Update app.py

app.py

@@ -1,5 +1,4 @@
 import gradio as gr
-from pyvis.network import Network
 import pyarabic.araby as araby
 import numpy as np
 import pandas as pd
@@ -15,7 +14,6 @@ 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')
 
 lst = ['Rawi ID',
  'Gender',
@@ -52,27 +50,19 @@ 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 
+narrator_bios.loc[49845, 'Number of Narrations'] = 22000 
 narrator_bios['Generation'] = narrator_bios['Generation'].replace([None], [-1])
 narrator_bios['Generation'] = narrator_bios['Generation'].astype(int)
 
 
 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 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])
@@ -80,36 +70,13 @@ def splitIsnad(dataframe):
     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(directed =True)
-    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("'", "\"")
-
+def network_narrator(narrator_id):
+    # edges = subsetEdges(fst_year, lst_year)
+    # edges_single = edges[(edges['Teacher_ID']==narrator_id) | (edges['Student_ID']==narrator_id)]
+    # edges_prepped = splitIsnad(edges_single)
     edge_narrator = edge_info[(edge_info['Teacher_ID'] == narrator_id) | (edge_info['Student_ID'] == narrator_id)]
     edge_full = splitIsnad(edge_narrator[['Tarafs', 'Hadiths', 'Isnads', 'Edge_Name', 'Books']]).drop(['Edge_Name'], axis=1)
-    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>""", edge_full
+    return edge_full
 
 def narrator_retriever(name):
         return narrator_bios[(narrator_bios['Official Name'].apply(lambda x: araby.strip_diacritics(x)).str.contains(araby.strip_diacritics(name))) | (narrator_bios['Famous Name'].apply(lambda x: araby.strip_diacritics(x)).str.contains(araby.strip_diacritics(name))) | (narrator_bios['Rawi ID'].astype(str) == name)]
@@ -117,7 +84,7 @@ def narrator_retriever(name):
 
 
 with gr.Blocks() as demo:
-    gr.Markdown("Search Narrators using this tool or Visualize Network of a Narrator")
+    gr.Markdown("Search Narrators using this tool or Retrieve Transmissions involving Narrator")
     with gr.Tab("Search Narrator"):
         text_input = gr.Textbox()
         text_output = gr.DataFrame()
@@ -125,14 +92,9 @@ with gr.Blocks() as demo:
         text_button.click(narrator_retriever, inputs=text_input, outputs=text_output)
 
     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!")
-        image_button.click(network_narrator, inputs=[image_input, FirstYear, Last_Year, Yaxis], outputs=[image_output, gr.DataFrame()])
+        image_input = gr.Number()
+        image_button = gr.Button("Retrieve!")
+        image_button.click(network_narrator, inputs=[image_input], outputs=[gr.DataFrame(wrap=True)])