# 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' # 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") # 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()