app.py

import gradio as gr
from data_loader import DataLoader
from recommender import RecommenderService
def recommender_pipeline(dataset_path, book_name):
    loader = DataLoader(dataset_path)
    data = loader.load_data()

    recommender_service = RecommenderService(data)
    recommendations = recommender_service.recommend_books(book_name)
    
    return "\n".join(recommendations)


with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            book_name_input = gr.Textbox(label="Enter a Book Title", placeholder="Type the book name...")
            dataset_path_input = gr.Textbox(label="Dataset Path", placeholder="Enter path to dataset", value="books_summary.csv")
            submit_btn = gr.Button(value="Get Recommendations")
        with gr.Column():
            # Output for the recommended books
            recommendations_output = gr.Textbox(label="Recommended Books", interactive=False)

    submit_btn.click(
        recommender_pipeline, inputs=[dataset_path_input, book_name_input], outputs=[recommendations_output]
    )

    examples = gr.Examples(
        examples=[
            ["The Alchemist"],
            ["The Seven Principles For Making Marriage Work"],
            ["The Pragmatist’s Guide To Relationships"]
        ],
        inputs=[book_name_input],
    )

if __name__ == "__main__":
    demo.launch(show_api=False)