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

from datasets import load_dataset
from datasets import Features
from datasets import Value
from datasets import 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 = 'Taraf_Info.csv', token = Secret_token, split = 'train')
edge_info = dataset.to_pandas()
taraf_info = dataset2.to_pandas()
cities = taraf_info['City'].unique().tolist()
min_year = int(taraf_info['Year'].min())
max_year = int(taraf_info['Year'].max())


features = Features({'Rawi ID': Value('int32'), 'Famous Name': Value('string'), 'Narrator Rank': Value('string'), 'Number of Narrations': Value('string')})
narrator_bios = load_dataset("FDSRashid/hadith_info", data_files = 'Teacher_Bios.csv', token = Secret_token,features=features )
narrator_bios = narrator_bios['train'].to_pandas()
narrator_bios.loc[49845, 'Narrator Rank'] = 'رسول الله'
narrator_bios.loc[49845, 'Number of Narrations'] = 0
narrator_bios['Number of Narrations'] = narrator_bios['Number of Narrations'].astype(int)
narrator_bios.loc[49845, 'Number of Narrations'] = narrator_bios['Number of Narrations'].sum()


def subset_city_year(city, year1, year2):
  edges = taraf_info[(taraf_info['Year'] >= year1) & (taraf_info['City'].isin(city)) & (taraf_info['Year'] <= year2)]
  return edges

def subset_year(year = 50):
    edges = taraf_info[(taraf_info['Year'] == year)]
    return edges

def splitIsnad(dataframe):
    teacher_student =dataframe['Edge_Name'].str.split(' TO ')
    dataframe['Teacher'] = teacher_student.apply(lambda x: x[0])
    dataframe['Student'] = teacher_student.apply(lambda x: x[1])
    return dataframe

    

def get_narrators( city , year1, year2):
  try:
      df = subset_city_year(city, year1, year2)
      narrators = edge_info[edge_info['Edge_ID'].isin(df['ID'])]
      fixed = splitIsnad(narrators)
      fixed['Teacher Reports'] = fixed['Teacher_ID'].apply(lambda x: narrator_bios[narrator_bios['Rawi ID']== x]['Number of Narrations'].to_list()[0])
      fixed['Student Reports'] = fixed['Student_ID'].apply(lambda x: narrator_bios[narrator_bios['Rawi ID']== x]['Number of Narrations'].to_list()[0])
      return fixed[['Teacher', 'Student', 'Teacher Reports', 'Student Reports', 'Isnads', 'Hadiths', 'Tarafs', 'Books']]
  except Exception as e:
      return str(e)




with gr.Blocks() as demo:
  Places = gr.Dropdown(choices = cities, value = ['المدينه', 'بغداد', 'كوفة', 'بصرة'], multiselect=True, label = 'Location')
  First_Year = gr.Slider(min_year, max_year, value = 10, label = 'Begining', info = 'Choose the first year to display Narrators')
  Last_Year = gr.Slider(min_year, max_year, value = 50, label = 'End', info = 'Choose the Last year to display Narrators')
  btn = gr.Button('Submit')
  btn.click(fn = get_narrators, inputs = [Places, First_Year, Last_Year], outputs = gr.DataFrame())
  demo.launch()