app.py

import gradio as gr
from pyvis.network import Network
import networkx as nx
import numpy as np
import pandas as pd
import os
from datasets import load_dataset
from datasets import Features
from datasets import Value
from datasets import Dataset
import matplotlib.pyplot as plt

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')



features = Features({'Rawi ID': Value('int32'), 'Famous Name': Value('string'), 'Narrator Rank': Value('string'), 'Number of Narrations': Value('string'),  'Generation': 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'] = 443471


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())
cmap = plt.colormaps['cool']
def value_to_hex(value):
    rgba_color = cmap(value)
    return "#{:02X}{:02X}{:02X}".format(int(rgba_color[0] * 255), int(rgba_color[1] * 255), int(rgba_color[2] * 255))

def subsetEdges(city, fstyear, lstyear):
  info = taraf_info[(taraf_info['Year'] >= fstyear) & (taraf_info['City'] == city) & (taraf_info['Year'] <= lstyear)]
  narrators = edge_info[edge_info['Edge_ID'].isin(info['ID'].unique())]
  return narrators

def get_node_info(node):
    node_info = narrator_bios[narrator_bios['Rawi ID'] == int(node)]
    student_narrations = node_info['Number of Narrations'].to_list()
    if len(student_narrations):
        student_narrations = student_narrations[0]
    else:
        student_narrations = 1
    student_gen = node_info['Generation'].to_list()
    if len(student_gen):
        student_gen = student_gen[0]
    else:
        student_gen = -1
    student_rank = node_info["Narrator Rank"].to_list()
    if len(student_rank):
        student_rank = student_rank[0]
    else:
        student_rank = 'فلان'
    node_name = node_info['Famous Name'].to_list()
    if len(node_name):
        node_name = node_name[0]
    else:
        node_name = 'فلان'
    return node_info,student_narrations,student_gen, student_rank, node_name

def network_visualizer(yaxis, city, fstyear,lastyr, num_nodes):
    edges = subsetEdges(city, fstyear, lastyr).sort_values(yaxis, ascending=False).head(num_nodes)
    G = nx.from_pandas_edgelist(edges, source = 'Teacher_ID', target = 'Student_ID', create_using = nx.DiGraph())
    # nodes = list(G.nodes)
    # node_reports = [narrator_bios[narrator_bios['Rawi ID'].astype(int) == int(x)]['Number of Narrations'].to_list()[0] for x in nodes]
    # nodes_df = pd.DataFrame({'Node': nodes, 'Report': node_reports}).sort_values('Report', ascending=False).head(num_nodes)
    # nodes_remove = list(set(nodes) - set(nodes_df['Node'].to_list()))
    # [G.remove_nodes_from(nodes_remove)]
    
    #.groupby(['Teacher', 'Student']).sum()
    # if edges_split.shape[0] > num_nodes:
    #     edge_15 = edges_split.sort_values(by=yaxis, ascending=False).head(num_nodes)
    # else:
    #     edge_15 = edges_split.copy()

    net = Network(directed =True, select_menu=True, cdn_resources='remote')
    for node in G.nodes:
        node_info,student_narrations,student_gen, student_rank, node_name = get_node_info(node)
        if node == 99999:
            net.add_node(int(node), font = {'size':50, 'color': 'black'}, color = '#000000', label = f'{node_name} \n ID: {node} - Gen {student_gen}', size= 70)  
        else:
            net.add_node(int(node), font = {'size':30, 'color': 'red'}, color = value_to_hex(student_narrations), label = f'{node_name} \n {student_rank} \n ID: {node} - Gen {student_gen}', size= 50)
    for edge in G.edges:
        row = edges[(edges['Teacher_ID'] == edge[0]) & (edges['Student_ID'] == edge[1])].iloc[0]
        source = row['Teacher_ID']
        target = row['Student_ID']
        net.add_edge(int(source), int(target), color = value_to_hex(int(row[yaxis])), label = f"{row[yaxis]}", value= int(row[yaxis]))
    
    # for _, row in edge_15.iterrows():
    #     source = row['Teacher']
    #     target = row['Student']
    #     attribute_value = row[yaxis]
    #     edge_color = value_to_hex(attribute_value)
    #     teacher_info = narrator_bios[narrator_bios['Rawi ID'] == row['Teacher_ID']]
    #     student_info = narrator_bios[narrator_bios['Rawi ID'] == row['Student_ID']]
    #     teacher_narrations = teacher_info['Number of Narrations'].to_list()[0]
    #     student_narrations = student_info['Number of Narrations'].to_list()[0]
        
    #     net.add_node(source, color=value_to_hex(teacher_narrations), font = {'size':30, 'color': 'orange'}, label = f"{source}\n{teacher_narrations}")
    #     net.add_node(target, color=value_to_hex(student_narrations), font = {'size': 20, 'color': 'red'}, label = f"{target}\n{student_narrations}")
    #     net.add_edge(source, target, color=edge_color, value=attribute_value, label = f"{yaxis}:{attribute_value}")


    net.barnes_hut(gravity=-5000, central_gravity=0.1, spring_length=200)
    html = net.generate_html()
    html = html.replace("'", "\"")

    
    return f"""<iframe style="width: 100%; height: 600px;margin:0 auto" name="result" allow="midi; geolocation; microphone; camera; 
    display-capture; encrypted-media;" sandbox="allow-modals allow-forms 
    allow-scripts allow-same-origin allow-popups 
    allow-top-navigation-by-user-activation allow-downloads" allowfullscreen="" 
    allowpaymentrequest="" frameborder="0" srcdoc='{html}'></iframe>"""


with gr.Blocks() as demo:
  Yaxis = gr.Dropdown(choices = ['Tarafs', 'Hadiths', 'Isnads', 'Books'], value = 'Tarafs', label = 'Variable to Display', info = 'Choose the variable to visualize.')  
  Places = gr.Dropdown(choices = cities, value = 'المدينه', label = 'Location')
  FirstYear = gr.Slider(min_year, max_year, value = -11, label = 'Begining', info = 'Choose the first year to display Narrators')
  Last_Year = gr.Slider(min_year, max_year, value = 9, label = 'End', info = 'Choose the  last year to display Narrators')
  num_narrators = gr.Slider(0, 2000, value = 400, label = 'Transmissions', info = 'Choose the  number of Transmissions to display')  
    
  btn = gr.Button('Submit')
  btn.click(fn = network_visualizer, inputs = [Yaxis, Places, FirstYear, Last_Year, num_narrators], outputs = gr.HTML())
  demo.launch()