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Update app.py
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app.py
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@@ -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=
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# ✅ Fetch papers from ArXiv
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feed = feedparser.parse(url)
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@@ -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
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