Create app.py

app.py

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+import gradio as gr
+from pyvis.network import Network
+
+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('HF_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'] = 327512 
+
+
+
+edge_info = dataset.to_pandas()
+taraf_info = dataset2.to_pandas()
+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(fstyear, lstyear):
+    info = taraf_info[(taraf_info['Year'] >= fstyear)& (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_narrator(narrator_id, fst_year, lst_year, yaxis):
+    edges = subsetEdges(fst_year, lst_year)
+    edges_single = edges[edges['Teacher_ID']==narrator_id | edges['Student_ID']==narrator_id]
+    edges_prepped = splitIsnad(edges_single)
+    net = Network()
+    for _, row in edges_prepped.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=-3000, 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>"""
+
+def narrator_retriever(name):
+    return narrator_bios[narrator_bios['Official Name'] == name | narrator_bios['Famous Name'] == name][['Rawi ID', 'Title Name', 'Official Name', 'Famous Name', 'Number of Narrations' ]]
+    
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("Search Narrators using this tool or Visualize Network of a Narrator")
+    with gr.Tab("Search Narrator"):
+        text_input = gr.Textbox()
+        text_output = gr.DataFrame()
+        text_button = gr.Button("Search")
+    with gr.Tab("Visualize Network"):
+        with gr.Row():
+            image_input = gr.Number()
+            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')
+            Yaxis = gr.Dropdown(choices = ['Tarafs', 'Hadiths', 'Isnads', 'Books'], value = 'Tarafs', label = 'Variable to Display', info = 'Choose the variable to visualize.')
+            image_output = gr.HTML()
+        image_button = gr.Button("Visualize!")
+
+    text_button.click(narrator_retriever, inputs=text_input, outputs=text_output)
+    image_button.click(network_narrator, inputs=[image_input, FirstYear, Last_Year, Yaxis], outputs=image_output)
+
+demo.launch()
+