Upload app.py

app.py

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+# Importing Data
+import os
+import pandas as pd
+import tensorflow as tf
+import numpy as np
+import gradio as gr
+
+# Data preparation
+
+df = pd.read_csv(r"train.csv.zip")
+
+# Creating Word Embeddings
+from tensorflow import TextVectorization
+X = df['comment_text']
+y = df[df.columns[2:]].values
+MAX_FEATURES = 200000
+vectorizer = TextVectorization(max_tokens = MAX_FEATURES, output_sequence_length = 1800, output_mode = 'int')
+vectorizer.adapt(X.values)
+vectorized_text = vectorizer(X.values)
+print('Vectorization Complete!')
+
+# Loading The Model
+model = tf.keras.models.load_model('hate_model.h5')
+
+# To display results
+def score_comment(comment):
+    vectorize_comment = vectorizer([comment])
+    results = model.predict(vectorize_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.inputs.Textbox(lines=2, placeholder='Comment to score'),
+                        outputs='text')
+interface.launch()