Update app.py

app.py

@@ -1,34 +1,34 @@
-import tensorflow as tf
-from tensorflow.keras.layers import TextVectorization,LSTM
-import gradio as gr
-import pandas as pd
-import os
-
-df = pd.read_csv(os.path.join('jigsaw-toxic-comment-classification-challenge','train.csv', 'train.csv'))
-
-MAX_FEATURES = 200000 
-
-vectorizer = TextVectorization(max_tokens=MAX_FEATURES,
-                               output_sequence_length=1800,
-                               output_mode='int')
-
-# Adapt the vectorizer to the training data
-vectorizer.adapt(df['comment_text'].values)
-
-model = tf.keras.models.load_model('toxicity.keras')
-
-def score_comment(comment):
-    vectorized_comment = vectorizer([comment])
-    results = model.predict(vectorized_comment)
-    
-    text = ''
-    for idx, col in enumerate(df.columns[2:]):
-        text += '{}: {}\n'.format(col, results[0][idx]>0.5)
-
-    return text
-
-interface = gr.Interface(fn=score_comment, 
-                         inputs=gr.Textbox(lines=2, placeholder='Comment to score'),
-                        outputs='text')
-
+import tensorflow as tf
+from tensorflow.keras.layers import TextVectorization,LSTM
+import gradio as gr
+import pandas as pd
+import os
+
+df = pd.read_csv('train.csv')
+
+MAX_FEATURES = 200000 
+
+vectorizer = TextVectorization(max_tokens=MAX_FEATURES,
+                               output_sequence_length=1800,
+                               output_mode='int')
+
+# Adapt the vectorizer to the training data
+vectorizer.adapt(df['comment_text'].values)
+
+model = tf.keras.models.load_model('toxicity.keras')
+
+def score_comment(comment):
+    vectorized_comment = vectorizer([comment])
+    results = model.predict(vectorized_comment)
+    
+    text = ''
+    for idx, col in enumerate(df.columns[2:]):
+        text += '{}: {}\n'.format(col, results[0][idx]>0.5)
+
+    return text
+
+interface = gr.Interface(fn=score_comment, 
+                         inputs=gr.Textbox(lines=2, placeholder='Comment to score'),
+                        outputs='text')
+
 interface.launch(share=True)