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import pandas as pd |
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import numpy as np |
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from dotenv import load_dotenv |
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from langchain_community.document_loaders import TextLoader |
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from langchain_community.embeddings import HuggingFaceEmbeddings |
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from langchain_text_splitters import CharacterTextSplitter |
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from langchain_chroma import Chroma |
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import gradio as gr |
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load_dotenv() |
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books = pd.read_csv("books_with_emotions.csv") |
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books["large_thumbnail"] = books["thumbnail"] + "&fife=w800" |
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books["large_thumbnail"] = np.where( |
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books["large_thumbnail"].isna(), |
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"cover-not-found.jpg", |
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books["large_thumbnail"], |
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) |
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raw_documents = TextLoader("tagged_description.txt", encoding = 'utf-8').load() |
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text_splitter = CharacterTextSplitter(separator="\n", chunk_size=0, chunk_overlap=0) |
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documents = text_splitter.split_documents(raw_documents) |
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embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-MiniLM-L3-v2") |
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db_books = Chroma.from_documents( |
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documents, |
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embedding=embedding_model |
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) |
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def retrieve_semantic_recommendations( |
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query: str, |
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category: str = None, |
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tone: str = None, |
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initial_top_k: int = 50, |
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final_top_k: int = 16, |
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) -> pd.DataFrame: |
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recs = db_books.similarity_search(query, k=initial_top_k) |
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books_list = [int(rec.page_content.strip('"').split()[0]) for rec in recs] |
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book_recs = books[books["isbn13"].isin(books_list)].head(initial_top_k) |
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if category != "All": |
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book_recs = book_recs[book_recs["simple_categories"] == category].head(final_top_k) |
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else: |
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book_recs = book_recs.head(final_top_k) |
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if tone == "Happy": |
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book_recs.sort_values(by="joy", ascending=False, inplace=True) |
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elif tone == "Surprising": |
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book_recs.sort_values(by="surprise", ascending=False, inplace=True) |
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elif tone == "Angry": |
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book_recs.sort_values(by="anger", ascending=False, inplace=True) |
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elif tone == "Suspenseful": |
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book_recs.sort_values(by="fear", ascending=False, inplace=True) |
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elif tone == "Sad": |
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book_recs.sort_values(by="sadness", ascending=False, inplace=True) |
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return book_recs |
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def recommend_books(query:str, category:str,tone:str ): |
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recommendations = retrieve_semantic_recommendations(query, category, tone) |
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results = [] |
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for _, row in recommendations.iterrows(): |
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description = row["description"] |
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truncated_desc_split = description.split() |
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truncated_description = " ".join(truncated_desc_split[:30]) + "..." |
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authors_split = row["authors"].split(";") |
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if len(authors_split) == 2: |
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authors_str = f"{authors_split[0]} and {authors_split[1]}" |
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elif len(authors_split) > 2: |
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authors_str = f"{', '.join(authors_split[:-1])}, and {authors_split[-1]}" |
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else: |
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authors_str = row["authors"] |
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caption = f"{row['title']} by {authors_str}: {truncated_description}" |
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results.append((row["large_thumbnail"], caption)) |
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return results |
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categories = ["All"] + sorted(books["simple_categories"].unique()) |
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tones = ["All"] + ["Happy", "Surprising", "Angry", "Suspenseful", "Sad"] |
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with gr.Blocks(theme = gr.themes.Glass()) as dashboard: |
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gr.Markdown("# Semantic book recommender") |
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with gr.Row(): |
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user_query = gr.Textbox(label = "Please enter a description of a book:", |
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placeholder = "e.g., A story about forgiveness") |
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category_dropdown = gr.Dropdown(choices = categories, label = "Select a category:", value = "All") |
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tone_dropdown = gr.Dropdown(choices = tones, label = "Select an emotional tone:", value = "All") |
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submit_button = gr.Button("Find recommendations") |
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gr.Markdown("## Recommendations") |
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output = gr.Gallery(label = "Recommended books", columns = 8, rows = 2) |
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submit_button.click(fn = recommend_books, |
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inputs = [user_query, category_dropdown, tone_dropdown], |
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outputs = output) |
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if __name__ == "__main__": |
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dashboard.launch() |
