Update app.py

app.py

@@ -1,119 +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()
+
+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()