added all files

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+cleaned.csv filter=lfs diff=lfs merge=lfs -text
+training.csv filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,199 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import textwrap
+
+# Page configuration
+st.set_page_config(
+    page_title="Article Recommender",
+    layout="wide"
+)
+
+# Custom CSS with improved visibility
+st.markdown("""
+    <style>
+    .article-card {
+        background-color: #ffffff;
+        padding: 1.5rem;
+        border-radius: 10px;
+        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+        margin-bottom: 1rem;
+        color: #1f1f1f;  /* Dark text color for contrast */
+    }
+    .article-title {
+        color: #1f1f1f;
+        font-size: 1.5rem;
+        margin-bottom: 1rem;
+        font-weight: bold;
+    }
+    .article-preview {
+        color: #2d2d2d;  /* Darker grey for better visibility */
+        font-size: 1rem;
+        line-height: 1.6;
+    }
+    .article-full-text {
+        color: #2d2d2d;
+        font-size: 1.1rem;
+        line-height: 1.8;
+        white-space: pre-wrap;  /* Preserve formatting */
+    }
+    .recommendation-card {
+        background-color: #f8f9fa;
+        padding: 1rem;
+        border-radius: 8px;
+        margin-bottom: 1rem;
+        border: 1px solid #e9ecef;
+    }
+    .recommendation-title {
+        color: #1f1f1f;
+        font-size: 1.2rem;
+        margin-bottom: 0.5rem;
+        font-weight: bold;
+    }
+    .recommendation-preview {
+        color: #2d2d2d;
+        font-size: 0.9rem;
+        line-height: 1.5;
+    }
+    .page-navigation {
+        display: flex;
+        justify-content: center;
+        gap: 1rem;
+        margin: 2rem 0;
+    }
+    .search-results {
+        margin: 1rem 0;
+        padding: 0.5rem;
+        background-color: #e9ecef;
+        border-radius: 5px;
+        color: #1f1f1f;
+    }
+    /* Override Streamlit's default text color */
+    .stMarkdown, .stText {
+        color: #1f1f1f !important;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+# Load and prepare data functions (unchanged)
+@st.cache_data
+def load_data():
+    df = pd.read_csv("cleaned.csv")
+    train_df = pd.read_csv("training.csv")
+    return (df,train_df)
+
+@st.cache_resource
+def prepare_similarity_matrix(df):
+    tfidf = TfidfVectorizer(max_features=5000)
+    tf_vectors = tfidf.fit_transform(df["data"]).toarray()
+    tf_similarity = cosine_similarity(tf_vectors)
+    return tf_similarity
+
+def get_recommended_articles(title, df, tf_similarity):
+    title_idx = df[df["title"] == title].index[0]
+    similar_idx_scores = list(enumerate(tf_similarity[title_idx]))
+    sorted_similar_idx = sorted(similar_idx_scores, key=lambda x: x[1], reverse=True)
+    recommended_idx = sorted_similar_idx[1:4]
+    return recommended_idx
+
+def truncate_text(text, max_words=50):
+    return " ".join(text.split()[:max_words]) + "..."
+
+# Load data
+df,train_df = load_data()
+tf_similarity = prepare_similarity_matrix(train_df)
+
+# Initialize session state
+if 'page' not in st.session_state:
+    st.session_state.page = 'home'
+
+# Sidebar with improved visibility
+with st.sidebar:
+    st.title("Navigation")
+    if st.button("🏠 Home", use_container_width=True):
+        st.session_state.page = 'home'
+        st.rerun()
+    
+    st.markdown("---")
+    search_query = st.text_input("🔍 Search Articles:")
+
+# Main content
+if st.session_state.page == 'home':
+    st.title("📚 Article Collection")
+    
+    # Search functionality
+    if search_query:
+        mask = (df["title"].str.contains(search_query, case=False)) | \
+               (df["text"].str.contains(search_query, case=False))
+        filtered_df = df[mask]
+        st.markdown(f"""
+            <div class="search-results">
+                📊 Found {len(filtered_df)} articles matching '{search_query}'
+            </div>
+            """, unsafe_allow_html=True)
+    else:
+        filtered_df = df
+
+    # Pagination
+    articles_per_page = 10
+    total_pages = len(filtered_df) // articles_per_page + (1 if len(filtered_df) % articles_per_page > 0 else 0)
+    col1, col2, col3 = st.columns([2, 3, 2])
+    with col2:
+        page_number = st.number_input("Page", min_value=1, max_value=max(1, total_pages), value=1) - 1
+    
+    start_idx = page_number * articles_per_page
+    end_idx = start_idx + articles_per_page
+    page_df = filtered_df.iloc[start_idx:end_idx]
+
+    # Display articles
+    for _, row in page_df.iterrows():
+        st.markdown(f"""
+            <div class="article-card">
+                <div class="article-title">{row["title"]}</div>
+                <div class="article-preview">{truncate_text(row["text"])}</div>
+            </div>
+        """, unsafe_allow_html=True)
+        if st.button("📖 Read Full Article", key=f"read_{_}"):
+            st.session_state.page = 'article'
+            st.session_state.article_title = row["title"]
+            st.rerun()
+
+else:  # Article page
+    # Back button in sidebar
+    if st.sidebar.button("← Back to Articles", use_container_width=True):
+        st.session_state.page = 'home'
+        st.rerun()
+
+    # Display full article
+    article_data = df[df["title"] == st.session_state.article_title].iloc[0]
+    
+    st.title(article_data["title"])
+    
+    # Article container with improved visibility
+    st.markdown(f"""
+        <div class="article-card">
+            <div class="article-full-text">
+                {article_data["text"]}
+            </div>
+        </div>
+    """, unsafe_allow_html=True)
+    
+    # Recommendations section
+    st.markdown("---")
+    st.subheader("📚 Recommended Articles")
+    recommended_articles = get_recommended_articles(st.session_state.article_title, df, tf_similarity)
+    
+    cols = st.columns(3)
+    for idx, (article_idx, similarity_score) in enumerate(recommended_articles):
+        with cols[idx]:
+            st.markdown(f"""
+                <div class="recommendation-card">
+                    <div class="recommendation-title">{df['title'].iloc[article_idx]}</div>
+                    <div class="recommendation-preview">{truncate_text(df["text"].iloc[article_idx], max_words=30)}</div>
+                </div>
+            """, unsafe_allow_html=True)
+            if st.button("📖 Read This Article", key=f"rec_{article_idx}"):
+                st.session_state.article_title = df["title"].iloc[article_idx]
+                st.rerun()

cleaned.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c196ee96b98b14e4bfd83d7b6ea55b5ccc0f25f23f5cab45e56d41a8f96642f2
+size 13426956

training.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bbd165c71d1994f8236fe298c6eb3fa6b1ac06cf7555ae5eb84e50cbc459afb3
+size 14748930