grad-dashboard.py

import pandas as pd
import numpy as np
from dotenv import load_dotenv

from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import TextLoader
# from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

from langchain_google_genai import GoogleGenerativeAIEmbeddings


import gradio as gr

load_dotenv()

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.jpeg",books["large_thumbnail"],
                                  )
raw_documents=TextLoader("tagged_description.txt",encoding="utf-8").load()
text_splitter=CharacterTextSplitter(separator="\n",chunk_size=0,chunk_overlap=0)
documents=text_splitter.split_documents(raw_documents)
print(f"Number of documents loaded: {len(documents)}")


db_books = FAISS.from_documents(
    documents,
    embedding=GoogleGenerativeAIEmbeddings(model="models/embedding-001")
)
print("FAISS DB created with documents")
def retrieve_semantic_recommendation(
        query:str,
        category:str=None,
        tone:str =None,
        initial_top_k:int =50,
        final_top_k:int =24,
)-> pd.DataFrame:
    # print("rsr")
    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(final_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
):
        # print("Inside recommend_books function")
        recommendations= retrieve_semantic_recommendation(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("Get Recommendations")
    
    gr.Markdown("## Recommendations")
    output=gr.Gallery(label="Recommend books", columns=8,rows=3)

    submit_button.click(fn=recommend_books,
                        inputs=[user_query,category_dropdown,tone_dropdown],
                        outputs=output)


if __name__ == "__main__":
    try:
        print("Launching the Gradio dashboard...")
        dashboard.launch()
    except Exception as e:
        print(f"An error occurred: {e}")