Update app.py

app.py

@@ -1,9 +1,9 @@
 import pandas as pd
 import numpy as np
 import gradio as gr 
-
 import tensorflow as tf
-from tensorflow.keras.layers import TextVectorization
+from tensorflow.keras.layers import TextVectorization, LSTM
+from tensorflow.keras.models import load_model
 
 df = pd.read_csv('https://raw.githubusercontent.com/whitehatjr1001/comment-toxicity-detecttion-/main/jigsaw-toxic-comment-classification-challenge/train.csv/train.csv')
 
@@ -12,14 +12,13 @@ y = df[df.columns[2:]].values
 
 max_features = 2000000
 
-vecterizor = TextVectorization(max_tokens=max_features,output_sequence_length=1800,output_mode='int')
-
+vecterizor = TextVectorization(max_tokens=max_features, output_sequence_length=1800, output_mode='int')
 vecterizor.adapt(X.values)
 
 model_path = 'commenttoxicity (1).h5'
 
-model = tf.keras.models.load_model(model_path)
-
+# Load the model with custom_objects
+model = load_model(model_path, custom_objects={'LSTM': LSTM})
 
 def score_comment(comment):
     vectorized_comment = vecterizor([comment])
@@ -33,6 +32,6 @@ def score_comment(comment):
 
 interface = gr.Interface(fn=score_comment,
                          inputs=gr.Textbox(lines=2, placeholder='Comment to score'),
-                        outputs='text')
+                         outputs='text')
 
 interface.launch(share=True)