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 that you want to use to kickstart the deck."""
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()