app.py

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
import seaborn as sns
from matplotlib import pyplot as plt
import pickle

mean_citation_list = []

# Open the file and read the content in a list
with open('mean_citation_list.txt', 'r') as filehandle:
    for line in filehandle:
        temp = line[:-1]
        mean_citation_list.append(temp)

# # 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'
    
    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(theme=gr.themes.Soft()) as demo:
  with gr.Row():
      gr.Markdown(
      """
      <center><h2>Citational Amnesia</h2></center>
      <br>
      <center>Demo to predict the number of references, mean age of citation(mAoC), and comparison of mAoC with all the papers in the ACL Anthology. </center>
      <center>Kindly provide the Semantic Scholar ID of the paper to retrieve the results. </center>
      """
      )
  with gr.Row():
    ss_paper_id = gr.Textbox(label='Semantic Scholar ID',placeholder="Enter the Semantic Scholar ID here and press enter...", lines=1)
  with gr.Row():
    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")
  with gr.Row():
    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])
  
demo.queue(concurrency_count=3)
demo.launch()


# 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")
  
#   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()

# import openai
# import gradio

# openai.api_key = "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"

# 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

# 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()