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| import gradio as gr | |
| import pandas as pd | |
| from PIL import Image | |
| import requests | |
| dfp_matrice_association = pd.read_csv("./data/association.csv", index_col=0) | |
| dfp_matrice_distance_from_encoding = pd.read_csv("./data/ed_from_encoding.csv", index_col=0) | |
| dfp_matrice_distance_from_association = pd.read_csv("./data/ed_from_association.csv", index_col=0) | |
| dfp_cards = pd.read_csv("./data/cards.csv") | |
| dfp_cards.sort_values("cname", inplace=True) | |
| mapping_cname_cid = dfp_cards[["cname", "cid"]].reset_index() | |
| mapping_cname_cid = mapping_cname_cid.set_index("cname").to_dict(orient="index") | |
| dfp_cards.set_index(["cid"], inplace=True) | |
| def clean_recommendations(cid, recommendations): | |
| if cid in recommendations: | |
| recommendations.remove(cid) | |
| if str(cid) in recommendations: | |
| recommendations.remove(str(cid)) | |
| return recommendations | |
| def get_recommendations(cid, dfp_, ascending=False, k=5): | |
| recommendations = dfp_.loc[cid].sort_values(ascending=ascending).index.tolist() | |
| clean_recommendations(cid, recommendations) | |
| return recommendations[:k] | |
| def display_recommendations(cname, use_euclidian_distance, use_description_embedding, k): | |
| cid = int(mapping_cname_cid[cname]["cid"]) | |
| # Collect the recommendations | |
| dfp_dist = dfp_matrice_distance_from_association if use_euclidian_distance else dfp_matrice_association | |
| if (use_description_embedding == False): | |
| recommendations = get_recommendations(cid, dfp_dist, use_euclidian_distance, k=25) | |
| else: | |
| closest_card = get_recommendations(cid, dfp_matrice_distance_from_encoding, True, k=1)[0] | |
| recommendations = get_recommendations(int(closest_card), dfp_dist, use_euclidian_distance, k=25) | |
| recommendations = recommendations[:k] | |
| recommendations_string = [dfp_cards.loc[int(cid_r)]["cname"] for cid_r in recommendations] | |
| recommendations_image = [Image.open(requests.get(dfp_cards.loc[int(cid_r)]["art"], stream=True).raw).resize((240,300)) for cid_r in recommendations] | |
| block_text = "\n" | |
| for idx, cid_r in enumerate(recommendations): | |
| block_text += f"{idx+1}){dfp_cards.loc[int(cid_r)]['cname']} : {dfp_cards.loc[int(cid_r)]['ability']}\n" | |
| # block_text += f"{idx+1})\n" | |
| text_output = f""" | |
| Recommended cards:{block_text} | |
| """ | |
| return text_output, recommendations_image | |
| title = "Marvel Snap deck starter" | |
| description = """Gradio demo for Marvel Snap deck starter \n | |
| To use it, simply select the card in the dropdown (cname) that you want to use to kickstart the deck and after use the type of recommender system:\n | |
| 1) use_euclidian_distance: Use the euclidian distance or the normalized association \n | |
| 2) use_description_embedding: Use the description (name + stats + ability X transformers encoder) as first filtering before the model in 1)\n | |
| More details in this article https://www.the-odd-dataguy.com/2023/01/15/marvel_snap_cold_start/ | |
| """ | |
| article = "<div style='text-align: center;'>Marvel Snap deck starter by <a href='https://www.linkedin.com/in/jeanmicheldaignan/' target='_blank'>Jean-Michel D</a> | <a href='https://www.the-odd-dataguy.com/2023/01/15/marvel_snap_cold_start/' target='_blank'>Article</a>" | |
| demo = gr.Interface( | |
| fn=display_recommendations, | |
| inputs=[gr.inputs.Dropdown(dfp_cards["cname"].tolist()), | |
| "checkbox", | |
| "checkbox", | |
| gr.Slider(minimum=1, maximum=20, step=1, value=5)], | |
| outputs=["text", gr.Gallery(label="Recommendations")], | |
| title=title, | |
| description=description, | |
| article=article, | |
| ) | |
| demo.launch() |