Upload app.py

app.py

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+import streamlit as st
+import wikipedia
+import wikipediaapi
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.decomposition import TruncatedSVD
+from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.preprocessing import normalize
+
+# Initialize Wikipedia API
+wiki_wiki = wikipediaapi.Wikipedia(
+    language='en',
+    user_agent="LSI1/1.0 ([email protected])"
+)
+
+# Function to fetch related Wikipedia articles
+def fetch_related_articles(query, max_articles=20):
+    search_results = wikipedia.search(query, results=max_articles)
+    articles = {}
+    for title in search_results:
+        page = wiki_wiki.page(title)
+        if page.exists():
+            articles[title] = page.text
+    return articles
+
+# Function to rank articles using SVD
+def rank_articles(query, vectorizer, svd, lsi_matrix, titles):
+    query_tfidf = vectorizer.transform([query])
+    query_lsi = svd.transform(query_tfidf)
+    query_lsi = normalize(query_lsi)
+    similarities = cosine_similarity(query_lsi, lsi_matrix).flatten()
+    ranked_indices = similarities.argsort()[::-1]  # Sort by similarity (desc)
+    return [(titles[idx], similarities[idx]) for idx in ranked_indices]
+
+# Streamlit UI
+st.title("Wikipedia Search with SVD")
+st.write("Enter a search query to fetch and rank Wikipedia articles.")
+
+# Input for query
+search_query = st.text_input("Search Wikipedia:")
+
+# Detect query change and clear session state if needed
+if "previous_query" not in st.session_state:
+    st.session_state.previous_query = None
+
+if search_query and search_query != st.session_state.previous_query:
+    # Reset session state variables
+    st.session_state.previous_query = search_query
+    st.session_state.ranked_results = None
+    st.session_state.end_index = 10
+
+if search_query:
+    # Fetch articles dynamically
+    with st.spinner("Fetching articles..."):
+        articles = fetch_related_articles(search_query)
+    
+    if not articles:
+        st.warning("No articles found! Try a different query.")
+    else:
+        # Prepare data for TF-IDF
+        titles = list(articles.keys())
+        contents = list(articles.values())
+        
+        # TF-IDF Vectorization
+        vectorizer = TfidfVectorizer(max_features=5000, stop_words="english")
+        tfidf_matrix = vectorizer.fit_transform(contents)
+        
+        # SVD Dimensionality Reduction
+        n_components = 100  # Number of dimensions
+        svd = TruncatedSVD(n_components=n_components)
+        lsi_matrix = svd.fit_transform(tfidf_matrix)
+        lsi_matrix = normalize(lsi_matrix)  # Normalize for cosine similarity
+        
+        # Handle session state for pagination
+        if st.session_state.ranked_results is None:
+            st.session_state.ranked_results = rank_articles(search_query, vectorizer, svd, lsi_matrix, titles)
+        
+        # Display ranked results
+        end_index = st.session_state.end_index
+        ranked_results = st.session_state.ranked_results[:end_index]
+        
+        st.subheader("Search Results:")
+        for title, similarity in ranked_results:
+            st.markdown(f"### [{title}](https://en.wikipedia.org/wiki/{title.replace(' ', '_')})")
+            st.write(f"**Similarity Score:** {similarity:.2f}")
+            st.write("---")
+        
+        # Pagination controls
+        if end_index < len(st.session_state.ranked_results):
+            if st.button("Load More"):
+                st.session_state.end_index += 10
+        else:
+            st.info("No more articles to load.")