| import gradio as gr |
| from api import MovieRecommender |
|
|
| recommender = MovieRecommender() |
|
|
| def recommend_movies(prompt, topk): |
| df = recommender.recommend(prompt, topk=int(topk)) |
| return df |
|
|
| demo = gr.Interface( |
| fn=recommend_movies, |
| inputs=[ |
| gr.Textbox(label="Movie prompt", placeholder="action thriller with robots"), |
| gr.Slider(1, 20, value=5, step=1, label="Top K") |
| ], |
| outputs=gr.Dataframe(label="Recommendations"), |
| title="🎬 Movie Nerd", |
| description="Prompt-based movie recommendations using embeddings" |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch(share=True) |
| |
