| import pandas as pd
|
| import numpy as np
|
|
|
| from langchain_community.document_loaders import TextLoader
|
| from langchain_community.embeddings import HuggingFaceEmbeddings
|
| from langchain_text_splitters import CharacterTextSplitter
|
| from langchain_chroma import Chroma
|
|
|
| import gradio as gr
|
|
|
|
|
| books = pd.read_csv("books_with_emotions.csv")
|
| books["large_thumbnail"] = books["thumbnail"] + "&fife=w800"
|
| books["large_thumbnail"] = np.where(
|
| books["large_thumbnail"].isna(),
|
| "cover-not-found.jpg",
|
| books["large_thumbnail"],
|
| )
|
|
|
| raw_documents = TextLoader("tagged_description.txt",encoding="utf-8").load()
|
| text_splitter = CharacterTextSplitter(separator="\n", chunk_size=500, chunk_overlap=50)
|
| documents = text_splitter.split_documents(raw_documents)
|
|
|
| embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
|
| db_books = Chroma.from_documents(documents, embeddings)
|
|
|
|
|
| def retrieve_semantic_recommendations(
|
| query: str,
|
| category: str = None,
|
| tone: str = None,
|
| initial_top_k: int = 50,
|
| final_top_k: int = 16,
|
| ) -> pd.DataFrame:
|
|
|
| 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":
|
| book_recs = book_recs[book_recs["simple_categories"] == category].head(final_top_k)
|
| else:
|
| book_recs = book_recs.head(final_top_k)
|
|
|
| if tone == "Happy":
|
| book_recs.sort_values(by="joy", ascending=False, inplace=True)
|
| elif tone == "Surprising":
|
| book_recs.sort_values(by="surprise", ascending=False, inplace=True)
|
| elif tone == "Angry":
|
| book_recs.sort_values(by="anger", ascending=False, inplace=True)
|
| elif tone == "Suspenseful":
|
| book_recs.sort_values(by="fear", ascending=False, inplace=True)
|
| elif tone == "Sad":
|
| book_recs.sort_values(by="sadness", ascending=False, inplace=True)
|
|
|
| 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']} by {authors_str}: {truncated_description}"
|
| results.append((row["large_thumbnail"], caption))
|
| return results
|
|
|
| categories = ["All"] + sorted(books["simple_categories"].unique())
|
| tones = ["All"] + ["Happy", "Surprising", "Angry", "Suspenseful", "Sad"]
|
|
|
| with gr.Blocks(theme = gr.themes.Glass()) as dashboard:
|
| gr.Markdown("# Semantic book recommender")
|
|
|
| with gr.Row():
|
| user_query = gr.Textbox(label = "Please enter a description of a book:",
|
| placeholder = "e.g., A story about forgiveness")
|
| category_dropdown = gr.Dropdown(choices = categories, label = "Select a category:", value = "All")
|
| tone_dropdown = gr.Dropdown(choices = tones, label = "Select an emotional tone:", value = "All")
|
| submit_button = gr.Button("Find recommendations")
|
|
|
| gr.Markdown("## Recommendations")
|
| output = gr.Gallery(label = "Recommended books", columns = 8, rows = 2)
|
|
|
| submit_button.click(fn = recommend_books,
|
| inputs = [user_query, category_dropdown, tone_dropdown],
|
| outputs = output)
|
|
|
|
|
| if __name__ == "__main__":
|
| dashboard.launch()
|
|
|
