Create app.py

app.py

@@ -0,0 +1,62 @@
+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
+demo = gr.Interface(
+    fn=display_recommendations,
+    inputs=[gr.inputs.Dropdown(dfp_cards["cname"].tolist()), 
+            "checkbox", 
+            "checkbox",
+            gr.Slider(minimum=1, maximum=10, ste=1, value=5)],
+    outputs=["text", gr.Gallery(label="Recommendations")],
+)
+demo.launch()