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app.py

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+import pandas as pd
+import numpy as np
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import gradio as gr
+
+# 1. LOAD DATA DARI GITHUB (Otomatis tanpa perlu upload CSV)
+@gr.utils.custom_cache
+def load_data():
+    books_url = "https://raw.githubusercontent.com/zygmuntz/goodbooks-10k/master/books.csv"
+    df = pd.read_csv(books_url, on_bad_lines='skip')
+    # Ambil kolom yang penting & bersihkan data kosong
+    df = df[['book_id', 'original_title', 'authors', 'average_rating', 'image_url']].fillna('')
+    df = df[df['original_title'] != ''].reset_index(drop=True)
+    
+    # Batasi 3000 buku agar aplikasi berjalan sangat cepat di Free Tier Hugging Face
+    df = df.head(3000)
+    return df
+
+df_books = load_data()
+
+# 2. NLP: CONTENT-BASED FILTERING (TF-IDF)
+# Menggabungkan judul dan penulis sebagai "Konteks Teks" untuk dipahami AI
+df_books['content'] = df_books['original_title'] + " " + df_books['authors']
+tfidf = TfidfVectorizer(stop_words='english')
+tfidf_matrix = tfidf.fit_transform(df_books['content'])
+
+# Menghitung kemiripan antar buku menggunakan Cosine Similarity
+cosine_sim = cosine_similarity(tfidf_matrix, tfidf_matrix)
+
+def recommend_books(book_title):
+    try:
+        # Cari ID buku berdasarkan judul
+        idx = df_books.index[df_books['original_title'] == book_title].tolist()[0]
+    except IndexError:
+        return "<p style='color:red;'>⚠️ Buku tidak ditemukan.</p>"
+
+    # Ambil skor kemiripan untuk buku tersebut
+    sim_scores = list(enumerate(cosine_sim[idx]))
+    # Urutkan dari yang paling mirip (Skor tertinggi)
+    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
+    
+    # Ambil Top 5 buku (Lewati index 0 karena itu adalah buku itu sendiri)
+    sim_scores = sim_scores[1:6]
+    book_indices = [i[0] for i in sim_scores]
+    
+    # 3. UI/UX: INJEKSI HTML & CSS UNTUK TAMPILAN GALERI (Visual Wow Factor)
+    html_content = "<div style='display: flex; gap: 20px; flex-wrap: wrap; justify-content: center; margin-top: 20px;'>"
+    for i in book_indices:
+        book = df_books.iloc[i]
+        html_content += f"""
+        <div style='width: 160px; background: white; border: 1px solid #e0e0e0; border-radius: 12px; padding: 15px; text-align: center; box-shadow: 0 4px 6px rgba(0,0,0,0.05); transition: transform 0.2s;'>
+            <img src='{book['image_url']}' style='width: 120px; height: 170px; object-fit: cover; border-radius: 6px; margin-bottom: 12px; box-shadow: 0 2px 4px rgba(0,0,0,0.1);' onerror="this.src='https://via.placeholder.com/120x170?text=No+Cover'">
+            <h4 style='font-size: 14px; color: #333; margin: 0 0 5px 0; display: -webkit-box; -webkit-line-clamp: 2; -webkit-box-orient: vertical; overflow: hidden; height: 38px;' title="{book['original_title']}">{book['original_title']}</h4>
+            <p style='font-size: 12px; color: #7f8c8d; margin: 0 0 8px 0; white-space: nowrap; overflow: hidden; text-overflow: ellipsis;'>{book['authors']}</p>
+            <div style='background: #f1f8ff; color: #0366d6; padding: 4px 8px; border-radius: 20px; display: inline-block; font-size: 12px; font-weight: bold;'>
+                ⭐ {book['average_rating']}
+            </div>
+        </div>
+        """
+    html_content += "</div>"
+    return html_content
+
+# Ambil daftar judul buku untuk dropdown (diurutkan abjad agar rapi)
+book_list = sorted(df_books['original_title'].tolist())
+
+# --- UI GRADIO ---
+with gr.Blocks(theme=gr.themes.Soft(), title="SmartLib AI") as app:
+    gr.Markdown("<h1 style='text-align: center; color: #2c3e50;'>📚 SmartLib: AI-Powered Recommender</h1>")
+    gr.Markdown("<p style='text-align: center; font-size: 16px; color: #7f8c8d;'>Sistem rekomendasi buku cerdas berbasis <b>Machine Learning (NLP TF-IDF)</b>. AI akan menganalisis konteks judul dan gaya penulis untuk menemukan bacaan yang paling sesuai dengan selera Anda.</p>")
+    
+    with gr.Row():
+        with gr.Column(scale=3):
+            in_book = gr.Dropdown(choices=book_list, value="Harry Potter and the Sorcerer's Stone", 
+                                  label="🔍 Ketik atau Pilih Buku Favorit Anda", interactive=True)
+        with gr.Column(scale=1):
+            btn = gr.Button("✨ Berikan Rekomendasi", variant="primary")
+            
+    gr.Markdown("### 🎯 Rekomendasi Teratas Untuk Anda:")
+    out_html = gr.HTML()
+    
+    # Interaksi: Bisa dengan klik tombol, atau langsung ganti dropdown
+    btn.click(recommend_books, inputs=in_book, outputs=out_html)
+    in_book.change(recommend_books, inputs=in_book, outputs=out_html)
+    
+    # Tampilan awal saat web dibuka
+    app.load(recommend_books, inputs=in_book, outputs=out_html)
+
+app.launch()

requirements.txt

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+pandas
+scikit-learn
+gradio