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Dockerfile

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+# 베이스 이미지 설정
+FROM python:3.10-slim
+
+# 필수 패키지 설치 및 Java 설치
+RUN apt-get update && \
+    apt-get install -y openjdk-11-jdk curl && \
+    apt-get clean
+
+# 환경 변수 설정
+ENV JAVA_HOME=/usr/lib/jvm/java-11-openjdk-amd64
+ENV PATH=$JAVA_HOME/bin:$PATH
+
+# 작업 디렉토리 설정
+WORKDIR /app
+
+# 필요한 파일들 복사
+COPY app.py /app
+COPY requirements.txt /app
+COPY deep_learning_model(okt_drop).h5 /app
+COPY tokenizer(okt_drop).json /app
+COPY scaler.pkl /app
+
+# 필요한 Python 패키지 설치
+RUN pip install --no-cache-dir -r requirements.txt
+
+# 애플리케이션 실행
+CMD ["python", "app.py"]
+

app.py

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+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)

deep_learning_model(okt_drop).h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f464139189e497a20f91ea5f511ef0e15271f45ac0dcbf4743f9f782ce79413c
+size 4892352

requirements.txt

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+gradio
+numpy
+tensorflow==2.15.0
+scikit-learn
+konlpy
+h5py

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a2679e430d3bfdd5cef34882fef19ac9c143a3355982123d4168687f0696c660
+size 720