Delete hist.txt

hist.txt

@@ -1,56 +0,0 @@
-
-# 위키 요약 관련
-def extract_main_query(text):
-    sentences = re.split(r'[.?!]\s*', text)
-    sentences = [s.strip() for s in sentences if s.strip()]
-    if not sentences:
-        return text
-    last = sentences[-1]
-    last = re.sub(r'[^가-힣a-zA-Z0-9 ]', '', last)
-    particles = ['이', '가', '은', '는', '을', '를', '의', '에서', '에게', '한테', '보다']
-    for p in particles:
-        last = re.sub(rf'\b(\w+){p}\b', r'\1', last)
-    return last.strip()
-
-def get_wikipedia_summary(query):
-    cleaned_query = extract_main_query(query)
-    url = f"https://ko.wikipedia.org/api/rest_v1/page/summary/{cleaned_query}"
-    res = requests.get(url)
-    if res.status_code == 200:
-        return res.json().get("extract", "요약 정보를 찾을 수 없습니다.")
-    else:
-        return "위키백과에서 정보를 가져올 수 없습니다."
-
-def textrank_summarize(text, top_n=3):
-    sentences = re.split(r'(?<=[.!?])\s+', text.strip())
-    sentences = [s.strip() for s in sentences if len(s.strip()) > 10]
-    if len(sentences) <= top_n:
-        return text
-    vectorizer = TfidfVectorizer()
-    tfidf_matrix = vectorizer.fit_transform(sentences)
-    sim_matrix = cosine_similarity(tfidf_matrix)
-    np.fill_diagonal(sim_matrix, 0)
-    def pagerank(matrix, damping=0.85, max_iter=100, tol=1e-4):
-        N = matrix.shape[0]
-        ranks = np.ones(N) / N
-        row_sums = np.sum(matrix, axis=1)
-        row_sums[row_sums == 0] = 1
-        for _ in range(max_iter):
-            prev_ranks = ranks.copy()
-            for i in range(N):
-                incoming = matrix[:, i]
-                ranks[i] = (1 - damping) / N + damping * np.sum(incoming * prev_ranks / row_sums)
-            if np.linalg.norm(ranks - prev_ranks) < tol:
-                break
-        return ranks
-    scores = pagerank(sim_matrix)
-    ranked_idx = np.argsort(scores)[::-1]
-    selected_idx = sorted(ranked_idx[:top_n])
-    summary = ' '.join([sentences[i] for i in selected_idx])
-    return summary
-
-def summarize_from_wikipedia(query, top_n=3):  
-    raw_summary = get_wikipedia_summary(query)  
-    first_summary = textrank_summarize(raw_summary, top_n=top_n)  
-    second_summary = textrank_summarize(first_summary, top_n=top_n)  
-    return second_summary