charbelmalo commited on
Commit
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1 Parent(s): ac2467c

gradio 5.5.0

Browse files
.pre-commit-config.yaml DELETED
@@ -1,53 +0,0 @@
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- # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
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- #
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- # Licensed under the Apache License, Version 2.0 (the "License");
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- # you may not use this file except in compliance with the License.
5
- # You may obtain a copy of the License at
6
- #
7
- # http://www.apache.org/licenses/LICENSE-2.0
8
- #
9
- # Unless required by applicable law or agreed to in writing, software
10
- # distributed under the License is distributed on an "AS IS" BASIS,
11
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12
- # See the License for the specific language governing permissions and
13
- # limitations under the License.
14
-
15
- default_language_version:
16
- python: python3
17
-
18
- ci:
19
- autofix_prs: true
20
- autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions'
21
- autoupdate_schedule: quarterly
22
-
23
- repos:
24
- - repo: https://github.com/pre-commit/pre-commit-hooks
25
- rev: v4.3.0
26
- hooks:
27
- - id: check-yaml
28
- - id: check-case-conflict
29
- - id: detect-private-key
30
- - id: check-added-large-files
31
- args: ['--maxkb=1000']
32
- - id: requirements-txt-fixer
33
- - id: end-of-file-fixer
34
- - id: trailing-whitespace
35
-
36
- - repo: https://github.com/PyCQA/isort
37
- rev: 5.12.0
38
- hooks:
39
- - id: isort
40
- name: Format imports
41
-
42
- - repo: https://github.com/psf/black
43
- rev: 22.12.0
44
- hooks:
45
- - id: black
46
- name: Format code
47
- additional_dependencies: ['click==8.0.2']
48
-
49
- - repo: https://github.com/charliermarsh/ruff-pre-commit
50
- # Ruff version.
51
- rev: 'v0.0.267'
52
- hooks:
53
- - id: ruff
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
app.py CHANGED
@@ -55,7 +55,7 @@ def get_shortcuts_dataframe(sort_by='Recently Added', search_query='', filter_ta
55
  elif sort_by == 'Recently Added':
56
  datafra = datafra.sort_values('date_added', ascending=False)
57
  elif sort_by == 'Favorites':
58
- datafra = datafra.sort_values('favorited')
59
  # Reset index
60
  datafra = datafra.reset_index(drop=True)
61
  return datafra
@@ -65,31 +65,117 @@ def generate_cards_html(datafra):
65
  return "<p>No shortcuts available.</p>"
66
  cards_html = '<div style="display: flex; flex-wrap: wrap;">'
67
  for idx, shortcut in datafra.iterrows():
68
- # Create HTML for each card
69
- style = f"background: linear-gradient(135deg, {shortcut['color_from']}, {shortcut['color_to']}); padding: 20px; border-radius: 10px; position: relative; color: white; width: 300px; margin: 10px;"
70
- labels = []
71
  if shortcut['pinned']:
72
- labels.append("πŸ“Œ")
 
 
 
 
 
 
73
  if shortcut['favorited']:
74
- labels.append("❀️")
 
 
 
 
 
 
 
 
 
 
 
 
 
75
  labels_html = ""
76
- if labels:
77
- labels_html = f"<div style='position: absolute; top: 10px; right: 10px; font-size: 24px;'>{' '.join(labels)}</div>"
 
 
 
 
 
78
  card_html = f"""
79
- <div style="{style}">
80
  {labels_html}
81
  <div style='font-size: 40px; text-align: center;'>{shortcut['emojis']}</div>
82
  <h3 style='text-align: center;'>{shortcut['name']}</h3>
83
  <p style='text-align: center;'>{shortcut['short_description']}</p>
84
  <div style='text-align: center;'>
85
- <button onclick="togglePin({idx})">πŸ“Œ Pin/Unpin</button>
86
- <button onclick="toggleFavorite({idx})">❀️ Favorite/Unfavorite</button>
87
  <button onclick="window.open('{shortcut['link']}', '_blank')">πŸ”— Open</button>
88
  </div>
 
 
 
89
  </div>
90
  """
91
  cards_html += card_html
92
  cards_html += '</div>'
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
93
  return cards_html
94
 
95
  def update_display(sort_by='Recently Added', search_query='', filter_tags=[]):
@@ -115,108 +201,70 @@ def toggle_favorite(index):
115
  load_shortcuts()
116
 
117
  # Build the Gradio App
118
- with gr.Blocks(theme='charbelgrower/Crystal') as demo:
119
-
120
- # Actions for filters
121
- gr.Label("Website Shortcuts")
122
  with gr.Row():
123
  search_bar = gr.Textbox(label="Search")
124
  sort_options = gr.Dropdown(choices=['Recently Added', 'Alphabetical', 'Favorites'], label="Sort By", value='Recently Added')
 
125
  # Collect all unique tags
126
  def get_all_tags():
127
  return list(set(tag for shortcut in shortcuts_list for tag in shortcut['tags']))
 
128
  filter_tags = gr.CheckboxGroup(choices=get_all_tags(), label="Filter Tags")
129
-
130
 
131
- grid_output = gr.HTML(elem_id="grid_output")
132
 
133
  gr.Markdown("## Add a New Website Shortcut")
134
  with gr.Row():
135
  name = gr.Textbox(label="Name")
136
  link = gr.Textbox(label="Link")
137
  with gr.Row():
138
- with gr.Column():
139
- tags = gr.Textbox(label="Tags (separate levels with '/')")
140
- with gr.Column():
141
- emojis = gr.Textbox(label="Emojis")
142
- with gr.Column():
143
- color_from = gr.ColorPicker(label="Gradient Color From")
144
- with gr.Column():
145
- color_to = gr.ColorPicker(label="Gradient Color To")
146
  short_description = gr.Textbox(label="Short Description")
147
  add_button = gr.Button("Add Shortcut")
148
- # Output component for the updated grid
149
-
150
  # Update display when filters change
151
- def refresh_display(sort_by, search_query, filter_tags):
152
  return update_display(sort_by, search_query, filter_tags)
153
-
154
- search_bar.change(fn=refresh_display, inputs=[sort_options, search_bar, filter_tags], outputs=grid_output)
155
- sort_options.change(fn=refresh_display, inputs=[sort_options, search_bar, filter_tags], outputs=grid_output)
156
- filter_tags.change(fn=refresh_display, inputs=[sort_options, search_bar, filter_tags], outputs=grid_output)
157
-
158
  # Add shortcut action
159
  add_button.click(
160
- add_shortcut,
161
  inputs=[name, tags, link, emojis, color_from, color_to, short_description],
162
  outputs=grid_output
163
  )
164
-
165
- # Set initial data
166
- grid_output.value = update_display()
167
-
168
- # JavaScript functions for interactivity
169
- custom_js = """
170
- <script>
171
- function togglePin(idx) {
172
- fetch('/toggle_pin', {
173
- method: 'POST',
174
- headers: {
175
- 'Content-Type': 'application/json'
176
- },
177
- body: JSON.stringify({'index': idx})
178
- }).then(response => response.json()).then(data => {
179
- const gridOutput = document.querySelector('#grid_output');
180
- gridOutput.innerHTML = data['grid_html'];
181
- });
182
- }
183
- function toggleFavorite(idx) {
184
- fetch('/toggle_favorite', {
185
- method: 'POST',
186
- headers: {
187
- 'Content-Type': 'application/json'
188
- },
189
- body: JSON.stringify({'index': idx})
190
- }).then(response => response.json()).then(data => {
191
- const gridOutput = document.querySelector('#grid_output');
192
- gridOutput.innerHTML = data['grid_html'];
193
- });
194
- }
195
- </script>
196
- """
197
- # Inject custom JavaScript
198
- demo.load(fn=lambda: None, inputs=None, outputs=None, _js=custom_js)
199
-
200
- # Expose endpoints for toggle functions
201
- api = FastAPI()
202
-
203
- @api.post("/toggle_pin")
204
- async def toggle_pin_route(request: Request):
205
- data = await request.json()
206
- index = data['index']
207
- grid_html = toggle_pin(index)
208
- return {'grid_html': grid_html}
209
-
210
- @api.post("/toggle_favorite")
211
- async def toggle_favorite_route(request: Request):
212
- data = await request.json()
213
- index = data['index']
214
- grid_html = toggle_favorite(index)
215
- return {'grid_html': grid_html}
216
-
217
- # Mount the Gradio app onto the FastAPI app
218
- api = gr.mount_gradio_app(app=api, blocks=demo, path="/")
219
-
220
  load_shortcuts()
221
- # Run the app
222
- demo.launch()
 
55
  elif sort_by == 'Recently Added':
56
  datafra = datafra.sort_values('date_added', ascending=False)
57
  elif sort_by == 'Favorites':
58
+ datafra = datafra.sort_values('favorited', ascending=False)
59
  # Reset index
60
  datafra = datafra.reset_index(drop=True)
61
  return datafra
 
65
  return "<p>No shortcuts available.</p>"
66
  cards_html = '<div style="display: flex; flex-wrap: wrap;">'
67
  for idx, shortcut in datafra.iterrows():
68
+ # Determine pin icon based on state
 
 
69
  if shortcut['pinned']:
70
+ pin_icon = "πŸ“Œ"
71
+ pin_style = "opacity: 1;"
72
+ else:
73
+ pin_icon = "πŸ“"
74
+ pin_style = "opacity: 0.3; text-decoration: line-through;"
75
+
76
+ # Determine favorite icon based on state
77
  if shortcut['favorited']:
78
+ favorite_icon = "❀️"
79
+ else:
80
+ favorite_icon = "🀍"
81
+
82
+ style = f"""
83
+ background: linear-gradient(135deg, {shortcut['color_from']}, {shortcut['color_to']});
84
+ padding: 20px;
85
+ border-radius: 10px;
86
+ position: relative;
87
+ color: white;
88
+ width: 300px;
89
+ margin: 10px;
90
+ cursor: pointer;
91
+ """
92
  labels_html = ""
93
+ if shortcut['pinned'] or shortcut['favorited']:
94
+ labels_html = f"""
95
+ <div style='position: absolute; top: 10px; right: 10px; font-size: 24px;'>
96
+ {pin_icon if shortcut['pinned'] else ''}
97
+ {favorite_icon if shortcut['favorited'] else ''}
98
+ </div>
99
+ """
100
  card_html = f"""
101
+ <div style="{style}" onmouseover="handleHover(event, {idx})" onmouseout="handleHoverOut(event, {idx})">
102
  {labels_html}
103
  <div style='font-size: 40px; text-align: center;'>{shortcut['emojis']}</div>
104
  <h3 style='text-align: center;'>{shortcut['name']}</h3>
105
  <p style='text-align: center;'>{shortcut['short_description']}</p>
106
  <div style='text-align: center;'>
107
+ <button style="background: none; border: none; cursor: pointer; {pin_style}" onclick="togglePin({idx})">{pin_icon}</button>
108
+ <button style="background: none; border: none; cursor: pointer;" onclick="toggleFavorite({idx})">{favorite_icon}</button>
109
  <button onclick="window.open('{shortcut['link']}', '_blank')">πŸ”— Open</button>
110
  </div>
111
+ <div id="delete-{idx}" style="display: none; position: absolute; top: 10px; left: 10px; cursor: pointer;" onclick="deleteShortcut({idx})">
112
+ πŸ—‘οΈ
113
+ </div>
114
  </div>
115
  """
116
  cards_html += card_html
117
  cards_html += '</div>'
118
+
119
+ # Add JavaScript for handling hover and command/control key
120
+ cards_html += """
121
+ <script>
122
+ let isCmdOrCtrl = false;
123
+
124
+ window.addEventListener('keydown', function(e) {
125
+ if (e.key === 'Meta' || e.key === 'Control') {
126
+ isCmdOrCtrl = true;
127
+ showDeleteIcons();
128
+ }
129
+ });
130
+
131
+ window.addEventListener('keyup', function(e) {
132
+ if (e.key === 'Meta' || e.key === 'Control') {
133
+ isCmdOrCtrl = false;
134
+ hideDeleteIcons();
135
+ }
136
+ });
137
+
138
+ function handleHover(event, idx) {
139
+ if (isCmdOrCtrl) {
140
+ document.getElementById(`delete-${idx}`).style.display = 'block';
141
+ }
142
+ }
143
+
144
+ function handleHoverOut(event, idx) {
145
+ document.getElementById(`delete-${idx}`).style.display = 'none';
146
+ }
147
+
148
+ function showDeleteIcons() {
149
+ const deleteIcons = document.querySelectorAll('[id^="delete-"]');
150
+ deleteIcons.forEach(icon => {
151
+ icon.style.display = 'block';
152
+ });
153
+ }
154
+
155
+ function hideDeleteIcons() {
156
+ const deleteIcons = document.querySelectorAll('[id^="delete-"]');
157
+ deleteIcons.forEach(icon => {
158
+ icon.style.display = 'none';
159
+ });
160
+ }
161
+
162
+ function deleteShortcut(idx) {
163
+ // Implement the delete functionality, e.g., call an API endpoint
164
+ fetch('/delete_shortcut', {
165
+ method: 'POST',
166
+ headers: {
167
+ 'Content-Type': 'application/json'
168
+ },
169
+ body: JSON.stringify({ index: idx })
170
+ })
171
+ .then(response => response.json())
172
+ .then(data => {
173
+ // Update the grid display
174
+ document.getElementById('grid_output').innerHTML = data.grid_html;
175
+ });
176
+ }
177
+ </script>
178
+ """
179
  return cards_html
180
 
181
  def update_display(sort_by='Recently Added', search_query='', filter_tags=[]):
 
201
  load_shortcuts()
202
 
203
  # Build the Gradio App
204
+ with gr.Blocks(theme="charbelgrower/Crystal") as demo:
205
+
206
+ gr.Markdown("## Website Shortcuts")
 
207
  with gr.Row():
208
  search_bar = gr.Textbox(label="Search")
209
  sort_options = gr.Dropdown(choices=['Recently Added', 'Alphabetical', 'Favorites'], label="Sort By", value='Recently Added')
210
+
211
  # Collect all unique tags
212
  def get_all_tags():
213
  return list(set(tag for shortcut in shortcuts_list for tag in shortcut['tags']))
214
+
215
  filter_tags = gr.CheckboxGroup(choices=get_all_tags(), label="Filter Tags")
 
216
 
217
+ grid_output = gr.HTML(value=update_display(), elem_id="grid_output")
218
 
219
  gr.Markdown("## Add a New Website Shortcut")
220
  with gr.Row():
221
  name = gr.Textbox(label="Name")
222
  link = gr.Textbox(label="Link")
223
  with gr.Row():
224
+ tags = gr.Textbox(label="Tags (separate levels with '/')")
225
+ emojis = gr.Textbox(label="Emojis")
226
+ color_from = gr.ColorPicker(label="Gradient Color From")
227
+ color_to = gr.ColorPicker(label="Gradient Color To")
 
 
 
 
228
  short_description = gr.Textbox(label="Short Description")
229
  add_button = gr.Button("Add Shortcut")
230
+
 
231
  # Update display when filters change
232
+ def refresh_display(search_query, sort_by, filter_tags):
233
  return update_display(sort_by, search_query, filter_tags)
234
+
235
+ search_bar.change(fn=refresh_display, inputs=[search_bar, sort_options, filter_tags], outputs=grid_output)
236
+ sort_options.change(fn=refresh_display, inputs=[search_bar, sort_options, filter_tags], outputs=grid_output)
237
+ filter_tags.change(fn=refresh_display, inputs=[search_bar, sort_options, filter_tags], outputs=grid_output)
238
+
239
  # Add shortcut action
240
  add_button.click(
241
+ fn=add_shortcut,
242
  inputs=[name, tags, link, emojis, color_from, color_to, short_description],
243
  outputs=grid_output
244
  )
245
+
246
+
247
+ # Expose endpoints for toggle functions
248
+ api = FastAPI()
249
+
250
+ @api.post("/toggle_pin")
251
+ async def toggle_pin_route(request: Request):
252
+ data = await request.json()
253
+ index = data['index']
254
+ grid_html = toggle_pin(index)
255
+ return {'grid_html': grid_html}
256
+
257
+ @api.post("/toggle_favorite")
258
+ async def toggle_favorite_route(request: Request):
259
+ data = await request.json()
260
+ index = data['index']
261
+ grid_html = toggle_favorite(index)
262
+ return {'grid_html': grid_html}
263
+
264
+ # Mount the Gradio app onto the FastAPI app
265
+ app = gr.mount_gradio_app(api, demo, path="/")
266
+
267
+ if __name__ == "__main__":
268
+ import uvicorn
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
269
  load_shortcuts()
270
+ uvicorn.run(app, host="0.0.0.0", port=7860)
 
gradiodoc.json ADDED
The diff for this file is too large to render. See raw diff
 
shortcuts.json CHANGED
@@ -1 +1 @@
1
- [{"name": "Charbel", "tags": ["Grower", "Main"], "link": "http://example.com", "emojis": "\u26a1", "color_from": "#931010", "color_to": "#33417a", "short_description": "asdasfd", "pinned": false, "favorited": false, "date_added": "2024-11-12T23:04:40.313478"}, {"name": "Charbel", "tags": ["Grower", "Main"], "link": "http://example.com", "emojis": "\u26a1", "color_from": "#931010", "color_to": "#33417a", "short_description": "asdasfd", "pinned": false, "favorited": false, "date_added": "2024-11-12T23:04:42.705989"}, {"name": "", "tags": [], "link": "", "emojis": "", "color_from": "#000000", "color_to": "#000000", "short_description": "", "pinned": false, "favorited": false, "date_added": "2024-11-12T23:05:43.625612"}]
 
1
+ [{"name": "Charbel", "tags": ["Grower", "Main"], "link": "http://example.com", "emojis": "\u26a1", "color_from": "#931010", "color_to": "#33417a", "short_description": "asdasfd", "pinned": false, "favorited": false, "date_added": "2024-11-12T23:04:40.313478"}, {"name": "Charbel", "tags": ["Grower", "Main"], "link": "http://example.com", "emojis": "\u26a1", "color_from": "#931010", "color_to": "#33417a", "short_description": "asdasfd", "pinned": false, "favorited": true, "date_added": "2024-11-12T23:04:42.705989"}, {"name": "", "tags": [], "link": "", "emojis": "", "color_from": "#000000", "color_to": "#000000", "short_description": "", "pinned": false, "favorited": false, "date_added": "2024-11-12T23:05:43.625612"}]
src/.DS_Store DELETED
Binary file (6.15 kB)
 
src/about.py DELETED
@@ -1,72 +0,0 @@
1
- from dataclasses import dataclass
2
- from enum import Enum
3
-
4
- @dataclass
5
- class Task:
6
- benchmark: str
7
- metric: str
8
- col_name: str
9
-
10
-
11
- # Select your tasks here
12
- # ---------------------------------------------------
13
- class Tasks(Enum):
14
- # task_key in the json file, metric_key in the json file, name to display in the leaderboard
15
- task0 = Task("anli_r1", "acc", "ANLI")
16
- task1 = Task("logiqa", "acc_norm", "LogiQA")
17
-
18
- NUM_FEWSHOT = 0 # Change with your few shot
19
- # ---------------------------------------------------
20
-
21
-
22
-
23
- # Your leaderboard name
24
- TITLE = """<h1 align="center" id="space-title">Demo leaderboard</h1>"""
25
-
26
- # What does your leaderboard evaluate?
27
- INTRODUCTION_TEXT = """
28
- Intro text
29
- """
30
-
31
- # Which evaluations are you running? how can people reproduce what you have?
32
- LLM_BENCHMARKS_TEXT = f"""
33
- ## How it works
34
-
35
- ## Reproducibility
36
- To reproduce our results, here is the commands you can run:
37
-
38
- """
39
-
40
- EVALUATION_QUEUE_TEXT = """
41
- ## Some good practices before submitting a model
42
-
43
- ### 1) Make sure you can load your model and tokenizer using AutoClasses:
44
- ```python
45
- from transformers import AutoConfig, AutoModel, AutoTokenizer
46
- config = AutoConfig.from_pretrained("your model name", revision=revision)
47
- model = AutoModel.from_pretrained("your model name", revision=revision)
48
- tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision)
49
- ```
50
- If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been improperly uploaded.
51
-
52
- Note: make sure your model is public!
53
- Note: if your model needs `use_remote_code=True`, we do not support this option yet but we are working on adding it, stay posted!
54
-
55
- ### 2) Convert your model weights to [safetensors](https://huggingface.co/docs/safetensors/index)
56
- It's a new format for storing weights which is safer and faster to load and use. It will also allow us to add the number of parameters of your model to the `Extended Viewer`!
57
-
58
- ### 3) Make sure your model has an open license!
59
- This is a leaderboard for Open LLMs, and we'd love for as many people as possible to know they can use your model πŸ€—
60
-
61
- ### 4) Fill up your model card
62
- When we add extra information about models to the leaderboard, it will be automatically taken from the model card
63
-
64
- ## In case of model failure
65
- If your model is displayed in the `FAILED` category, its execution stopped.
66
- Make sure you have followed the above steps first.
67
- If everything is done, check you can launch the EleutherAIHarness on your model locally, using the above command without modifications (you can add `--limit` to limit the number of examples per task).
68
- """
69
-
70
- CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
71
- CITATION_BUTTON_TEXT = r"""
72
- """
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
src/display/css_html_js.py DELETED
@@ -1,105 +0,0 @@
1
- custom_css = """
2
-
3
- .markdown-text {
4
- font-size: 16px !important;
5
- }
6
-
7
- #models-to-add-text {
8
- font-size: 18px !important;
9
- }
10
-
11
- #citation-button span {
12
- font-size: 16px !important;
13
- }
14
-
15
- #citation-button textarea {
16
- font-size: 16px !important;
17
- }
18
-
19
- #citation-button > label > button {
20
- margin: 6px;
21
- transform: scale(1.3);
22
- }
23
-
24
- #leaderboard-table {
25
- margin-top: 15px
26
- }
27
-
28
- #leaderboard-table-lite {
29
- margin-top: 15px
30
- }
31
-
32
- #search-bar-table-box > div:first-child {
33
- background: none;
34
- border: none;
35
- }
36
-
37
- #search-bar {
38
- padding: 0px;
39
- }
40
-
41
- /* Limit the width of the first AutoEvalColumn so that names don't expand too much */
42
- #leaderboard-table td:nth-child(2),
43
- #leaderboard-table th:nth-child(2) {
44
- max-width: 400px;
45
- overflow: auto;
46
- white-space: nowrap;
47
- }
48
-
49
- .tab-buttons button {
50
- font-size: 20px;
51
- }
52
-
53
- #scale-logo {
54
- border-style: none !important;
55
- box-shadow: none;
56
- display: block;
57
- margin-left: auto;
58
- margin-right: auto;
59
- max-width: 600px;
60
- }
61
-
62
- #scale-logo .download {
63
- display: none;
64
- }
65
- #filter_type{
66
- border: 0;
67
- padding-left: 0;
68
- padding-top: 0;
69
- }
70
- #filter_type label {
71
- display: flex;
72
- }
73
- #filter_type label > span{
74
- margin-top: var(--spacing-lg);
75
- margin-right: 0.5em;
76
- }
77
- #filter_type label > .wrap{
78
- width: 103px;
79
- }
80
- #filter_type label > .wrap .wrap-inner{
81
- padding: 2px;
82
- }
83
- #filter_type label > .wrap .wrap-inner input{
84
- width: 1px
85
- }
86
- #filter-columns-type{
87
- border:0;
88
- padding:0.5;
89
- }
90
- #filter-columns-size{
91
- border:0;
92
- padding:0.5;
93
- }
94
- #box-filter > .form{
95
- border: 0
96
- }
97
- """
98
-
99
- get_window_url_params = """
100
- function(url_params) {
101
- const params = new URLSearchParams(window.location.search);
102
- url_params = Object.fromEntries(params);
103
- return url_params;
104
- }
105
- """
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
src/display/formatting.py DELETED
@@ -1,27 +0,0 @@
1
- def model_hyperlink(link, model_name):
2
- return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
3
-
4
-
5
- def make_clickable_model(model_name):
6
- link = f"https://huggingface.co/{model_name}"
7
- return model_hyperlink(link, model_name)
8
-
9
-
10
- def styled_error(error):
11
- return f"<p style='color: red; font-size: 20px; text-align: center;'>{error}</p>"
12
-
13
-
14
- def styled_warning(warn):
15
- return f"<p style='color: orange; font-size: 20px; text-align: center;'>{warn}</p>"
16
-
17
-
18
- def styled_message(message):
19
- return f"<p style='color: green; font-size: 20px; text-align: center;'>{message}</p>"
20
-
21
-
22
- def has_no_nan_values(df, columns):
23
- return df[columns].notna().all(axis=1)
24
-
25
-
26
- def has_nan_values(df, columns):
27
- return df[columns].isna().any(axis=1)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
src/display/utils.py DELETED
@@ -1,110 +0,0 @@
1
- from dataclasses import dataclass, make_dataclass
2
- from enum import Enum
3
-
4
- import pandas as pd
5
-
6
- from src.about import Tasks
7
-
8
- def fields(raw_class):
9
- return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
10
-
11
-
12
- # These classes are for user facing column names,
13
- # to avoid having to change them all around the code
14
- # when a modif is needed
15
- @dataclass
16
- class ColumnContent:
17
- name: str
18
- type: str
19
- displayed_by_default: bool
20
- hidden: bool = False
21
- never_hidden: bool = False
22
-
23
- ## Leaderboard columns
24
- auto_eval_column_dict = []
25
- # Init
26
- auto_eval_column_dict.append(["model_type_symbol", ColumnContent, ColumnContent("T", "str", True, never_hidden=True)])
27
- auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
28
- #Scores
29
- auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Average ⬆️", "number", True)])
30
- for task in Tasks:
31
- auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
32
- # Model information
33
- auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False)])
34
- auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)])
35
- auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
36
- auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", False)])
37
- auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)])
38
- auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
39
- auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❀️", "number", False)])
40
- auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
41
- auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])
42
-
43
- # We use make dataclass to dynamically fill the scores from Tasks
44
- AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)
45
-
46
- ## For the queue columns in the submission tab
47
- @dataclass(frozen=True)
48
- class EvalQueueColumn: # Queue column
49
- model = ColumnContent("model", "markdown", True)
50
- revision = ColumnContent("revision", "str", True)
51
- private = ColumnContent("private", "bool", True)
52
- precision = ColumnContent("precision", "str", True)
53
- weight_type = ColumnContent("weight_type", "str", "Original")
54
- status = ColumnContent("status", "str", True)
55
-
56
- ## All the model information that we might need
57
- @dataclass
58
- class ModelDetails:
59
- name: str
60
- display_name: str = ""
61
- symbol: str = "" # emoji
62
-
63
-
64
- class ModelType(Enum):
65
- PT = ModelDetails(name="pretrained", symbol="🟒")
66
- FT = ModelDetails(name="fine-tuned", symbol="πŸ”Ά")
67
- IFT = ModelDetails(name="instruction-tuned", symbol="β­•")
68
- RL = ModelDetails(name="RL-tuned", symbol="🟦")
69
- Unknown = ModelDetails(name="", symbol="?")
70
-
71
- def to_str(self, separator=" "):
72
- return f"{self.value.symbol}{separator}{self.value.name}"
73
-
74
- @staticmethod
75
- def from_str(type):
76
- if "fine-tuned" in type or "πŸ”Ά" in type:
77
- return ModelType.FT
78
- if "pretrained" in type or "🟒" in type:
79
- return ModelType.PT
80
- if "RL-tuned" in type or "🟦" in type:
81
- return ModelType.RL
82
- if "instruction-tuned" in type or "β­•" in type:
83
- return ModelType.IFT
84
- return ModelType.Unknown
85
-
86
- class WeightType(Enum):
87
- Adapter = ModelDetails("Adapter")
88
- Original = ModelDetails("Original")
89
- Delta = ModelDetails("Delta")
90
-
91
- class Precision(Enum):
92
- float16 = ModelDetails("float16")
93
- bfloat16 = ModelDetails("bfloat16")
94
- Unknown = ModelDetails("?")
95
-
96
- def from_str(precision):
97
- if precision in ["torch.float16", "float16"]:
98
- return Precision.float16
99
- if precision in ["torch.bfloat16", "bfloat16"]:
100
- return Precision.bfloat16
101
- return Precision.Unknown
102
-
103
- # Column selection
104
- COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
105
-
106
- EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
107
- EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]
108
-
109
- BENCHMARK_COLS = [t.value.col_name for t in Tasks]
110
-
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
src/envs.py DELETED
@@ -1,25 +0,0 @@
1
- import os
2
-
3
- from huggingface_hub import HfApi
4
-
5
- # Info to change for your repository
6
- # ----------------------------------
7
- TOKEN = os.environ.get("HF_TOKEN") # A read/write token for your org
8
-
9
- OWNER = "demo-leaderboard-backend" # Change to your org - don't forget to create a results and request dataset, with the correct format!
10
- # ----------------------------------
11
-
12
- REPO_ID = f"{OWNER}/leaderboard"
13
- QUEUE_REPO = f"{OWNER}/requests"
14
- RESULTS_REPO = f"{OWNER}/results"
15
-
16
- # If you setup a cache later, just change HF_HOME
17
- CACHE_PATH=os.getenv("HF_HOME", ".")
18
-
19
- # Local caches
20
- EVAL_REQUESTS_PATH = os.path.join(CACHE_PATH, "eval-queue")
21
- EVAL_RESULTS_PATH = os.path.join(CACHE_PATH, "eval-results")
22
- EVAL_REQUESTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-queue-bk")
23
- EVAL_RESULTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-results-bk")
24
-
25
- API = HfApi(token=TOKEN)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
src/leaderboard/read_evals.py DELETED
@@ -1,196 +0,0 @@
1
- import glob
2
- import json
3
- import math
4
- import os
5
- from dataclasses import dataclass
6
-
7
- import dateutil
8
- import numpy as np
9
-
10
- from src.display.formatting import make_clickable_model
11
- from src.display.utils import AutoEvalColumn, ModelType, Tasks, Precision, WeightType
12
- from src.submission.check_validity import is_model_on_hub
13
-
14
-
15
- @dataclass
16
- class EvalResult:
17
- """Represents one full evaluation. Built from a combination of the result and request file for a given run.
18
- """
19
- eval_name: str # org_model_precision (uid)
20
- full_model: str # org/model (path on hub)
21
- org: str
22
- model: str
23
- revision: str # commit hash, "" if main
24
- results: dict
25
- precision: Precision = Precision.Unknown
26
- model_type: ModelType = ModelType.Unknown # Pretrained, fine tuned, ...
27
- weight_type: WeightType = WeightType.Original # Original or Adapter
28
- architecture: str = "Unknown"
29
- license: str = "?"
30
- likes: int = 0
31
- num_params: int = 0
32
- date: str = "" # submission date of request file
33
- still_on_hub: bool = False
34
-
35
- @classmethod
36
- def init_from_json_file(self, json_filepath):
37
- """Inits the result from the specific model result file"""
38
- with open(json_filepath) as fp:
39
- data = json.load(fp)
40
-
41
- config = data.get("config")
42
-
43
- # Precision
44
- precision = Precision.from_str(config.get("model_dtype"))
45
-
46
- # Get model and org
47
- org_and_model = config.get("model_name", config.get("model_args", None))
48
- org_and_model = org_and_model.split("/", 1)
49
-
50
- if len(org_and_model) == 1:
51
- org = None
52
- model = org_and_model[0]
53
- result_key = f"{model}_{precision.value.name}"
54
- else:
55
- org = org_and_model[0]
56
- model = org_and_model[1]
57
- result_key = f"{org}_{model}_{precision.value.name}"
58
- full_model = "/".join(org_and_model)
59
-
60
- still_on_hub, _, model_config = is_model_on_hub(
61
- full_model, config.get("model_sha", "main"), trust_remote_code=True, test_tokenizer=False
62
- )
63
- architecture = "?"
64
- if model_config is not None:
65
- architectures = getattr(model_config, "architectures", None)
66
- if architectures:
67
- architecture = ";".join(architectures)
68
-
69
- # Extract results available in this file (some results are split in several files)
70
- results = {}
71
- for task in Tasks:
72
- task = task.value
73
-
74
- # We average all scores of a given metric (not all metrics are present in all files)
75
- accs = np.array([v.get(task.metric, None) for k, v in data["results"].items() if task.benchmark == k])
76
- if accs.size == 0 or any([acc is None for acc in accs]):
77
- continue
78
-
79
- mean_acc = np.mean(accs) * 100.0
80
- results[task.benchmark] = mean_acc
81
-
82
- return self(
83
- eval_name=result_key,
84
- full_model=full_model,
85
- org=org,
86
- model=model,
87
- results=results,
88
- precision=precision,
89
- revision= config.get("model_sha", ""),
90
- still_on_hub=still_on_hub,
91
- architecture=architecture
92
- )
93
-
94
- def update_with_request_file(self, requests_path):
95
- """Finds the relevant request file for the current model and updates info with it"""
96
- request_file = get_request_file_for_model(requests_path, self.full_model, self.precision.value.name)
97
-
98
- try:
99
- with open(request_file, "r") as f:
100
- request = json.load(f)
101
- self.model_type = ModelType.from_str(request.get("model_type", ""))
102
- self.weight_type = WeightType[request.get("weight_type", "Original")]
103
- self.license = request.get("license", "?")
104
- self.likes = request.get("likes", 0)
105
- self.num_params = request.get("params", 0)
106
- self.date = request.get("submitted_time", "")
107
- except Exception:
108
- print(f"Could not find request file for {self.org}/{self.model} with precision {self.precision.value.name}")
109
-
110
- def to_dict(self):
111
- """Converts the Eval Result to a dict compatible with our dataframe display"""
112
- average = sum([v for v in self.results.values() if v is not None]) / len(Tasks)
113
- data_dict = {
114
- "eval_name": self.eval_name, # not a column, just a save name,
115
- AutoEvalColumn.precision.name: self.precision.value.name,
116
- AutoEvalColumn.model_type.name: self.model_type.value.name,
117
- AutoEvalColumn.model_type_symbol.name: self.model_type.value.symbol,
118
- AutoEvalColumn.weight_type.name: self.weight_type.value.name,
119
- AutoEvalColumn.architecture.name: self.architecture,
120
- AutoEvalColumn.model.name: make_clickable_model(self.full_model),
121
- AutoEvalColumn.revision.name: self.revision,
122
- AutoEvalColumn.average.name: average,
123
- AutoEvalColumn.license.name: self.license,
124
- AutoEvalColumn.likes.name: self.likes,
125
- AutoEvalColumn.params.name: self.num_params,
126
- AutoEvalColumn.still_on_hub.name: self.still_on_hub,
127
- }
128
-
129
- for task in Tasks:
130
- data_dict[task.value.col_name] = self.results[task.value.benchmark]
131
-
132
- return data_dict
133
-
134
-
135
- def get_request_file_for_model(requests_path, model_name, precision):
136
- """Selects the correct request file for a given model. Only keeps runs tagged as FINISHED"""
137
- request_files = os.path.join(
138
- requests_path,
139
- f"{model_name}_eval_request_*.json",
140
- )
141
- request_files = glob.glob(request_files)
142
-
143
- # Select correct request file (precision)
144
- request_file = ""
145
- request_files = sorted(request_files, reverse=True)
146
- for tmp_request_file in request_files:
147
- with open(tmp_request_file, "r") as f:
148
- req_content = json.load(f)
149
- if (
150
- req_content["status"] in ["FINISHED"]
151
- and req_content["precision"] == precision.split(".")[-1]
152
- ):
153
- request_file = tmp_request_file
154
- return request_file
155
-
156
-
157
- def get_raw_eval_results(results_path: str, requests_path: str) -> list[EvalResult]:
158
- """From the path of the results folder root, extract all needed info for results"""
159
- model_result_filepaths = []
160
-
161
- for root, _, files in os.walk(results_path):
162
- # We should only have json files in model results
163
- if len(files) == 0 or any([not f.endswith(".json") for f in files]):
164
- continue
165
-
166
- # Sort the files by date
167
- try:
168
- files.sort(key=lambda x: x.removesuffix(".json").removeprefix("results_")[:-7])
169
- except dateutil.parser._parser.ParserError:
170
- files = [files[-1]]
171
-
172
- for file in files:
173
- model_result_filepaths.append(os.path.join(root, file))
174
-
175
- eval_results = {}
176
- for model_result_filepath in model_result_filepaths:
177
- # Creation of result
178
- eval_result = EvalResult.init_from_json_file(model_result_filepath)
179
- eval_result.update_with_request_file(requests_path)
180
-
181
- # Store results of same eval together
182
- eval_name = eval_result.eval_name
183
- if eval_name in eval_results.keys():
184
- eval_results[eval_name].results.update({k: v for k, v in eval_result.results.items() if v is not None})
185
- else:
186
- eval_results[eval_name] = eval_result
187
-
188
- results = []
189
- for v in eval_results.values():
190
- try:
191
- v.to_dict() # we test if the dict version is complete
192
- results.append(v)
193
- except KeyError: # not all eval values present
194
- continue
195
-
196
- return results
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
src/populate.py DELETED
@@ -1,58 +0,0 @@
1
- import json
2
- import os
3
-
4
- import pandas as pd
5
-
6
- from src.display.formatting import has_no_nan_values, make_clickable_model
7
- from src.display.utils import AutoEvalColumn, EvalQueueColumn
8
- from src.leaderboard.read_evals import get_raw_eval_results
9
-
10
-
11
- def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
12
- """Creates a dataframe from all the individual experiment results"""
13
- raw_data = get_raw_eval_results(results_path, requests_path)
14
- all_data_json = [v.to_dict() for v in raw_data]
15
-
16
- df = pd.DataFrame.from_records(all_data_json)
17
- df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
18
- df = df[cols].round(decimals=2)
19
-
20
- # filter out if any of the benchmarks have not been produced
21
- df = df[has_no_nan_values(df, benchmark_cols)]
22
- return df
23
-
24
-
25
- def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
26
- """Creates the different dataframes for the evaluation queues requestes"""
27
- entries = [entry for entry in os.listdir(save_path) if not entry.startswith(".")]
28
- all_evals = []
29
-
30
- for entry in entries:
31
- if ".json" in entry:
32
- file_path = os.path.join(save_path, entry)
33
- with open(file_path) as fp:
34
- data = json.load(fp)
35
-
36
- data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
37
- data[EvalQueueColumn.revision.name] = data.get("revision", "main")
38
-
39
- all_evals.append(data)
40
- elif ".md" not in entry:
41
- # this is a folder
42
- sub_entries = [e for e in os.listdir(f"{save_path}/{entry}") if os.path.isfile(e) and not e.startswith(".")]
43
- for sub_entry in sub_entries:
44
- file_path = os.path.join(save_path, entry, sub_entry)
45
- with open(file_path) as fp:
46
- data = json.load(fp)
47
-
48
- data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
49
- data[EvalQueueColumn.revision.name] = data.get("revision", "main")
50
- all_evals.append(data)
51
-
52
- pending_list = [e for e in all_evals if e["status"] in ["PENDING", "RERUN"]]
53
- running_list = [e for e in all_evals if e["status"] == "RUNNING"]
54
- finished_list = [e for e in all_evals if e["status"].startswith("FINISHED") or e["status"] == "PENDING_NEW_EVAL"]
55
- df_pending = pd.DataFrame.from_records(pending_list, columns=cols)
56
- df_running = pd.DataFrame.from_records(running_list, columns=cols)
57
- df_finished = pd.DataFrame.from_records(finished_list, columns=cols)
58
- return df_finished[cols], df_running[cols], df_pending[cols]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
src/submission/check_validity.py DELETED
@@ -1,99 +0,0 @@
1
- import json
2
- import os
3
- import re
4
- from collections import defaultdict
5
- from datetime import datetime, timedelta, timezone
6
-
7
- import huggingface_hub
8
- from huggingface_hub import ModelCard
9
- from huggingface_hub.hf_api import ModelInfo
10
- from transformers import AutoConfig
11
- from transformers.models.auto.tokenization_auto import AutoTokenizer
12
-
13
- def check_model_card(repo_id: str) -> tuple[bool, str]:
14
- """Checks if the model card and license exist and have been filled"""
15
- try:
16
- card = ModelCard.load(repo_id)
17
- except huggingface_hub.utils.EntryNotFoundError:
18
- return False, "Please add a model card to your model to explain how you trained/fine-tuned it."
19
-
20
- # Enforce license metadata
21
- if card.data.license is None:
22
- if not ("license_name" in card.data and "license_link" in card.data):
23
- return False, (
24
- "License not found. Please add a license to your model card using the `license` metadata or a"
25
- " `license_name`/`license_link` pair."
26
- )
27
-
28
- # Enforce card content
29
- if len(card.text) < 200:
30
- return False, "Please add a description to your model card, it is too short."
31
-
32
- return True, ""
33
-
34
- def is_model_on_hub(model_name: str, revision: str, token: str = None, trust_remote_code=False, test_tokenizer=False) -> tuple[bool, str]:
35
- """Checks if the model model_name is on the hub, and whether it (and its tokenizer) can be loaded with AutoClasses."""
36
- try:
37
- config = AutoConfig.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token)
38
- if test_tokenizer:
39
- try:
40
- tk = AutoTokenizer.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token)
41
- except ValueError as e:
42
- return (
43
- False,
44
- f"uses a tokenizer which is not in a transformers release: {e}",
45
- None
46
- )
47
- except Exception as e:
48
- return (False, "'s tokenizer cannot be loaded. Is your tokenizer class in a stable transformers release, and correctly configured?", None)
49
- return True, None, config
50
-
51
- except ValueError:
52
- return (
53
- False,
54
- "needs to be launched with `trust_remote_code=True`. For safety reason, we do not allow these models to be automatically submitted to the leaderboard.",
55
- None
56
- )
57
-
58
- except Exception as e:
59
- return False, "was not found on hub!", None
60
-
61
-
62
- def get_model_size(model_info: ModelInfo, precision: str):
63
- """Gets the model size from the configuration, or the model name if the configuration does not contain the information."""
64
- try:
65
- model_size = round(model_info.safetensors["total"] / 1e9, 3)
66
- except (AttributeError, TypeError):
67
- return 0 # Unknown model sizes are indicated as 0, see NUMERIC_INTERVALS in app.py
68
-
69
- size_factor = 8 if (precision == "GPTQ" or "gptq" in model_info.modelId.lower()) else 1
70
- model_size = size_factor * model_size
71
- return model_size
72
-
73
- def get_model_arch(model_info: ModelInfo):
74
- """Gets the model architecture from the configuration"""
75
- return model_info.config.get("architectures", "Unknown")
76
-
77
- def already_submitted_models(requested_models_dir: str) -> set[str]:
78
- """Gather a list of already submitted models to avoid duplicates"""
79
- depth = 1
80
- file_names = []
81
- users_to_submission_dates = defaultdict(list)
82
-
83
- for root, _, files in os.walk(requested_models_dir):
84
- current_depth = root.count(os.sep) - requested_models_dir.count(os.sep)
85
- if current_depth == depth:
86
- for file in files:
87
- if not file.endswith(".json"):
88
- continue
89
- with open(os.path.join(root, file), "r") as f:
90
- info = json.load(f)
91
- file_names.append(f"{info['model']}_{info['revision']}_{info['precision']}")
92
-
93
- # Select organisation
94
- if info["model"].count("/") == 0 or "submitted_time" not in info:
95
- continue
96
- organisation, _ = info["model"].split("/")
97
- users_to_submission_dates[organisation].append(info["submitted_time"])
98
-
99
- return set(file_names), users_to_submission_dates
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
src/submission/submit.py DELETED
@@ -1,119 +0,0 @@
1
- import json
2
- import os
3
- from datetime import datetime, timezone
4
-
5
- from src.display.formatting import styled_error, styled_message, styled_warning
6
- from src.envs import API, EVAL_REQUESTS_PATH, TOKEN, QUEUE_REPO
7
- from src.submission.check_validity import (
8
- already_submitted_models,
9
- check_model_card,
10
- get_model_size,
11
- is_model_on_hub,
12
- )
13
-
14
- REQUESTED_MODELS = None
15
- USERS_TO_SUBMISSION_DATES = None
16
-
17
- def add_new_eval(
18
- model: str,
19
- base_model: str,
20
- revision: str,
21
- precision: str,
22
- weight_type: str,
23
- model_type: str,
24
- ):
25
- global REQUESTED_MODELS
26
- global USERS_TO_SUBMISSION_DATES
27
- if not REQUESTED_MODELS:
28
- REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH)
29
-
30
- user_name = ""
31
- model_path = model
32
- if "/" in model:
33
- user_name = model.split("/")[0]
34
- model_path = model.split("/")[1]
35
-
36
- precision = precision.split(" ")[0]
37
- current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
38
-
39
- if model_type is None or model_type == "":
40
- return styled_error("Please select a model type.")
41
-
42
- # Does the model actually exist?
43
- if revision == "":
44
- revision = "main"
45
-
46
- # Is the model on the hub?
47
- if weight_type in ["Delta", "Adapter"]:
48
- base_model_on_hub, error, _ = is_model_on_hub(model_name=base_model, revision=revision, token=TOKEN, test_tokenizer=True)
49
- if not base_model_on_hub:
50
- return styled_error(f'Base model "{base_model}" {error}')
51
-
52
- if not weight_type == "Adapter":
53
- model_on_hub, error, _ = is_model_on_hub(model_name=model, revision=revision, token=TOKEN, test_tokenizer=True)
54
- if not model_on_hub:
55
- return styled_error(f'Model "{model}" {error}')
56
-
57
- # Is the model info correctly filled?
58
- try:
59
- model_info = API.model_info(repo_id=model, revision=revision)
60
- except Exception:
61
- return styled_error("Could not get your model information. Please fill it up properly.")
62
-
63
- model_size = get_model_size(model_info=model_info, precision=precision)
64
-
65
- # Were the model card and license filled?
66
- try:
67
- license = model_info.cardData["license"]
68
- except Exception:
69
- return styled_error("Please select a license for your model")
70
-
71
- modelcard_OK, error_msg = check_model_card(model)
72
- if not modelcard_OK:
73
- return styled_error(error_msg)
74
-
75
- # Seems good, creating the eval
76
- print("Adding new eval")
77
-
78
- eval_entry = {
79
- "model": model,
80
- "base_model": base_model,
81
- "revision": revision,
82
- "precision": precision,
83
- "weight_type": weight_type,
84
- "status": "PENDING",
85
- "submitted_time": current_time,
86
- "model_type": model_type,
87
- "likes": model_info.likes,
88
- "params": model_size,
89
- "license": license,
90
- "private": False,
91
- }
92
-
93
- # Check for duplicate submission
94
- if f"{model}_{revision}_{precision}" in REQUESTED_MODELS:
95
- return styled_warning("This model has been already submitted.")
96
-
97
- print("Creating eval file")
98
- OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}"
99
- os.makedirs(OUT_DIR, exist_ok=True)
100
- out_path = f"{OUT_DIR}/{model_path}_eval_request_False_{precision}_{weight_type}.json"
101
-
102
- with open(out_path, "w") as f:
103
- f.write(json.dumps(eval_entry))
104
-
105
- print("Uploading eval file")
106
- API.upload_file(
107
- path_or_fileobj=out_path,
108
- path_in_repo=out_path.split("eval-queue/")[1],
109
- repo_id=QUEUE_REPO,
110
- repo_type="dataset",
111
- commit_message=f"Add {model} to eval queue",
112
- )
113
-
114
- # Remove the local file
115
- os.remove(out_path)
116
-
117
- return styled_message(
118
- "Your request has been submitted to the evaluation queue!\nPlease wait for up to an hour for the model to show in the PENDING list."
119
- )