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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')})
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 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 network_visualizer(yaxis, city, fstyear,lastyr, num_nodes):
edges = subsetEdges(city, fstyear, lastyr)
edges_split = splitIsnad(edges).reset_index()
#.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()
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=0.3, 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 = 'Narrators', info = 'Choose the number of Narrators to display')
btn = gr.Button('Submit')
btn.click(fn = network_visualizer, inputs = [Yaxis, Places, FirstYear, Last_Year, num_narrators], outputs = gr.HTML())
demo.launch() |