import numpy as np
import gradio as gr
import os
import pandas as pd
from datasets import load_dataset

Secret_token = os.getenv('token')

dataset = load_dataset('FDSRashid/hadith_info',data_files = 'Basic_Edge_Information.csv', token = Secret_token, split = 'train')
dataset2 = load_dataset('FDSRashid/hadith_info',data_files = 'masteredgecityratiosapplied_updated_with_ID_mapping_to_NEO4J.csv', token = Secret_token, split = 'train')
edge_info = dataset.to_pandas()
taraf_info = dataset2.to_pandas()
cities = taraf_info['City'].unique().tolist()

def subset_city_year( city = ['المدينه', 'بغداد', 'كوفة', 'بصرة'], year = 50):
  edges = edge_info[(taraf_info['Year'] == year) & (taraf_info['City'].isin(city))]
  return edges



def get_narrators( city =  ['المدينه', 'بغداد', 'كوفة', 'بصرة'], year = 50):
  df = subset_city_year(year, city)
  narrators = edge_info[edge_info['Edge_ID'].isin(df['ID'])]
  return narrators['Edge_Name'].reset_index().drop('index', axis = 1).rename(columns = {'Edge_Name': 'Teacher To Student'})

app = gr.Interface(get_narrators,
             [gr.Dropdown(choices = cities, value = ['المدينه', 'بغداد', 'كوفة', 'بصرة'], multiselect=True),
                        gr.Slider(min_year, max_year, value = 50, label = 'Begining', info = 'Choose The Year to Retrieve Narrators'),
                        ],
                       gr.Dataframe()).launch()