| import os | |
| import json | |
| import gradio as gr | |
| from sentence_transformers import SentenceTransformer | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') | |
| def get_n_weighted_scores(embeddings, query, n, objective_weight, subjective_weight): | |
| query = [model.encode(query)] | |
| weighted_scores = [] | |
| for key, value in embeddings.items(): | |
| objective_embedding = value['objective_embedding'] | |
| subjective_embeddings = value['subjective_embeddings'] | |
| objective_score = cosine_similarity(query, objective_embedding).item() | |
| subjective_scores = cosine_similarity(query, subjective_embeddings) | |
| max_score = 0 | |
| max_review_index = 0 | |
| for idx, score in enumerate(subjective_scores[0].tolist()): | |
| weighted_score = ((objective_score * objective_weight)+(score * subjective_weight)) | |
| if weighted_score > max_score: | |
| max_score = weighted_score | |
| max_review_index = idx | |
| weighted_scores.append((key, max_score, max_review_index)) | |
| return sorted(weighted_scores, key=lambda x: x[1], reverse=True)[:n] | |
| def filter_anime(embeddings, genres, themes, rating): | |
| genres = set(genres) | |
| themes = set(themes) | |
| rating = set(rating) | |
| filtered_anime = embeddings.copy() | |
| for key, anime in embeddings.items(): | |
| anime_genres = set(anime['genres']) | |
| anime_themes = set(anime['themes']) | |
| anime_rating = set([anime['rating']]) | |
| if genres.intersection(anime_genres) or 'ALL' in genres: | |
| pass | |
| else: | |
| filtered_anime.pop(key) | |
| continue | |
| if themes.intersection(anime_themes) or 'ALL' in themes: | |
| pass | |
| else: | |
| filtered_anime.pop(key) | |
| continue | |
| if rating.intersection(anime_rating) or 'ALL' in rating: | |
| pass | |
| else: | |
| filtered_anime.pop(key) | |
| continue | |
| return filtered_anime | |
| def get_recommendation(query, number_of_recommendations, genres, themes, rating, objective_weight, subjective_weight): | |
| filtered_anime = filter_anime(embeddings, genres, themes, rating) | |
| results = [] | |
| weighted_scores = get_n_weighted_scores(filtered_anime, query, number_of_recommendations, float(objective_weight), float(subjective_weight)) | |
| for idx, (key, score, review_index) in enumerate(weighted_scores, start=1): | |
| data = embeddings[key] | |
| if not data['english']: | |
| name = data['japanese'] | |
| else: | |
| name = data['english'] | |
| description = data['description'] | |
| review = data['reviews'][review_index]['text'] | |
| image = data['image'] | |
| results.append(gr.Image(label=f"Recommendation {idx}: {name}",value=image, height=435, width=500, visible=True)) | |
| results.append(gr.Textbox(label=f"Synopsis", value=description, max_lines=7, visible=True)) | |
| results.append(gr.Textbox(label=f"Most Relevant User Review",value=review, max_lines=7, visible=True)) | |
| for _ in range(10-number_of_recommendations): | |
| results.append(gr.Image(visible=False)) | |
| results.append(gr.Textbox(visible=False)) | |
| results.append(gr.Textbox(visible=False)) | |
| return results | |
| if __name__ == '__main__': | |
| with open('./embeddings/data.json') as f: | |
| data = json.load(f) | |
| embeddings = data['embeddings'] | |
| filters = data['filters'] | |
| with gr.Blocks(theme=gr.themes.Soft(primary_hue='red')) as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown( | |
| ''' | |
| # Welcome to the Nuanced Recommendation System! | |
| ### This system **combines** both objective (synopsis, episode count, themes) and subjective (user reviews) data, in order to recommend the most approprate anime. Feel free to refine using the **optional** filters below! | |
| ''' | |
| ) | |
| with gr.Column(): | |
| pass | |
| with gr.Row(): | |
| with gr.Column() as input_col: | |
| query = gr.Textbox(label="What are you looking for?") | |
| number_of_recommendations = gr.Slider(label= "# of Recommendations", minimum=1, maximum=10, value=3, step=1) | |
| genres = gr.Dropdown(label='Genres',multiselect=True,choices=filters['genres'], value=['ALL']) | |
| themes = gr.Dropdown(label='Themes',multiselect=True,choices=filters['themes'], value=['ALL']) | |
| rating = gr.Dropdown(label='Rating',multiselect=True,choices=filters['rating'], value=['ALL']) | |
| objective_weight = gr.Slider(label= "Objective Weight", minimum=0, maximum=1, value=.5, step=.1) | |
| subjective_weight = gr.Slider(label= "Subjective Weight", minimum=0, maximum=1, value=.5, step=.1) | |
| submit_btn = gr.Button("Submit") | |
| examples = gr.Examples( | |
| examples=[ | |
| ['A sci-fi anime set in a future where AI and robots have become self-aware', 3, ['Action', 'Sci-Fi', 'Fantasy'], ['ALL'], ['PG-13 - Teens 13 or older'], .8, .2], | |
| ['An anime where a group of students form a band, and the story focuses on their personal growth and struggles with adulthood', 5, ['ALL'], ['Music'], ['PG-13 - Teens 13 or older', 'R - 17+ (violence & profanity)'], .3, .7], | |
| ['An anime where the main character starts as a villain but slowly redeems themselves', 3, ['Suspense', 'Action'], ['ALL'], ['PG-13 - Teens 13 or older', 'R - 17+ (violence & profanity)'], .2, .8], | |
| ], | |
| inputs=[query, number_of_recommendations, genres, themes, rating, objective_weight, subjective_weight], | |
| ) | |
| outputs = [] | |
| with gr.Column(): | |
| for i in range(10): | |
| with gr.Row(): | |
| with gr.Column(): | |
| outputs.append(gr.Image(height=435, width=500, visible=False)) | |
| with gr.Column(): | |
| outputs.append(gr.Textbox(max_lines=7, visible=False)) | |
| outputs.append(gr.Textbox(max_lines=7, visible=False)) | |
| submit_btn.click( | |
| get_recommendation, | |
| [query, number_of_recommendations, genres, themes, rating, objective_weight, subjective_weight], | |
| outputs | |
| ) | |
| demo.launch() | |