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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 | |
import re | |
from collections import defaultdict | |
from huggingface_hub import hf_hub_download | |
import json | |
pattern = r'"(.*?)"' | |
# this pattern captures anything in a double quotes. | |
Secret_token = os.getenv('HF_token') | |
dataset = load_dataset('FDSRashid/hadith_info',data_files = 'Basic_Edge_Information.csv', token = Secret_token, split = 'train') | |
edge_info = dataset.to_pandas() | |
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'] = 'ุฑุณูู ุงููู' | |
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'] = 327512 | |
# 8125 Narrators have no Generation, listed in dataset as None | |
narrator_bios['Generation'] = narrator_bios['Generation'].replace([None], [-1]) | |
narrator_bios['Generation'] = narrator_bios['Generation'].astype(int) | |
features = Features({'matn': Value('string'), 'taraf_ID': Value('string'), 'bookid_hadithid': Value('string')}) | |
dataset = load_dataset("FDSRashid/hadith_info", data_files = 'All_Matns.csv',token = Secret_token, features = features) | |
matn_info = dataset['train'].to_pandas() | |
matn_info = matn_info.drop(97550) | |
matn_info = matn_info.drop(307206) | |
matn_info['taraf_ID'] = matn_info['taraf_ID'].replace('KeyAbsent', -1) | |
matn_info['taraf_ID'] = matn_info['taraf_ID'].astype(int) | |
# Isnad Info Hadiths column is structured like {"BookNum_HadithNum", ...} for each edge | |
isnad_info = load_dataset('FDSRashid/hadith_info',token = Secret_token, data_files = 'isnad_info.csv', split = 'train').to_pandas() | |
isnad_info['Hadiths Cleaned'] = isnad_info['Hadiths'].apply(lambda x: [re.findall(pattern, string)[0].split("_") for string in x[1:-1].split(',')]) | |
# Hadiths Cleaned is a list of lists, each sub-list is Book Id, Hadith ID | |
taraf_max = np.max(matn_info['taraf_ID'].unique()) | |
isnad_info['Tarafs Cleaned'] = isnad_info['Tarafs'].apply(lambda x: np.array([int(i.strip(' ')) for i in x[1:-1].split(',')])) | |
cmap = plt.colormaps['cool'] | |
books = load_dataset('FDSRashid/Hadith_info', data_files='Books.csv', token = Secret_token)['train'].to_pandas() | |
matn_info['Book_ID'] = matn_info['bookid_hadithid'].apply(lambda x: int(x.split('_')[0])) | |
matn_info['Hadith Number'] = matn_info['bookid_hadithid'].apply(lambda x: int(x.split('_')[1])) | |
matn_info = pd.merge(matn_info, books, on='Book_ID') | |
# Preprocess narrator_bios into a dictionary | |
narrator_info = narrator_bios.set_index('Rawi ID').to_dict(orient='index') | |
# Download and read a file | |
file_path = hf_hub_download( | |
repo_id="FDSRashid/hadith_info", # read in fast lookup data structure | |
filename="hadith_lookup.json", | |
repo_type="dataset", | |
token=Secret_token, | |
) | |
with open(file_path, 'r') as f: | |
hadith_lookup_dict = json.load(f) | |
HADITH_LOOKUP = defaultdict(list, hadith_lookup_dict) | |
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 get_node_info(node): | |
node = int(node) # Ensure node is an integer | |
info = narrator_info.get(node, {}) | |
student_narrations = info.get('Number of Narrations', 1) | |
student_gen = info.get('Generation', -1) | |
student_rank = info.get('Narrator Rank', 'ููุงู') | |
node_name = info.get('Famous Name', 'ููุงู') | |
return info, student_narrations, student_gen, student_rank, node_name | |
def lookup_hadith(taraf_hadith, hadith_lookup): | |
""" | |
Returns a list of unique elements from the hadith_lookup for the given taraf_hadith. | |
Parameters: | |
taraf_hadith (str or list of str): A string or list of strings to look up. | |
hadith_lookup (defaultdict): A defaultdict containing the hadith data. | |
Returns: | |
list: A list of unique elements from the lookup results. | |
""" | |
# Ensure taraf_hadith is always a list | |
if isinstance(taraf_hadith, str): | |
taraf_hadith = [taraf_hadith] | |
# Create a set to accumulate unique elements | |
unique_elements = {elem for key in taraf_hadith for elem in hadith_lookup[key]} | |
# Convert the set to a list for consistency | |
return list(unique_elements) | |
def visualize_isnad(taraf_num, yaxis): | |
# Precompute filtered dataframes | |
taraf = matn_info[matn_info['taraf_ID'] == taraf_num] | |
taraf_hadith = taraf['bookid_hadithid'].to_list() | |
# Precompute hadiths where taraf_num exists | |
hadith_cleaned = isnad_info['Tarafs Cleaned'].apply(lambda x: taraf_num in x) | |
isnad_hadith = isnad_info[hadith_cleaned] | |
lst_hadith = [] | |
for i, hadith_parts in enumerate(taraf_hadith): | |
# look up hadith for each bookid_hadithid | |
isnad_hadith1 = isnad_info.iloc[lookup_hadith(taraf_hadith[i], HADITH_LOOKUP)][['Source', 'Destination']] | |
# Create graph and find end nodes | |
G = nx.from_pandas_edgelist(isnad_hadith1, source='Source', target='Destination', create_using=nx.DiGraph()) | |
nodes = [int(n) for n, d in G.out_degree() if d == 0] | |
if nodes: | |
# Batch fetch data from narrator_bios for efficiency | |
bio_data = narrator_bios[narrator_bios['Rawi ID'].isin(nodes)] | |
for n in nodes: | |
gen_node = bio_data.loc[bio_data['Rawi ID'] == n, 'Generation'].squeeze() | |
gen_node = gen_node if pd.notna(gen_node) else -1 | |
name_node = bio_data.loc[bio_data['Rawi ID'] == n, 'Famous Name'].squeeze() | |
name_node = name_node if pd.notna(name_node) else 'ููุงู' | |
# Append result for each node | |
lst_hadith.append([ | |
taraf.iloc[i]['matn'], | |
gen_node, | |
name_node, | |
taraf.iloc[i]['Book_Name'], | |
taraf.iloc[i]['Author'], | |
taraf.iloc[i]['Hadith Number'], | |
n, | |
i | |
]) | |
# Convert to DataFrame | |
df = pd.DataFrame(lst_hadith, columns=['Matn', 'Generation', 'Name', 'Book_Name', 'Author', 'Book Hadith Number', 'End Transmitter ID', 'Hadith Number']) | |
isnad_hadith[['Source', 'Destination']] = isnad_hadith[['Source', 'Destination']].astype(int) | |
# Merge isnad_hadith with narrator_bios for Teacher and Student | |
isnad_hadith = isnad_hadith.merge(narrator_bios[['Rawi ID', 'Famous Name']], left_on='Source', right_on='Rawi ID', how='left').rename(columns={'Famous Name': 'Teacher'}) | |
isnad_hadith = isnad_hadith.merge(narrator_bios[['Rawi ID', 'Famous Name']], left_on='Destination', right_on='Rawi ID', how='left').rename(columns={'Famous Name': 'Student'}) | |
isnad_hadith[['Source', 'Destination']] = isnad_hadith[['Source', 'Destination']].astype(str) | |
# Fill missing values with 'ููุงู' | |
# isnad_hadith['Teacher'].fillna('ููุงู', inplace=True) | |
# isnad_hadith['Student'].fillna('ููุงู', inplace=True) | |
end_nodes = df['End Transmitter ID'].tolist() | |
G = nx.from_pandas_edgelist(isnad_hadith, source = 'Source', target = 'Destination', create_using = nx.DiGraph()) | |
isnad_pos = nx.nx_agraph.graphviz_layout(G, prog='dot') | |
x_stretch = 4 | |
y_stretch = 4 | |
net = Network(directed =True, select_menu=True, cdn_resources='remote') | |
# Precompute end_matn_info for each end node | |
end_node_data = df.groupby('End Transmitter ID').apply(lambda x: " ".join(x["Hadith Number"].astype("string"))).to_dict() | |
# Loop over isnad_pos | |
for node, pos in isnad_pos.items(): | |
node_info, student_narrations, student_gen, student_rank, node_name = get_node_info(node) | |
label = f'{node_name} \n {student_rank} \n ID: {node} - Gen {student_gen}' | |
size = 50 | |
font_color = 'red' | |
if node == '99999': | |
label = f'{node_name} \n ID: {node} - Gen {student_gen}' | |
size = 70 | |
font_color = 'black' | |
elif int(node) in end_nodes: | |
hadith_numbers = end_node_data.get(int(node), '') | |
label += f' \n Hadith {hadith_numbers}' | |
net.add_node(node, font={'size': 30, 'color': font_color}, color=value_to_hex(student_narrations), label=label, x=pos[0] * x_stretch, y=-pos[1] * y_stretch, size=size) | |
# Add edges efficiently | |
edge_data = isnad_hadith[['Source', 'Destination', f'{yaxis} Count']].values | |
for source, target, count in edge_data: | |
net.add_edge(source, target, color=value_to_hex(int(count)), label=f"{count}") | |
net.toggle_physics(False) | |
html = net.generate_html() | |
html = html.replace("'", "\"") | |
df = df.rename(columns = {'Generation': 'Gen.', 'Book Hadith Number': 'Hdth Num', 'End Transmitter ID': 'End Narrator ID', 'Hadith Number': 'Index', 'Book_Name': 'Book', 'Name':'Final Narrator'}) | |
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>""" , df.drop('Hdth Num', axis=1) | |
def visualize_subTaraf(taraf_num, hadith_str, yaxis): | |
hadith_list = hadith_str.split(',') | |
hadith_list = [hadith.strip() for hadith in hadith_list] | |
hadiths = np.array([], dtype=int) | |
for hadith in hadith_list: | |
if '-' in hadith: | |
if hadith.count('-') > 1: | |
#print('Please use only one Dash mark!') | |
raise gr.Error('Please use only one Dash mark!') | |
hadith_multi = hadith.strip().split('-') | |
if any([not had.isnumeric() for had in hadith_multi]): | |
#print('Invalid Begining') | |
raise gr.Error('Invalid Begining') | |
elif len(hadith_multi) != 2: | |
#print('Two numbers for a range of Hadith numbers please!') | |
raise gr.Error('Two numbers for a range of Hadith numbers please!') | |
hadith_multi = [int(had) for had in hadith_multi] | |
hadiths = np.append(hadiths, np.arange(hadith_multi[0], hadith_multi[1] +1)) | |
elif hadith.isnumeric(): | |
hadiths = np.append(hadiths, int(hadith)) | |
else: | |
#print('Invalid Data format!') | |
raise gr.Error("Invalid Data format!") | |
hadiths= np.unique(hadiths) | |
taraf = matn_info[matn_info['taraf_ID'] == taraf_num] | |
num_hadith = taraf.shape[0] | |
if np.max(hadiths) > num_hadith: | |
raise gr.Error(f'Hadith index outside of range. Total Number of Hadith in this Taraf: {num_hadith}') | |
taraf['Index'] = np.arange(num_hadith) | |
sub_taraf = taraf[taraf['Index'].isin(hadiths)] | |
isnad_hadith = isnad_info.iloc[lookup_hadith(sub_taraf['bookid_hadithid'].to_list(), HADITH_LOOKUP)] | |
isnad_hadith[['Source', 'Destination']] = isnad_hadith[['Source', 'Destination']].astype(int) | |
# Merge isnad_hadith with narrator_bios for Teacher and Student | |
isnad_hadith = isnad_hadith.merge(narrator_bios[['Rawi ID', 'Famous Name']], left_on='Source', right_on='Rawi ID', how='left').rename(columns={'Famous Name': 'Teacher'}) | |
isnad_hadith = isnad_hadith.merge(narrator_bios[['Rawi ID', 'Famous Name']], left_on='Destination', right_on='Rawi ID', how='left').rename(columns={'Famous Name': 'Student'}) | |
isnad_hadith[['Source', 'Destination']] = isnad_hadith[['Source', 'Destination']].astype(str) | |
# isnad_hadith['Teacher'].fillna('ููุงู', inplace=True) | |
# isnad_hadith['Student'].fillna('ููุงู', inplace=True) | |
G = nx.from_pandas_edgelist(isnad_hadith, source = 'Source', target = 'Destination', create_using = nx.DiGraph()) | |
isnad_pos = nx.nx_agraph.graphviz_layout(G, prog='dot') | |
x_stretch = 4 | |
y_stretch = 4 | |
net = Network(directed =True, select_menu=True, cdn_resources='remote') | |
for node, pos in isnad_pos.items(): | |
node_info,student_narrations,student_gen, student_rank, node_name = get_node_info(node) | |
if node == '99999': | |
net.add_node(node, font = {'size':50, 'color': 'black'}, color = '#000000', label = f'{node_name} \n ID: {node} - Gen {student_gen}', x= pos[0]*x_stretch, y= -1*pos[1]*y_stretch, size= 70) | |
else: | |
net.add_node(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}', x= pos[0]*x_stretch, y= -1*pos[1]*y_stretch, size= 50) | |
for _, row in isnad_hadith.iterrows(): | |
source = row['Source'] | |
target = row['Destination'] | |
net.add_edge(source, target, color = value_to_hex(int(row[f'{yaxis} Count'])), label = f"{row[f'{yaxis} Count']}") | |
net.toggle_physics(False) | |
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>""", sub_taraf[['matn', 'Book_Name', 'Author', 'Book_ID', 'Hadith Number']] | |
def taraf_booknum(taraf_num): | |
taraf = matn_info[matn_info['taraf_ID'] == taraf_num] | |
num_hadith = taraf.shape[0] | |
taraf['Index'] = np.arange(num_hadith) | |
return taraf[['matn', 'Book_ID', 'Hadith Number', 'Book_Name', 'Author', 'Index']] | |
def visualize_hadith_isnad(df, yaxis): | |
df['bookid_hadithid'] = df['Book_ID'].astype(str) + '_' + df['Hadith Number'].astype(str) | |
hadith = matn_info[matn_info['bookid_hadithid'].isin(df['bookid_hadithid'])] | |
taraf_hadith = df['bookid_hadithid'].to_list() | |
isnad_hadith = isnad_info.iloc[lookup_hadith(taraf_hadith, HADITH_LOOKUP)] | |
isnad_hadith[['Source', 'Destination']] = isnad_hadith[['Source', 'Destination']].astype(int) | |
# Merge isnad_hadith with narrator_bios for Teacher and Student | |
isnad_hadith = isnad_hadith.merge(narrator_bios[['Rawi ID', 'Famous Name']], left_on='Source', right_on='Rawi ID', how='left').rename(columns={'Famous Name': 'Teacher'}) | |
isnad_hadith = isnad_hadith.merge(narrator_bios[['Rawi ID', 'Famous Name']], left_on='Destination', right_on='Rawi ID', how='left').rename(columns={'Famous Name': 'Student'}) | |
isnad_hadith[['Source', 'Destination']] = isnad_hadith[['Source', 'Destination']].astype(str) | |
# isnad_hadith['Teacher'].fillna('ููุงู', inplace=True) | |
# isnad_hadith['Student'].fillna('ููุงู', inplace=True) | |
G = nx.from_pandas_edgelist(isnad_hadith, source = 'Source', target = 'Destination', create_using = nx.DiGraph()) | |
isnad_pos = nx.nx_agraph.graphviz_layout(G, prog='dot') | |
x_stretch = 4 | |
y_stretch = 4 | |
net = Network(directed =True, select_menu=True, cdn_resources='remote') | |
for node, pos in isnad_pos.items(): | |
node_info,student_narrations,student_gen, student_rank, node_name = get_node_info(node) | |
if node == '99999': | |
net.add_node(node, font = {'size':50, 'color': 'black'}, color = '#000000', label = f'{node_name} \n ID: {node} - Gen {student_gen}', x= pos[0]*x_stretch, y= -1*pos[1]*y_stretch, size= 70) | |
else: | |
net.add_node(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}', x= pos[0]*x_stretch, y= -1*pos[1]*y_stretch, size= 50) | |
for _, row in isnad_hadith.iterrows(): | |
source = row['Source'] | |
target = row['Destination'] | |
net.add_edge(source, target, color = value_to_hex(int(row[f'{yaxis} Count'])), label = f"{row[f'{yaxis} Count']}") | |
net.toggle_physics(False) | |
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>""" , hadith[['matn', 'Book_ID', 'Hadith Number', 'Book_Name', 'Author', 'taraf_ID']] | |
def visualize_narrator_taraf(taraf_num, narrator, yaxis): | |
taraf = matn_info[matn_info['taraf_ID'] == taraf_num].copy() | |
taraf['Index'] = np.arange(len(taraf)) | |
hadith_cleaned = isnad_info['Tarafs Cleaned'].apply(lambda x: taraf_num in x) | |
isnad_hadith = isnad_info[hadith_cleaned] | |
isnad_hadith[['Source', 'Destination']] = isnad_hadith[['Source', 'Destination']].astype(int) | |
# Merge isnad_hadith with narrator_bios for Teacher and Student | |
isnad_hadith = isnad_hadith.merge(narrator_bios[['Rawi ID', 'Famous Name']], left_on='Source', right_on='Rawi ID', how='left').rename(columns={'Famous Name': 'Teacher'}) | |
isnad_hadith = isnad_hadith.merge(narrator_bios[['Rawi ID', 'Famous Name']], left_on='Destination', right_on='Rawi ID', how='left').rename(columns={'Famous Name': 'Student'}) | |
isnad_hadith[['Source', 'Destination']] = isnad_hadith[['Source', 'Destination']].astype(str) | |
taraf_hadith = taraf['bookid_hadithid'].to_list() | |
# original graph of whole taraf | |
G = nx.from_pandas_edgelist(isnad_hadith, source = 'Source', target = 'Destination', create_using = nx.DiGraph()) | |
if narrator not in G.nodes(): | |
raise gr.Error('Narrator not in Isnad of Taraf!') | |
matns_with_narrator = [] | |
end_node = {} | |
# Process each hadith in taraf_hadith_split | |
for idx, split_hadith in enumerate(taraf_hadith): | |
isnad_hadith1 = isnad_info.iloc[lookup_hadith(taraf_hadith[idx], HADITH_LOOKUP)] | |
G1 = nx.from_pandas_edgelist(isnad_hadith1, source='Source', target='Destination', create_using=nx.DiGraph()) | |
if narrator in G1.nodes: | |
matns_with_narrator.append(taraf_hadith[idx]) | |
for node in (n for n, d in G1.out_degree() if d == 0): | |
end_node.setdefault(node, []).append(str(idx)) | |
# Update the graph | |
list_of_lists = [hadith_lookup[i] for i in matns_with_narrator] | |
flattened = list(set([elem for sublist in list_of_lists for elem in sublist])) | |
isnad_hadith = isnad_info.iloc[flattened][['Source', 'Destination']] | |
G = nx.from_pandas_edgelist(isnad_hadith, source = 'Source', target = 'Destination', create_using = nx.DiGraph()) | |
isnad_pos = nx.nx_agraph.graphviz_layout(G, prog='dot') | |
narrator_matn_info = taraf[taraf['bookid_hadithid'].isin(matns_with_narrator)] | |
narrator_matn_info['Subset Index'] = np.arange(len(narrator_matn_info)) | |
# Visualization with pyvis | |
x_stretch = 4 | |
y_stretch = 4 | |
net = Network(directed =True, select_menu=True, cdn_resources='remote') | |
for node, pos in isnad_pos.items(): | |
node_info, student_narrations, student_gen, student_rank, node_name = get_node_info(node) | |
label = f'{node_name} \n ID: {node} - Gen {student_gen}' | |
size = 70 if node == '99999' else 50 | |
font_color = 'black' if node == '99999' else 'red' | |
hadiths = f" \n Hadiths {', '.join(end_node[node])}" if node in end_node else '' | |
net.add_node(node, font={'size': 30, 'color': font_color}, color=value_to_hex(student_narrations), | |
label=f"{label} {hadiths}", x=pos[0] * x_stretch, y=-pos[1] * y_stretch, size=size) | |
for edge in G.edges: | |
row = isnad_hadith[(isnad_hadith['Source'] == edge[0]) & (isnad_hadith['Destination'] == edge[1])].iloc[0] | |
net.add_edge(edge[0], edge[1], color=value_to_hex(int(row[f'{yaxis} Count'])), label=f"{row[f'{yaxis} Count']}") | |
net.toggle_physics(False) | |
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>""" , narrator_matn_info[['matn', 'Book_Name', 'Author', 'Book_ID', 'Hadith Number', 'Index', 'Subset Index']] | |
with gr.Blocks() as demo: | |
with gr.Tab("Whole Taraf Visualizer"): | |
Yaxis = gr.Dropdown(choices = ['Taraf', 'Hadith', 'Isnad', 'Book'], value = 'Taraf', label = 'Variable to Display', info = 'Choose the variable to visualize.') | |
taraf_number = gr.Slider(1,taraf_max , value=10000, label="Taraf", info="Choose the Taraf to Input", step = 1) | |
btn = gr.Button('Submit') | |
# | |
btn.click(fn = visualize_isnad, inputs = [taraf_number, Yaxis], outputs = [gr.HTML(), gr.DataFrame(wrap=True, column_widths=[43, 8, 11,11,10,8, 9])]) | |
with gr.Tab("Book and Hadith Number Retriever"): | |
taraf_num = gr.Slider(1,taraf_max , value=10000, label="Taraf", info="Choose the Taraf to Input", step = 1) | |
btn_num = gr.Button('Retrieve') | |
btn_num.click(fn=taraf_booknum, inputs = [taraf_num], outputs= [gr.DataFrame(wrap=True)]) | |
with gr.Tab('Sub Taraf Visualizer'): | |
taraf_num = gr.Slider(1,taraf_max , value=10000, label="Taraf", info="Choose the Taraf to Input", step = 1) | |
Yaxis = gr.Dropdown(choices = ['Taraf', 'Hadith', 'Isnad', 'Book'], value = 'Taraf', label = 'Variable to Display', info = 'Choose the variable to visualize.') | |
hadith_str = gr.Textbox(label='Hadith Selection', info='Choose which range of Hadith you would like visualized from the Taraf (eg "1, 2, 4-7")') | |
btn_sub = gr.Button('Visualize') | |
btn_sub.click(fn=visualize_subTaraf, inputs = [taraf_num, hadith_str, Yaxis], outputs=[gr.HTML(), gr.DataFrame(wrap=True)]) | |
with gr.Tab('Select Hadith Isnad Visualizer'): | |
yyaxis = gr.Dropdown(choices = ['Taraf', 'Hadith', 'Isnad', 'Book'], value = 'Taraf', label = 'Variable to Display', info = 'Choose the variable to visualize.') | |
hadith_selection = gr.Dataframe( | |
headers=["Book_ID", "Hadith Number"], | |
datatype=["number", "number"], | |
row_count=5, | |
col_count=(2, "fixed")) | |
btn_hadith = gr.Button('Visualize') | |
btn_hadith.click(fn=visualize_hadith_isnad, inputs=[hadith_selection, yyaxis], outputs=[gr.HTML(), gr.DataFrame(wrap=True)]) | |
with gr.Tab('Taraf Narrator Isnad Visualizer'): | |
Yaxis = gr.Dropdown(choices = ['Taraf', 'Hadith', 'Isnad', 'Book'], value = 'Taraf', label = 'Variable to Display', info = 'Choose the variable to visualize.') | |
taraf_number = gr.Slider(1,taraf_max , value=10000, label="Taraf", info="Choose the Taraf to Input", step = 1) | |
narr = gr.Textbox(label='Narrator', info='Choose a Narrator (Refer to full isnad from previous tab)') | |
btn_narr = gr.Button('Visualize') | |
btn_narr.click(fn=visualize_narrator_taraf, inputs=[taraf_number, narr, Yaxis], outputs=[gr.HTML(), gr.DataFrame(wrap=True)]) | |
demo.launch() | |