| 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 | |
| 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') | |
| 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, year): | |
| info = taraf_info[(taraf_info['Year'] == year) & (taraf_info['City'] == city)] | |
| 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(city, year, num_nodes): | |
| edge_15 = splitIsnad(subsetEdges(city, year))[['Teacher', 'Student', 'Hadiths']].groupby(['Teacher', 'Student']).sum().reset_index().sample(num_nodes) | |
| net = Network() | |
| for _, row in edge_15.iterrows(): | |
| source = row['Teacher'] | |
| target = row['Student'] | |
| attribute_value = row['Hadiths'] | |
| edge_color = value_to_hex(attribute_value) | |
| net.add_node(source, color=value_to_hex(attribute_value), font = {'size':30, 'color': 'orange'}) | |
| net.add_node(target, color=value_to_hex(attribute_value) , font = {'size': 20, 'color': 'red'}) | |
| net.add_edge(source, target, color=edge_color, value=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: | |
| Places = gr.Dropdown(choices = cities, value = 'ุงูู ุฏููู', label = 'Location') | |
| Last_Year = gr.Slider(min_year, max_year, value = 9, label = 'End', info = 'Choose the year to display Narrators') | |
| num_narrators = gr.Slider(0, 700, value = 400, label = 'Narrators', info = 'Choose the number of Narrators to display') | |
| btn = gr.Button('Submit') | |
| btn.click(fn = network_visualizer, inputs = [Places, Last_Year, num_narrators], outputs = gr.HTML()) | |
| demo.launch() |