Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import tensorflow as tf
+from tensorflow.keras.layers import TextVectorization
+
+df = pd.read_csv('train.csv')
+
+X=df['comment_text']
+
+vectorizer = TextVectorization(max_tokens=250000,
+                               output_sequence_length=300,
+                               output_mode='int')
+vectorizer.adapt(X)
+
+#load the model
+model = tf.keras.models.load_model('CT_epoch_3.h5')
+
+
+def score_comment(comment):
+    # Vectorize the input comment
+    vectorized_comment = vectorizer([comment])
+    # Predict using the loaded model
+    results = model.predict(vectorized_comment)
+    
+    # Generate the output text based on predictions
+    text = ''
+    for idx, col in enumerate(df.columns[2:]):  # Adjust the range if necessary
+        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()