Delete main.py

main.py

@@ -1,39 +0,0 @@
-# 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()