Upload app.py

app.py

@@ -77,7 +77,7 @@ def build_faiss_index(dataset: pd.DataFrame) -> Tuple[faiss.IndexFlatIP, np.ndar
 def compute_correlations_faiss(index, book_titles: List[str],
                                target_book, num_recommendations) -> pd.DataFrame:
     print(target_book, type(target_book))
-    emb = create_embedding([target_book[0]])
+    emb = create_embedding([target_book])
     # target_vector = book_titles.index(emb)
 
 
@@ -132,7 +132,7 @@ def load_and_prepare_data():
     book_titles = dataset["Book-Title"]
 
 
-def recommend_books_with_theme(target_book: str, num_recommendations: int = 10, theme= None):
+def recommend_books(target_book: str, num_recommendations: int = 10):
     global dataset, faiss_index, normalized_data, book_titles
 
     if dataset is None or faiss_index is None or normalized_data is None or book_titles is None:
@@ -140,8 +140,10 @@ def recommend_books_with_theme(target_book: str, num_recommendations: int = 10,
 
     target_book = target_book.lower()
     # Fuzzy match the input to the closest book title
-    closest_match = process.extractOne(target_book, book_titles)
+    closest_match, score, _ = process.extractOne(target_book, book_titles)
 
+    if score < 50:  # You can adjust this threshold
+        return f"No close match found for '{target_book}'. Please try a different title."
 
     correlations = compute_correlations_faiss(faiss_index, list(dataset["Book-Title"]), closest_match, num_recommendations)
 
@@ -150,25 +152,24 @@ def recommend_books_with_theme(target_book: str, num_recommendations: int = 10,
     result = f"Top {num_recommendations} recommendations for '{target_book}':\n\n"
     for i, (_, row) in enumerate(recommendations.iterrows(), 1):
         result += f"{i}. {row['book']} (Correlation: {row['corr']:.2f})\n"
-    # Set theme based on user selection
-    theme_mode = "light" if theme == "Light" else "dark"
-    return result, theme_mode
+    return result
 
 
-# Gradio interface
+import gradio as gr
+
 iface = gr.Interface(
-    fn=recommend_books_with_theme,
+    fn=recommend_books,
     inputs=[
         gr.Textbox(label="Enter a book title"),
-        gr.Slider(minimum=1, maximum=20, step=1, label="Number of recommendations", value=10),
-        gr.Dropdown(["Light", "Dark"], label="Theme", value="Light")  # Theme toggle
-    ],
-    outputs=[
-        gr.Textbox(label="Recommendations"),
-        gr.Text(label="Current Theme"),  # Show selected theme
+        gr.Slider(minimum=1, maximum=20, step=1, label="Number of recommendations", value=10)
     ],
-    title="Book Recommender with Theme Toggle",
-    description="Enter a book title to get recommendations and select a theme (Light/Dark)."
+    outputs=gr.Textbox(label="Recommendations"),
+    title="Book Recommender",
+    description="Enter a book title to get recommendations based on user ratings and book similarities.",
+    theme="light"  # Force light mode
 )
 
+iface.launch()
+
+
 iface.launch()