import gradio as gr
from Recommender import Recommender
from Preprocess import ModelUtils, Preprocess
import numpy as np
import pandas as pd


data_path = "result.csv"
model_path = "model_root"

data = pd.read_csv(data_path)

modelu = ModelUtils(model_path)
modelu.make_dirs()
modelu.download_model()
p = Preprocess(model_path)

data = pd.read_csv(data_path)

rec = Recommender (1, 2, 3, 5, 4)
k = 3

table = [tuple(row) for row in data.to_numpy()]

def recom (input) :
    # id = input.split("-")[-1]
    indices, scores, title_scores = rec.recommend_k(table, k, input)
    out = list(data[indices]['title'])
    return "\n".join(out)

demo = gr.Interface(fn=recom, 
             inputs=[gr.Dropdown(choices = list(data['title'][:20]), multiselect=False, label="Titles")], 
             outputs=gr.Textbox(label="Titles of recommended items"))
demo.launch()