Add chroma_books to gitignore

app.py

@@ -1,24 +1,71 @@
 import pandas as pd
 import gradio as gr
 import numpy as np
-
-
+import os
 from langchain_chroma import Chroma
 from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.document_loaders import TextLoader
+from langchain.text_splitter import CharacterTextSplitter
 
+# Ensure model caching
+os.environ["HF_HOME"] = "/tmp/hf_cache"
+os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf_cache"
 
+# Initialize embeddings with caching
+print("Loading embeddings model...")
 embeddings = HuggingFaceEmbeddings(
-    model_name="sentence-transformers/all-MiniLM-L6-v2"  # Fast and good quality
-    # or "sentence-transformers/all-mpnet-base-v2"  # Higher quality, slower
+    model_name="sentence-transformers/all-MiniLM-L6-v2",
+    model_kwargs={'device': 'cpu'},
+    encode_kwargs={'normalize_embeddings': True}
 )
 
+# Initialize ChromaDB
+print("Initializing ChromaDB...")
+if not os.path.exists("chroma_books"):
+    print("Creating new ChromaDB from tagged_description.txt...")
+    try:
+        raw_docs = TextLoader("tagged_description.txt", encoding="utf-8").load()
+        text_splitter = CharacterTextSplitter(
+            separator="\n",
+            chunk_size=0,
+            chunk_overlap=0,
+            length_function=len,
+        )
+        documents = text_splitter.split_documents(raw_docs)
+        print(f"Loaded {len(documents)} documents")
+        
+        db_books = Chroma.from_documents(
+            documents,
+            embedding=embeddings,
+            collection_name="books",
+            persist_directory="chroma_books",
+        )
+        print("ChromaDB created successfully!")
+    except FileNotFoundError:
+        print("ERROR: tagged_description.txt not found!")
+        raise
+else:
+    print("Loading existing ChromaDB...")
+    db_books = Chroma(
+        persist_directory="chroma_books", 
+        embedding_function=embeddings, 
+        collection_name="books"
+    )
 
-books = pd.read_csv("final_book_df.csv")
-books["large_thumbnail"] = books["thumbnail"] + "&fife=w800"
-books["large_thumbnail"] = np.where(books["large_thumbnail"].isna(), "cover-not-found.jpg", books["large_thumbnail"])
-
-db_books = Chroma(persist_directory="chroma_books", embedding_function=embeddings, collection_name="books")
-
+# Load books data
+print("Loading books data...")
+try:
+    books = pd.read_csv("final_book_df.csv")
+    books["large_thumbnail"] = books["thumbnail"] + "&fife=w800"
+    books["large_thumbnail"] = np.where(
+        books["large_thumbnail"].isna(), 
+        "cover-not-found.jpg", 
+        books["large_thumbnail"]
+    )
+    print(f"Loaded {len(books)} books")
+except FileNotFoundError:
+    print("ERROR: final_book_df.csv not found!")
+    raise
 
 def retrieve_semantic_recommendations(
         query: str,
@@ -27,96 +74,129 @@ def retrieve_semantic_recommendations(
         initial_top_k: int = 50,
         final_top_k: int = 16,
 ) -> pd.DataFrame:
-
+    """Retrieve semantic recommendations based on query, category, and tone."""
+    
     recs = db_books.similarity_search(query, k=initial_top_k)
     books_list = [int(rec.page_content.strip('"').split()[0]) for rec in recs]
     book_recs = books[books["isbn13"].isin(books_list)].head(initial_top_k)
 
-    if category != "All":
+    # Filter by category
+    if category and category != "All":
         book_recs = book_recs[book_recs["categories"] == category].head(final_top_k)
     else:
         book_recs = book_recs.head(final_top_k)
 
+    # Sort by emotional tone
     if tone == "Happy":
-        book_recs.sort_values(by="joy", ascending=False, inplace=True)
+        book_recs = book_recs.sort_values(by="joy", ascending=False)
     elif tone == "Surprising":
-        book_recs.sort_values(by="surprise", ascending=False, inplace=True)
+        book_recs = book_recs.sort_values(by="surprise", ascending=False)
     elif tone == "Angry":
-        book_recs.sort_values(by="anger", ascending=False, inplace=True)
+        book_recs = book_recs.sort_values(by="anger", ascending=False)
     elif tone == "Suspenseful":
-        book_recs.sort_values(by="fear", ascending=False, inplace=True)
+        book_recs = book_recs.sort_values(by="fear", ascending=False)
     elif tone == "Sad":
-        book_recs.sort_values(by="sadness", ascending=False, inplace=True)
+        book_recs = book_recs.sort_values(by="sadness", ascending=False)
 
     return book_recs
 
-
-def recommend_books(
-        query: str,
-        category: str,
-        tone: str
-):
-    recommendations = retrieve_semantic_recommendations(query, category, tone)
-    results = []
-
-    for _, row in recommendations.iterrows():
-        description = row["description"]
-        truncated_desc_split = description.split()
-        truncated_description = " ".join(truncated_desc_split[:30]) + "..."
-
-        authors_split = row["authors"].split(";")
-        if len(authors_split) == 2:
-            authors_str = f"{authors_split[0]} and {authors_split[1]}"
-        elif len(authors_split) > 2:
-            authors_str = f"{', '.join(authors_split[:-1])}, and {authors_split[-1]}"
-        else:
-            authors_str = row["authors"]
-
-        caption = f"{row['title_and_subtitle']} by {authors_str}: {truncated_description}"
-        results.append((row["large_thumbnail"], caption))
-    return results
-
-
-
-categories = ["All"] + sorted(books["categories"].unique())
-tones = ["All"] + ["Happy", "Surprising", "Angry", "Suspenseful", "Sad"]
-
+def recommend_books(query: str, category: str, tone: str):
+    """Main recommendation function for Gradio interface."""
+    
+    if not query.strip():
+        return []
+    
+    try:
+        recommendations = retrieve_semantic_recommendations(query, category, tone)
+        results = []
+
+        for _, row in recommendations.iterrows():
+            # Handle missing description
+            description = row.get("description", "No description available")
+            if pd.isna(description):
+                description = "No description available"
+            
+            # Truncate description
+            truncated_desc_split = str(description).split()
+            truncated_description = " ".join(truncated_desc_split[:30]) + "..."
+
+            # Format authors
+            authors = row.get("authors", "Unknown Author")
+            if pd.isna(authors):
+                authors_str = "Unknown Author"
+            else:
+                authors_split = str(authors).split(";")
+                if len(authors_split) == 2:
+                    authors_str = f"{authors_split[0]} and {authors_split[1]}"
+                elif len(authors_split) > 2:
+                    authors_str = f"{', '.join(authors_split[:-1])}, and {authors_split[-1]}"
+                else:
+                    authors_str = authors
+
+            # Create caption
+            title = row.get("title_and_subtitle", "Unknown Title")
+            caption = f"{title} by {authors_str}: {truncated_description}"
+            results.append((row["large_thumbnail"], caption))
+            
+        return results
+    
+    except Exception as e:
+        print(f"Error in recommend_books: {e}")
+        return []
+
+# Prepare dropdown options
+categories = ["All"] + sorted(books["categories"].unique().tolist())
+tones = ["All", "Happy", "Surprising", "Angry", "Suspenseful", "Sad"]
+
+# Create Gradio interface
 with gr.Blocks(theme=gr.themes.Glass()) as dashboard:
-    gr.Markdown("# Semantic Book Recommender")
-    gr.Markdown("## Find your next favorite book!")
+    gr.Markdown("# 📚 Semantic Book Recommender")
+    gr.Markdown("## Find your next favorite book using AI-powered semantic search!")
 
     with gr.Row():
         user_query = gr.Textbox(
-            label="please enter a description of your book:",
-            placeholder="Enter your query here...",
-            lines=1,
-            max_lines=1,
+            label="Describe your ideal book:",
+            placeholder="e.g., 'A thrilling mystery set in Victorian London'",
+            lines=2,
+            max_lines=3,
         )
 
-        category_dropdown = gr.Dropdown(
-            label="Select a category",
-            choices=categories,
-            value="All",
-        )
-        tone_dropdown = gr.Dropdown(
-            label="Select an emotional tone",
-            choices=tones,
-            value="All",
-        )
-        submit_button = gr.Button("Submit", variant="primary")
-
-    gr.Markdown("## Recommendations")
+        with gr.Column():
+            category_dropdown = gr.Dropdown(
+                label="Select a category (optional)",
+                choices=categories,
+                value="All",
+            )
+            tone_dropdown = gr.Dropdown(
+                label="Select an emotional tone (optional)",
+                choices=tones,
+                value="All",
+            )
+            submit_button = gr.Button("🔍 Find Books", variant="primary")
+
+    gr.Markdown("## 📖 Recommendations")
     output = gr.Gallery(
         label="Recommended Books",
-        columns=8,
-        rows=2,
+        columns=4,  # Reduced for better mobile experience
+        rows=4,
+        height="auto",
     )
 
+    # Event handlers
     submit_button.click(
         fn=recommend_books,
         inputs=[user_query, category_dropdown, tone_dropdown],
         outputs=output,
     )
+    
+    # Allow Enter key to submit
+    user_query.submit(
+        fn=recommend_books,
+        inputs=[user_query, category_dropdown, tone_dropdown],
+        outputs=output,
+    )
+
+print("App initialized successfully! 🚀")
 
 if __name__ == "__main__":
     dashboard.launch()