pdx97 commited on
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1fea399
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1 Parent(s): 1d916f0

Update app.py

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  1. app.py +2 -2
app.py CHANGED
@@ -347,7 +347,7 @@ def fetch_latest_arxiv_papers(keywords: list, num_results: int = 5) -> list:
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  # ✅ Construct the query for ArXiv API
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  query = "+AND+".join([f"all:{kw}" for kw in keywords])
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  query_encoded = urllib.parse.quote(query)
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- url = f"http://export.arxiv.org/api/query?search_query={query_encoded}&start=0&max_results=50&sortBy=submittedDate&sortOrder=descending"
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  # ✅ Fetch papers from ArXiv
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  feed = feedparser.parse(url)
@@ -370,7 +370,7 @@ def fetch_latest_arxiv_papers(keywords: list, num_results: int = 5) -> list:
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  # ✅ TF-IDF Vectorization
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  corpus = [paper["title"] + " " + paper["abstract"] for paper in papers]
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- vectorizer = TfidfVectorizer(stop_words=stopwords.words('english'))
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  tfidf_matrix = vectorizer.fit_transform(corpus)
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  # ✅ Transform Query into TF-IDF Vector
 
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  # ✅ Construct the query for ArXiv API
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  query = "+AND+".join([f"all:{kw}" for kw in keywords])
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  query_encoded = urllib.parse.quote(query)
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+ url = f"http://export.arxiv.org/api/query?search_query={query_encoded}&start=0&max_results=5&sortBy=submittedDate&sortOrder=descending"
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  # ✅ Fetch papers from ArXiv
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  feed = feedparser.parse(url)
 
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  # ✅ TF-IDF Vectorization
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  corpus = [paper["title"] + " " + paper["abstract"] for paper in papers]
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+ vectorizer = TfidfVectorizer(stop_words=stopwords.words('english'),max_features=3000)
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  tfidf_matrix = vectorizer.fit_transform(corpus)
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  # ✅ Transform Query into TF-IDF Vector