Update app.py

app.py

@@ -1,142 +1,142 @@
-# import gradio as gr
-# import json
-# import os
-# import sys
-# import csv
-# import requests
-# import json
-# import pandas as pd
-# import concurrent.futures
-# from tqdm import tqdm
-# import shutil
-# import numpy as np
-# from matplotlib import pyplot as plt
-# import pickle
-
-
-# # Read list to memory
-# def read_list():
-#     # for reading also binary mode is important
-#     with open('mean_aoc_all_papers.pkl', 'rb') as fp:
-#         n_list = pickle.load(fp)
-#         return n_list
-
-# mean_citation_list = read_list()
-
-# def generate_plot_maoc(input_maoc):
-#   sns.set(font_scale = 8)
-#   sns.set(rc={'figure.figsize':(10,6)})
-#   sns.set_style(style='whitegrid')
-
-#   ax = sns.histplot(mean_citation_list, bins=100, kde=True, color='skyblue')
-#   kdeline = ax.lines[0]
-#   xs = kdeline.get_xdata()
-#   ys = kdeline.get_ydata()
-
-#   interpolated_y_maoc = np.interp(input_maoc, kdeline.get_xdata(), kdeline.get_ydata())
-#   ax.scatter(input_maoc, interpolated_y_maoc,c='r', marker='*',linewidths=5, zorder=2)
-#   ax.vlines(input_maoc, 0, interpolated_y_maoc, color='tomato', ls='--', lw=2)
-#   epsilon = 0.3
-#   ax.text(input_maoc + epsilon, interpolated_y_maoc + epsilon, 'Your paper', {'color': '#DC143C', 'fontsize': 13})
-#   ax.set_xlabel("mean Age of Citation(mAoC)",fontsize=15)
-#   ax.set_ylabel("Number of papers",fontsize=15)
-#   ax.tick_params(axis='both', which='major', labelsize=12)
-#   return plt
-
-# # sent a request
-# def request_to_respose(request_url):
-#   request_response = requests.get(request_url, headers={'x-api-key': 'qZWKkOKyzP5g9fgjyMmBt1MN2NTC6aT61UklAiyw'})
-#   return request_response
-
-# def return_clear():
-#   return None, None, None, None, None
-
-
-# def compute_output(ssid_paper_id):
-#   output_num_ref = 0
-#   output_maoc = 0
-#   oldest_paper_list = ""
-
-#   request_url = f'https://api.semanticscholar.org/graph/v1/paper/{ssid_paper_id}?fields=references,title,venue,year'
-#   r = request_to_respose(request_url)
-#   if r.status_code == 200: # if successful request
-#     s2_ref_paper_keys = [reference_paper_tuple['paperId'] for reference_paper_tuple in r.json()['references']]
-#     filtered_s2_ref_paper_keys = [s2_ref_paper_key for s2_ref_paper_key in s2_ref_paper_keys if s2_ref_paper_key is not None]
-#     total_references = len(s2_ref_paper_keys)
-#     none_references = (len(s2_ref_paper_keys) - len(filtered_s2_ref_paper_keys))
-#     s2_ref_paper_keys = filtered_s2_ref_paper_keys
-
-#     print(r.json())
-
-#     s2_paper_key, title, venue, year = r.json()['paperId'], r.json()['title'], r.json()['venue'], r.json()['year']
-#     reference_year_list = []
-#     reference_title_list = []
-#     for ref_paper_key in s2_ref_paper_keys:
-#       request_url_ref = f'https://api.semanticscholar.org/graph/v1/paper/{ref_paper_key}?fields=references,title,venue,year'
-#       r_ref = request_to_respose(request_url_ref)
-#       if r_ref.status_code == 200:
-#         s2_paper_key_ref, title_ref, venue_ref, year_ref = r_ref.json()['paperId'], r_ref.json()['title'], r_ref.json()['venue'], r_ref.json()['year']
-#         reference_year_list.append(year_ref)
-#         reference_title_list.append(title_ref)
+import gradio as gr
+import json
+import os
+import sys
+import csv
+import requests
+import json
+import pandas as pd
+import concurrent.futures
+from tqdm import tqdm
+import shutil
+import numpy as np
+from matplotlib import pyplot as plt
+import pickle
+
+
+# Read list to memory
+def read_list():
+    # for reading also binary mode is important
+    with open('mean_aoc_all_papers.pkl', 'rb') as fp:
+        n_list = pickle.load(fp)
+        return n_list
+
+mean_citation_list = read_list()
+
+def generate_plot_maoc(input_maoc):
+  sns.set(font_scale = 8)
+  sns.set(rc={'figure.figsize':(10,6)})
+  sns.set_style(style='whitegrid')
+
+  ax = sns.histplot(mean_citation_list, bins=100, kde=True, color='skyblue')
+  kdeline = ax.lines[0]
+  xs = kdeline.get_xdata()
+  ys = kdeline.get_ydata()
+
+  interpolated_y_maoc = np.interp(input_maoc, kdeline.get_xdata(), kdeline.get_ydata())
+  ax.scatter(input_maoc, interpolated_y_maoc,c='r', marker='*',linewidths=5, zorder=2)
+  ax.vlines(input_maoc, 0, interpolated_y_maoc, color='tomato', ls='--', lw=2)
+  epsilon = 0.3
+  ax.text(input_maoc + epsilon, interpolated_y_maoc + epsilon, 'Your paper', {'color': '#DC143C', 'fontsize': 13})
+  ax.set_xlabel("mean Age of Citation(mAoC)",fontsize=15)
+  ax.set_ylabel("Number of papers",fontsize=15)
+  ax.tick_params(axis='both', which='major', labelsize=12)
+  return plt
+
+# sent a request
+def request_to_respose(request_url):
+  request_response = requests.get(request_url, headers={'x-api-key': 'qZWKkOKyzP5g9fgjyMmBt1MN2NTC6aT61UklAiyw'})
+  return request_response
+
+def return_clear():
+  return None, None, None, None, None
+
+
+def compute_output(ssid_paper_id):
+  output_num_ref = 0
+  output_maoc = 0
+  oldest_paper_list = ""
+
+  request_url = f'https://api.semanticscholar.org/graph/v1/paper/{ssid_paper_id}?fields=references,title,venue,year'
+  r = request_to_respose(request_url)
+  if r.status_code == 200: # if successful request
+    s2_ref_paper_keys = [reference_paper_tuple['paperId'] for reference_paper_tuple in r.json()['references']]
+    filtered_s2_ref_paper_keys = [s2_ref_paper_key for s2_ref_paper_key in s2_ref_paper_keys if s2_ref_paper_key is not None]
+    total_references = len(s2_ref_paper_keys)
+    none_references = (len(s2_ref_paper_keys) - len(filtered_s2_ref_paper_keys))
+    s2_ref_paper_keys = filtered_s2_ref_paper_keys
+
+    # print(r.json())
+
+    s2_paper_key, title, venue, year = r.json()['paperId'], r.json()['title'], r.json()['venue'], r.json()['year']
+    reference_year_list = []
+    reference_title_list = []
+    for ref_paper_key in s2_ref_paper_keys:
+      request_url_ref = f'https://api.semanticscholar.org/graph/v1/paper/{ref_paper_key}?fields=references,title,venue,year'
+      r_ref = request_to_respose(request_url_ref)
+      if r_ref.status_code == 200:
+        s2_paper_key_ref, title_ref, venue_ref, year_ref = r_ref.json()['paperId'], r_ref.json()['title'], r_ref.json()['venue'], r_ref.json()['year']
+        reference_year_list.append(year_ref)
+        reference_title_list.append(title_ref)
     
-#     print(f'Number of references for which we got the year = {len(reference_year_list)}')
-#     output_num_ref = len(reference_year_list)
-#     aoc_list = [year - year_ref for year_ref in reference_year_list]
-#     output_maoc = sum(aoc_list)/len(aoc_list)
-
-#     sorted_ref_title_list = [x for _,x in sorted(zip(reference_year_list,reference_title_list))]
-#     sorted_ref_year_list = [x for x,_ in sorted(zip(reference_year_list,reference_title_list))]
-#     text = ""
-#     sorted_ref_title_list = sorted_ref_title_list[:min(len(sorted_ref_title_list), 5)]
-#     sorted_ref_year_list = sorted_ref_year_list[:min(len(sorted_ref_year_list), 5)]
-#     for i in range(len(sorted_ref_year_list)):
-#       text += '[' + str(sorted_ref_year_list[i]) + ']' + " Title: " + sorted_ref_title_list[i] + '\n'
+    # print(f'Number of references for which we got the year = {len(reference_year_list)}')
+    output_num_ref = len(reference_year_list)
+    aoc_list = [year - year_ref for year_ref in reference_year_list]
+    output_maoc = sum(aoc_list)/len(aoc_list)
+
+    sorted_ref_title_list = [x for _,x in sorted(zip(reference_year_list,reference_title_list))]
+    sorted_ref_year_list = [x for x,_ in sorted(zip(reference_year_list,reference_title_list))]
+    text = ""
+    sorted_ref_title_list = sorted_ref_title_list[:min(len(sorted_ref_title_list), 5)]
+    sorted_ref_year_list = sorted_ref_year_list[:min(len(sorted_ref_year_list), 5)]
+    for i in range(len(sorted_ref_year_list)):
+      text += '[' + str(sorted_ref_year_list[i]) + ']' + " Title: " + sorted_ref_title_list[i] + '\n'
     
-#     oldest_paper_list = text
-#     plot_maoc = generate_plot_maoc(output_maoc)
-#     print(plot_maoc)
+    oldest_paper_list = text
+    plot_maoc = generate_plot_maoc(output_maoc)
+    # print(plot_maoc)
   
-#   return output_num_ref, output_maoc, oldest_paper_list, gr.update(value=plot_maoc)
-
-
-# with gr.Blocks() as demo:
-#   ss_paper_id = gr.Textbox(label='Semantic Scholar ID',placeholder="Enter the Semantic Scholar ID here and press enter...", lines=1)
-#   submit_btn = gr.Button("Generate")
-#   with gr.Row():
-#     num_ref = gr.Textbox(label="Number of references")
-#     mAoc = gr.Textbox(label="Mean AoC")
-#   with gr.Row():
-#     oldest_paper_list = gr.Textbox(label="Top 5 oldest papers cited:",lines=5)
-#   with gr.Row():
-#     mAocPlot = gr.Plot(label="Plot")
+  return output_num_ref, output_maoc, oldest_paper_list, gr.update(value=plot_maoc)
+
+
+with gr.Blocks() as demo:
+  ss_paper_id = gr.Textbox(label='Semantic Scholar ID',placeholder="Enter the Semantic Scholar ID here and press enter...", lines=1)
+  submit_btn = gr.Button("Generate")
+  with gr.Row():
+    num_ref = gr.Textbox(label="Number of references")
+    mAoc = gr.Textbox(label="Mean AoC")
+  with gr.Row():
+    oldest_paper_list = gr.Textbox(label="Top 5 oldest papers cited:",lines=5)
+  with gr.Row():
+    mAocPlot = gr.Plot(label="Plot")
   
-#   clear_btn = gr.Button("Clear")
+  clear_btn = gr.Button("Clear")
   
-#   submit_btn.click(fn = compute_output, inputs = [ss_paper_id], outputs = [num_ref, mAoc, oldest_paper_list, mAocPlot])
-#   # clear_btn.click(lambda: None, None, None, queue=False)
-#   clear_btn.click(fn = return_clear, inputs=[], outputs=[ss_paper_id, num_ref, mAoc, oldest_paper_list, mAocPlot])
+  submit_btn.click(fn = compute_output, inputs = [ss_paper_id], outputs = [num_ref, mAoc, oldest_paper_list, mAocPlot])
+  # clear_btn.click(lambda: None, None, None, queue=False)
+  clear_btn.click(fn = return_clear, inputs=[], outputs=[ss_paper_id, num_ref, mAoc, oldest_paper_list, mAocPlot])
 
-# demo.launch()
+demo.launch()
 
-import openai
-import gradio
+# import openai
+# import gradio
 
-openai.api_key = "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"
+# openai.api_key = "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"
 
-messages = [{"role": "system", "content": "Anhub Online Education Tutor for Any Subjects:"}]
+# messages = [{"role": "system", "content": "Anhub Online Education Tutor for Any Subjects:"}]
 
-def CustomChatGPT(user_input):
-    messages.append({"role": "user", "content": user_input})
-    response = openai.ChatCompletion.create(
-        model = "gpt-3.5-turbo",
-        messages = messages
-    )
-    ChatGPT_reply = response["choices"][0]["message"]["content"]
-    messages.append({"role": "assistant", "content": ChatGPT_reply})
-    return ChatGPT_reply
+# def CustomChatGPT(user_input):
+#     messages.append({"role": "user", "content": user_input})
+#     response = openai.ChatCompletion.create(
+#         model = "gpt-3.5-turbo",
+#         messages = messages
+#     )
+#     ChatGPT_reply = response["choices"][0]["message"]["content"]
+#     messages.append({"role": "assistant", "content": ChatGPT_reply})
+#     return ChatGPT_reply
 
-demo = gradio.Interface(fn=CustomChatGPT, inputs = "text", outputs = "text", title = "Anhub Metaverse Education Online Tutor for Any Subjects and any Languages @ 24 x 7:")
+# demo = gradio.Interface(fn=CustomChatGPT, inputs = "text", outputs = "text", title = "Anhub Metaverse Education Online Tutor for Any Subjects and any Languages @ 24 x 7:")
 
 
 
-demo.launch()
+# demo.launch()