Upload app.py

app.py

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+import tensorflow as tf
+import gradio as gr
+import pandas as pd
+import os
+from tensorflow.keras.layers import TextVectorization
+
+df = pd.read_csv('train.csv')
+
+X = df['comment_text']
+y = df[df.columns[2:]].values
+
+MAX_FEATURES = 200000 # number of words in the vocab
+
+vectorizer = TextVectorization(max_tokens=MAX_FEATURES,
+                              output_sequence_length=1800,
+                              output_mode='int')
+
+vectorizer.adapt(X.values)
+
+model = tf.keras.models.load_model('hate_speech.h5')
+
+def score_comment(comment):
+    vectorized_comment = vectorizer([comment])
+    results = model.predict(vectorized_comment)
+    
+    for idx, col in enumerate(df.columns[2:]):
+        if results[0][idx]>0.5:
+            return 'Hate Speech detected'
+        
+    return 'No hate speech detected'
+
+interface = gr.Interface(fn=score_comment,
+                        inputs=gr.Textbox(lines=2, placeholder='Comment to score'),
+                        outputs='text')
+
+interface.launch(share=True)