Spaces:
Runtime error
Runtime error
LysandreJik
commited on
Commit
•
fe8da28
1
Parent(s):
bd334dc
Cumulated only for pip
Browse files
app.py
CHANGED
@@ -6,7 +6,7 @@ from http.server import SimpleHTTPRequestHandler, ThreadingHTTPServer
|
|
6 |
from urllib.parse import parse_qs, urlparse
|
7 |
|
8 |
from huggingface_hub import list_datasets, set_access_token, HfFolder
|
9 |
-
from datasets import load_dataset, DatasetDict
|
10 |
import numpy as np
|
11 |
|
12 |
HF_TOKEN = os.environ['HF_TOKEN']
|
@@ -20,6 +20,30 @@ datasets = {
|
|
20 |
"pip": load_dataset("open-source-metrics/pip").sort('day')
|
21 |
}
|
22 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
23 |
# datasets = {
|
24 |
# k1: DatasetDict({
|
25 |
# k2: v2.select(range(0, len(v2), max(1, int(len(v2) / 1000)))) for k2, v2 in v1.items()
|
@@ -27,6 +51,18 @@ datasets = {
|
|
27 |
# }
|
28 |
|
29 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
30 |
def running_mean(x, N, total_length=-1):
|
31 |
cumsum = np.cumsum(np.insert(x, 0, 0))
|
32 |
to_pad = max(total_length - len(cumsum), 0)
|
@@ -43,7 +79,6 @@ class RequestHandler(SimpleHTTPRequestHandler):
|
|
43 |
|
44 |
if self.path.startswith("/initialize"):
|
45 |
dataset_keys = {k: set(v.keys()) for k, v in datasets.items()}
|
46 |
-
dataset_keys['issues'].remove('transformers')
|
47 |
dataset_with_most_splits = max([d for d in dataset_keys.values()], key=len)
|
48 |
warnings = []
|
49 |
|
@@ -68,18 +103,34 @@ class RequestHandler(SimpleHTTPRequestHandler):
|
|
68 |
library_names = query.get("input", None)[0]
|
69 |
library_names = library_names.split(',')
|
70 |
|
71 |
-
|
72 |
-
|
73 |
-
for
|
74 |
-
|
75 |
-
|
76 |
-
|
77 |
-
|
78 |
-
|
79 |
-
|
80 |
-
|
81 |
-
|
82 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
83 |
|
84 |
returned_values = collections.OrderedDict(sorted(returned_values.items()))
|
85 |
output = {l: [k[l] for k in returned_values.values()] for l in library_names}
|
@@ -105,23 +156,20 @@ class RequestHandler(SimpleHTTPRequestHandler):
|
|
105 |
for library_name in library_names:
|
106 |
dataset = dataset_dict[library_name]
|
107 |
|
108 |
-
n = 0
|
109 |
for i in dataset:
|
110 |
-
n += 1
|
111 |
if i['dates'] in returned_values:
|
112 |
-
returned_values[i['dates']][library_name] =
|
113 |
else:
|
114 |
-
returned_values[i['dates']] = {library_name:
|
115 |
-
|
116 |
-
for library_name in library_names:
|
117 |
-
for i in returned_values.keys():
|
118 |
-
if library_name not in returned_values[i]:
|
119 |
-
returned_values[i][library_name] = None
|
120 |
|
121 |
returned_values = collections.OrderedDict(sorted(returned_values.items()))
|
|
|
122 |
output = {l: [k[l] for k in returned_values.values()][::-1] for l in library_names}
|
123 |
output['day'] = list(returned_values.keys())[::-1]
|
124 |
|
|
|
|
|
|
|
125 |
self.send_response(200)
|
126 |
self.send_header("Content-Type", "application/json")
|
127 |
self.end_headers()
|
@@ -142,23 +190,20 @@ class RequestHandler(SimpleHTTPRequestHandler):
|
|
142 |
for library_name in library_names:
|
143 |
dataset = dataset_dict[library_name]
|
144 |
|
145 |
-
|
146 |
-
for k, i in enumerate(dataset):
|
147 |
-
n += 1
|
148 |
if i['dates'] in returned_values:
|
149 |
-
returned_values[i['dates']][library_name] =
|
150 |
else:
|
151 |
-
returned_values[i['dates']] = {library_name:
|
152 |
-
|
153 |
-
for library_name in library_names:
|
154 |
-
for i in returned_values.keys():
|
155 |
-
if library_name not in returned_values[i]:
|
156 |
-
returned_values[i][library_name] = None
|
157 |
|
158 |
returned_values = collections.OrderedDict(sorted(returned_values.items()))
|
|
|
159 |
output = {l: [k[l] for k in returned_values.values()][::-1] for l in library_names}
|
160 |
output['day'] = list(returned_values.keys())[::-1]
|
161 |
|
|
|
|
|
|
|
162 |
self.send_response(200)
|
163 |
self.send_header("Content-Type", "application/json")
|
164 |
self.end_headers()
|
|
|
6 |
from urllib.parse import parse_qs, urlparse
|
7 |
|
8 |
from huggingface_hub import list_datasets, set_access_token, HfFolder
|
9 |
+
from datasets import load_dataset, DatasetDict, Dataset
|
10 |
import numpy as np
|
11 |
|
12 |
HF_TOKEN = os.environ['HF_TOKEN']
|
|
|
20 |
"pip": load_dataset("open-source-metrics/pip").sort('day')
|
21 |
}
|
22 |
|
23 |
+
val = 0
|
24 |
+
|
25 |
+
|
26 |
+
def _range(e):
|
27 |
+
global val
|
28 |
+
e['range'] = val
|
29 |
+
val += 1
|
30 |
+
return e
|
31 |
+
|
32 |
+
|
33 |
+
stars = {}
|
34 |
+
for k, v in datasets['stars'].items():
|
35 |
+
stars[k] = v.map(_range)
|
36 |
+
val = 0
|
37 |
+
|
38 |
+
issues = {}
|
39 |
+
for k, v in datasets['issues'].items():
|
40 |
+
issues[k] = v.map(_range)
|
41 |
+
val = 0
|
42 |
+
|
43 |
+
datasets['stars'] = DatasetDict(**stars)
|
44 |
+
datasets['issues'] = DatasetDict(**issues)
|
45 |
+
|
46 |
+
|
47 |
# datasets = {
|
48 |
# k1: DatasetDict({
|
49 |
# k2: v2.select(range(0, len(v2), max(1, int(len(v2) / 1000)))) for k2, v2 in v1.items()
|
|
|
51 |
# }
|
52 |
|
53 |
|
54 |
+
def link_values(library_names, returned_values):
|
55 |
+
previous_values = {library_name: None for library_name in library_names}
|
56 |
+
for library_name in library_names:
|
57 |
+
for i in returned_values.keys():
|
58 |
+
if library_name not in returned_values[i]:
|
59 |
+
returned_values[i][library_name] = previous_values[library_name]
|
60 |
+
else:
|
61 |
+
previous_values[library_name] = returned_values[i][library_name]
|
62 |
+
|
63 |
+
return returned_values
|
64 |
+
|
65 |
+
|
66 |
def running_mean(x, N, total_length=-1):
|
67 |
cumsum = np.cumsum(np.insert(x, 0, 0))
|
68 |
to_pad = max(total_length - len(cumsum), 0)
|
|
|
79 |
|
80 |
if self.path.startswith("/initialize"):
|
81 |
dataset_keys = {k: set(v.keys()) for k, v in datasets.items()}
|
|
|
82 |
dataset_with_most_splits = max([d for d in dataset_keys.values()], key=len)
|
83 |
warnings = []
|
84 |
|
|
|
103 |
library_names = query.get("input", None)[0]
|
104 |
library_names = library_names.split(',')
|
105 |
|
106 |
+
if 'Cumulated' in library_names:
|
107 |
+
dataset_keys = {k: set(v.keys()) for k, v in datasets.items()}
|
108 |
+
dataset_with_most_splits = max([d for d in dataset_keys.values()], key=len)
|
109 |
+
library_names = list(dataset_with_most_splits)
|
110 |
+
|
111 |
+
returned_values = {}
|
112 |
+
for library_name in library_names:
|
113 |
+
for i in datasets['pip'][library_name]:
|
114 |
+
if i['day'] in returned_values:
|
115 |
+
returned_values[i['day']]['Cumulated'] += i['num_downloads']
|
116 |
+
else:
|
117 |
+
returned_values[i['day']] = {'Cumulated': i['num_downloads']}
|
118 |
+
|
119 |
+
library_names = ['Cumulated']
|
120 |
+
|
121 |
+
else:
|
122 |
+
returned_values = {}
|
123 |
+
for library_name in library_names:
|
124 |
+
for i in datasets['pip'][library_name]:
|
125 |
+
if i['day'] in returned_values:
|
126 |
+
returned_values[i['day']][library_name] = i['num_downloads']
|
127 |
+
else:
|
128 |
+
returned_values[i['day']] = {library_name: i['num_downloads']}
|
129 |
+
|
130 |
+
for library_name in library_names:
|
131 |
+
for i in returned_values.keys():
|
132 |
+
if library_name not in returned_values[i]:
|
133 |
+
returned_values[i][library_name] = None
|
134 |
|
135 |
returned_values = collections.OrderedDict(sorted(returned_values.items()))
|
136 |
output = {l: [k[l] for k in returned_values.values()] for l in library_names}
|
|
|
156 |
for library_name in library_names:
|
157 |
dataset = dataset_dict[library_name]
|
158 |
|
|
|
159 |
for i in dataset:
|
|
|
160 |
if i['dates'] in returned_values:
|
161 |
+
returned_values[i['dates']][library_name] = i['range']
|
162 |
else:
|
163 |
+
returned_values[i['dates']] = {library_name: i['range']}
|
|
|
|
|
|
|
|
|
|
|
164 |
|
165 |
returned_values = collections.OrderedDict(sorted(returned_values.items()))
|
166 |
+
returned_values = link_values(library_names, returned_values)
|
167 |
output = {l: [k[l] for k in returned_values.values()][::-1] for l in library_names}
|
168 |
output['day'] = list(returned_values.keys())[::-1]
|
169 |
|
170 |
+
# Trim down to a smaller number of points.
|
171 |
+
output = {k: [v for i, v in enumerate(value) if i % int(len(value) / 100) == 0] for k, value in output.items()}
|
172 |
+
|
173 |
self.send_response(200)
|
174 |
self.send_header("Content-Type", "application/json")
|
175 |
self.end_headers()
|
|
|
190 |
for library_name in library_names:
|
191 |
dataset = dataset_dict[library_name]
|
192 |
|
193 |
+
for i in dataset:
|
|
|
|
|
194 |
if i['dates'] in returned_values:
|
195 |
+
returned_values[i['dates']][library_name] = i['range']
|
196 |
else:
|
197 |
+
returned_values[i['dates']] = {library_name: i['range']}
|
|
|
|
|
|
|
|
|
|
|
198 |
|
199 |
returned_values = collections.OrderedDict(sorted(returned_values.items()))
|
200 |
+
returned_values = link_values(library_names, returned_values)
|
201 |
output = {l: [k[l] for k in returned_values.values()][::-1] for l in library_names}
|
202 |
output['day'] = list(returned_values.keys())[::-1]
|
203 |
|
204 |
+
# Trim down to a smaller number of points.
|
205 |
+
output = {k: [v for i, v in enumerate(value) if i % int(len(value) / 100) == 0] for k, value in output.items()}
|
206 |
+
|
207 |
self.send_response(200)
|
208 |
self.send_header("Content-Type", "application/json")
|
209 |
self.end_headers()
|
index.js
CHANGED
@@ -41,6 +41,7 @@ const createButton = (title, libraries, methods) => {
|
|
41 |
const initialize = async () => {
|
42 |
const inferResponse = await fetch(`initialize`);
|
43 |
const inferJson = await inferResponse.json();
|
|
|
44 |
// const graphsDiv = document.getElementsByClassName('graphs')[0];
|
45 |
const librarySelector = document.getElementById('library-selector');
|
46 |
const graphSelector = document.getElementById('graph-selector');
|
@@ -62,7 +63,11 @@ const initialize = async () => {
|
|
62 |
|
63 |
const checkBoxLabel = document.createElement('label');
|
64 |
const labelSpan = document.createElement('span')
|
65 |
-
|
|
|
|
|
|
|
|
|
66 |
checkBoxLabel.appendChild(checkBox)
|
67 |
checkBoxLabel.appendChild(labelSpan)
|
68 |
|
|
|
41 |
const initialize = async () => {
|
42 |
const inferResponse = await fetch(`initialize`);
|
43 |
const inferJson = await inferResponse.json();
|
44 |
+
inferJson.push('Cumulated')
|
45 |
// const graphsDiv = document.getElementsByClassName('graphs')[0];
|
46 |
const librarySelector = document.getElementById('library-selector');
|
47 |
const graphSelector = document.getElementById('graph-selector');
|
|
|
63 |
|
64 |
const checkBoxLabel = document.createElement('label');
|
65 |
const labelSpan = document.createElement('span')
|
66 |
+
|
67 |
+
if (element == 'Cumulated')
|
68 |
+
labelSpan.textContent = "Cumulated - Only works for pip installs, will crash otherwise."
|
69 |
+
else
|
70 |
+
labelSpan.textContent = element.charAt(0).toUpperCase() + element.slice(1)
|
71 |
checkBoxLabel.appendChild(checkBox)
|
72 |
checkBoxLabel.appendChild(labelSpan)
|
73 |
|