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OpenAI_Originality_GUI.py

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+"""
+OpenAI Embedding 기반 독창성 측정 (Gradio GUI)
+
+사용법:
+    pip install gradio openai numpy nltk
+    python OpenAI_Originality_GUI.py
+"""
+
+import numpy as np
+import gradio as gr
+from openai import OpenAI
+from nltk.tokenize import sent_tokenize, word_tokenize
+import nltk
+
+# NLTK 데이터 다운로드
+try:
+    nltk.data.find('tokenizers/punkt_tab')
+except LookupError:
+    nltk.download('punkt_tab')
+
+
+def cosine_distance(v1, v2):
+    dot = np.dot(v1, v2)
+    norm = np.linalg.norm(v1) * np.linalg.norm(v2)
+    similarity = dot / norm if norm > 0 else 0
+    return 1 - similarity
+
+
+def get_embeddings(client, texts, model="text-embedding-3-large"):
+    response = client.embeddings.create(input=texts, model=model)
+    return [item.embedding for item in response.data]
+
+
+def calculate_sem_div(client, text):
+    sentences = sent_tokenize(text)
+    if len(sentences) < 2:
+        return 0.0, sentences
+    embeddings = get_embeddings(client, sentences)
+    distances = []
+    for i in range(len(sentences)):
+        for j in range(i):
+            dist = cosine_distance(embeddings[i], embeddings[j])
+            distances.append(dist)
+    return np.mean(distances), sentences
+
+
+def calculate_lex_div(text):
+    tokens = word_tokenize(text.lower())
+    tokens = [t for t in tokens if t.isalpha()]
+    if len(tokens) == 0:
+        return 0.0, 0, 0
+    unique_tokens = set(tokens)
+    return len(unique_tokens) / len(tokens), len(unique_tokens), len(tokens)
+
+
+def analyze_originality(api_key, passage_a, passage_b):
+    if not api_key.strip():
+        return "Error: OpenAI API 키를 입력하세요."
+    if not passage_a.strip() or not passage_b.strip():
+        return "Error: 두 단락 모두 입력하세요."
+
+    try:
+        client = OpenAI(api_key=api_key.strip())
+
+        # Passage A 분석
+        sem_div_a, sentences_a = calculate_sem_div(client, passage_a)
+        lex_div_a, unique_a, total_a = calculate_lex_div(passage_a)
+        score_a = 0.50 * sem_div_a + 0.50 * lex_div_a
+
+        # Passage B 분석
+        sem_div_b, sentences_b = calculate_sem_div(client, passage_b)
+        lex_div_b, unique_b, total_b = calculate_lex_div(passage_b)
+        score_b = 0.50 * sem_div_b + 0.50 * lex_div_b
+
+        # 차이 계산
+        diff = score_a - score_b
+        lower_score = min(score_a, score_b)
+        diff_percent = (abs(diff) / lower_score) * 100 if lower_score > 0 else 0
+
+        # 판정
+        if diff_percent < 5:
+            judgment = "비슷함"
+        elif diff_percent < 10:
+            judgment = "차이 있음"
+        elif diff_percent < 15:
+            judgment = "유의미한 차이"
+        else:
+            judgment = "확실한 차이"
+
+        # 결과 텍스트
+        result = f"""
+{'='*50}
+                   분석 결과
+{'='*50}
+
+{'항목':<15} {'Passage A':>15} {'Passage B':>15}
+{'-'*50}
+{'문장 수':<15} {len(sentences_a):>15} {len(sentences_b):>15}
+{'고유 단어':<15} {unique_a:>15} {unique_b:>15}
+{'전체 단어':<15} {total_a:>15} {total_b:>15}
+{'-'*50}
+{'sem_div':<15} {sem_div_a:>15.4f} {sem_div_b:>15.4f}
+{'lex_div':<15} {lex_div_a:>15.4f} {lex_div_b:>15.4f}
+{'독창성 점수':<15} {score_a:>15.4f} {score_b:>15.4f}
+
+{'='*50}
+                   차이 비율
+{'='*50}
+점수 차이: {abs(diff):.4f}
+차이 비율: {diff_percent:.1f}%
+판정: {judgment}
+
+{'='*50}
+                   최종 판정
+{'='*50}
+"""
+        if diff_percent < 5:
+            result += f"두 텍스트의 독창성은 비슷함 (차이 {diff_percent:.1f}%)"
+        elif diff > 0:
+            result += f"Passage A가 더 독창적 (차이 {diff_percent:.1f}%, {judgment})"
+        else:
+            result += f"Passage B가 더 독창적 (차이 {diff_percent:.1f}%, {judgment})"
+
+        return result
+
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+# Gradio 인터페이스
+demo = gr.Interface(
+    fn=analyze_originality,
+    inputs=[
+        gr.Textbox(label="OpenAI API Key", type="password", placeholder="sk-..."),
+        gr.Textbox(label="Passage A", lines=8, placeholder="첫 번째 단락 입력..."),
+        gr.Textbox(label="Passage B", lines=8, placeholder="두 번째 단락 입력...")
+    ],
+    outputs=gr.Textbox(label="분석 결과", lines=25),
+    title="OpenAI Embedding 독창성 분석",
+    description="두 단락의 독창성을 비교합니다. (sem_div 50% + lex_div 50%)",
+    flagging_mode="never"
+)
+
+if __name__ == "__main__":
+    demo.launch()

OpenAI_README.txt

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+# OpenAI Embedding Originality Analyzer
+
+Compare the originality of two text passages using OpenAI's text-embedding-3-large model.
+
+## Metrics
+
+- **sem_div (50%)**: Semantic diversity - mean cosine distance between sentence embeddings
+- **lex_div (50%)**: Lexical diversity - unique tokens / total tokens
+- **Originality Score**: Weighted sum of sem_div and lex_div
+
+## Requirements
+
+- Python 3.8+
+- OpenAI API Key (https://platform.openai.com/api-keys)
+
+## Installation
+
+In command prompt:
+
+```
+pip install gradio openai numpy nltk
+```
+
+## Usage
+
+1. Download `OpenAI_Originality_GUI.py`
+
+2. Run in command prompt:
+```
+python OpenAI_Originality_GUI.py
+```
+
+3. Browser opens automatically (or go to http://127.0.0.1:7860)
+
+4. Enter:
+   - OpenAI API Key (sk-...)
+   - Passage A (first paragraph)
+   - Passage B (second paragraph)
+
+5. Click Submit
+
+6. View results
+
+## Interpretation
+
+| Difference | Judgment |
+|------------|----------|
+| < 5% | Similar |
+| 5% - 10% | Some difference |
+| 10% - 15% | Significant difference |
+| > 15% | Clear difference |
+
+## Model Information
+
+- Model: text-embedding-3-large
+- Embedding dimension: 3072
+- Provider: OpenAI
+
+## Notes
+
+- API costs apply (check OpenAI pricing)
+- Threshold values are empirically derived from pilot testing
+- Scores are relative comparisons, not absolute measures
+
+## License
+
+Apache-2.0
+
+## Citation
+
+If you use this tool, please acknowledge:
+- OpenAI text-embedding-3-large model
+- NLTK for tokenization