Delete app.py

app.py

@@ -1,53 +0,0 @@
-import gradio as gr
-import numpy as np
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-from sklearn.preprocessing import StandardScaler
-import json
-import re
-from konlpy.tag import Okt
-from tensorflow.keras.preprocessing.text import tokenizer_from_json
-
-model = load_model('deep_learning_model(okt_drop).h5')
-
-with open('tokenizer(okt_drop).json', 'r', encoding='utf-8') as f:
-    tokenizer_data = f.read()
-
-tokenizer = tokenizer_from_json(tokenizer_data)
-
-def calculate_sentence_stats(paragraph):
-    paragraph = re.sub(r'\.{2,}', '.', paragraph)
-    sentences = re.split(r'[.!?]', paragraph)
-    sentence_lengths = [len(s.strip()) for s in sentences if s.strip()]
-    sentence_count = len(sentence_lengths)
-    average_length = sum(sentence_lengths) / len(sentence_lengths) if sentence_lengths else 0
-    return sentence_count, average_length
-
-def process_text(text):
-    okt = Okt()
-    texts = ' '.join(okt.nouns(text))
-    sequences = tokenizer.texts_to_sequences([texts])
-    max_len = 301
-    X = pad_sequences(sequences, maxlen=max_len)
-    return X
-
-def predict_text(text, grade):
-    X = process_text(text)
-    sentence_count, sentence_average = calculate_sentence_stats(text)
-    length = len(text)
-    emoticon = 0
-    numeric_features = np.array([[int(grade), length, emoticon, sentence_count, sentence_average]])
-    scaler = StandardScaler()
-    numeric_features = scaler.fit_transform(numeric_features)
-    prediction = model.predict([X, numeric_features])
-    predicted_label = '인공지능이 생성한 독서감상문입니다.' if prediction[0][0] > 0.5 else '사람이 작성한 독서감상문입니다.'
-    return predicted_label
-
-iface = gr.Interface(
-    fn=predict_text,
-    inputs=[gr.Textbox(lines=10, placeholder="Enter Text Here..."), gr.Textbox(label="Grade")],
-    outputs="text",
-    title="독서감상문 분석기",
-    description="이 독서감상문이 학생에 의해 작성되었는지, 인공지능에 의해 생성되었는지 분석합니다."
-)
-iface.launch()