Update app.py

app.py

@@ -1,57 +1,64 @@
-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
-import pickle
-
-# 모델 및 토크나이저 파일 로드
-model = load_model('deep_learning_model(okt_drop).h5', compile=False)
-
-with open('tokenizer(okt_drop).json', 'r', encoding='utf-8') as f:
-    tokenizer_data = f.read()
-
-tokenizer = tokenizer_from_json(tokenizer_data)
-
-with open('scaler.pkl', 'rb') as f:
-    scaler = pickle.load(f)
-
-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]])
-    numeric_features = scaler.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(debug=True)
+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
+import pickle
+import jpype
+import os
+
+# JVM 초기화
+if not jpype.isJVMStarted():
+    jpype.startJVM(jpype.getDefaultJVMPath(), "-Djava.class.path=/usr/local/lib/python3.10/site-packages/konlpy/java/lib/komoran.jar", "-ea")
+
+# 모델 및 토크나이저 파일 로드
+model = load_model('deep_learning_model(okt_drop).h5', compile=False)
+
+with open('tokenizer(okt_drop).json', 'r', encoding='utf-8') as f:
+    tokenizer_data = f.read()
+
+tokenizer = tokenizer_from_json(tokenizer_data)
+
+with open('scaler.pkl', 'rb') as f:
+    scaler = pickle.load(f)
+
+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]])
+    numeric_features = scaler.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(debug=True)
+