Hugging Face's logo

Spaces:
kiyer
/
pathfinder
Running on CPU Upgrade

App Files Community
6
pathfinder
File size: 5,096 Bytes 
27b7558
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e75fcb6
27b7558
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e75fcb6
27b7558
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
 
import os
import datetime
import faiss
import streamlit as st
import feedparser
import urllib
import cloudpickle as cp
import pickle
from urllib.request import urlopen
from summa import summarizer
import numpy as np
import matplotlib.pyplot as plt
import requests
import json

from langchain_openai import AzureOpenAIEmbeddings
from langchain.llms import OpenAI
from langchain_openai import AzureChatOpenAI

os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["AZURE_ENDPOINT"] = st.secrets["endpoint1"]
os.environ["OPENAI_API_KEY"] = st.secrets["key1"]
os.environ["OPENAI_API_VERSION"] = "2023-05-15"

embeddings = AzureOpenAIEmbeddings(
    deployment="embedding",
    model="text-embedding-ada-002",
    azure_endpoint=st.secrets["endpoint1"],
)

llm = AzureChatOpenAI(
        deployment_name="gpt4_small",
        openai_api_version="2023-12-01-preview",
        azure_endpoint=st.secrets["endpoint2"],
        openai_api_key=st.secrets["key2"],
        openai_api_type="azure",
        temperature=0.
    )


@st.cache_data
def get_feeds_data(url):
    # data = cp.load(urlopen(url))
    with open(url, "rb") as fp:
        data = pickle.load(fp)
    st.sidebar.success("Loaded data")
    return data

# feeds_link = "https://drive.google.com/uc?export=download&id=1-IPk1voyUM9VqnghwyVrM1dY6rFnn1S_"
# embed_link = "https://dl.dropboxusercontent.com/s/ob2betm29qrtb8v/astro_ph_ga_feeds_ada_embedding_18-Apr-2023.pkl?dl=0"
dateval = "27-Jun-2023"
feeds_link = "local_files/astro_ph_ga_feeds_upto_"+dateval+".pkl"
embed_link = "local_files/astro_ph_ga_feeds_ada_embedding_"+dateval+".pkl"
gal_feeds = get_feeds_data(feeds_link)
arxiv_ada_embeddings = get_feeds_data(embed_link)

@st.cache_data
def get_embedding_data(url):
    # data = cp.load(urlopen(url))
    with open(url, "rb") as fp:
        data = pickle.load(fp)
    st.sidebar.success("Fetched data from API!")
    return data

# url = "https://drive.google.com/uc?export=download&id=1133tynMwsfdR1wxbkFLhbES3FwDWTPjP"
url = "local_files/astro_ph_ga_embedding_"+dateval+".pkl"
e2d = get_embedding_data(url)
# e2d, _, _, _, _ = get_embedding_data(url)

ctr = -1
num_chunks = len(gal_feeds)
ctr = -1
num_chunks = len(gal_feeds)
all_text, all_titles, all_arxivid, all_links, all_authors, all_pubdates, all_old = [], [], [], [], [], [], []

for nc in range(num_chunks):

    for i in range(len(gal_feeds[nc].entries)):
        text = gal_feeds[nc].entries[i].summary
        text = text.replace('\n', ' ')
        text = text.replace('\\', '')
        all_text.append(text)
        all_titles.append(gal_feeds[nc].entries[i].title)
        all_arxivid.append(gal_feeds[nc].entries[i].id.split('/')[-1][0:-2])
        all_links.append(gal_feeds[nc].entries[i].links[1].href)
        all_authors.append(gal_feeds[nc].entries[i].authors)
        temp = gal_feeds[nc].entries[i].published
        datetime_object = datetime.datetime.strptime(temp[0:10]+' '+temp[11:-1], '%Y-%m-%d %H:%M:%S')
        all_pubdates.append(datetime_object)
        all_old.append((datetime.datetime.now() - datetime_object).days)

def make_author_plot(inputstr, print_summary = False):

    authr_list = inputstr.split(', ')
    author_flag = np.zeros((len(all_authors),))
    ctr = 0
    pts = []
    for i in range(len(all_authors)):
        for j in range(len(all_authors[i])):
            for k in range(len(authr_list)):
                authr = authr_list[k]
                if authr.lower() in all_authors[i][j]['name'].lower():
                    author_flag[i] = 1
                    ctr = ctr+1
                    printstr = str(ctr)+'. [age= %.1f yr, x: %.1f, y: %.1f]' %(all_old[i]/365,e2d[i,0], e2d[i,1])+' name: '+all_authors[i][j]['name']
                    pts.append(printstr)
                    pts.append('Paper title: ' + all_titles[i])
                else:
                    continue
    print(np.sum(author_flag))
    author_flag = author_flag.astype(bool)

    fig = plt.figure(figsize=(10.8,9.))
    plt.scatter(e2d[0:,0], e2d[0:,1],s=1,color='k',alpha=0.3)
    plt.scatter(e2d[0:,0][author_flag], e2d[0:,1][author_flag],
                s=100,c=np.array(all_old)[author_flag]/365,alpha=1.0, cmap='coolwarm')
    clbr = plt.colorbar(); clbr.set_label('lookback time [years]',fontsize=18)
    tempx = plt.xlim(); tempy = plt.ylim()
    plt.title('Author: '+authr,fontsize=18,fontweight='bold')
    st.pyplot(fig)

    if print_summary == True:
        st.markdown('---')
        for i in range(len(pts)):
            st.markdown(pts[i])

    return


st.title('Author search')
st.markdown('[Includes papers up to: `'+dateval+'`]')
st.markdown('Trace the location and trajectory of a researcher in the astro-ph.GA manifold.')
st.markdown('The current text matching is exact (not case sensitive), so look at the printed summaries below to refine your input string. If you have multiple aliases by which you publish, separate the inputs with a comma followed by a space like in the example below.')

query = st.text_input('Author name:',
value="Kartheik Iyer, Kartheik G. Iyer, K. G. Iyer")

make_author_plot(query, print_summary=True)