename here
 
 # Extract text and label columns
 sentences = df["text"]
@@ -15,8 +15,11 @@ labels = df["label"]
 distil_bert_tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
 distil_bert_model = TFDistilBertModel.from_pretrained('distilbert-base-uncased')
 
-# Load the saved model
-model = tf.keras.models.load_model("bangla_fake.h5")
+# Define custom objects for model loading
+custom_objects = {'TFDistilBertModel': TFDistilBertModel}
+
+# Load the saved model with custom objects
+model = tf.keras.models.load_model("bangla_fake.h5", custom_objects=custom_objects)
 
 # Define Gradio interface
 def classify_bangla_fake_news(description):