lykeven commited on
Commit
6accf0d
1 Parent(s): 8d00201

add grounding

Browse files
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
+ *model filter=lfs diff=lfs merge=lfs -text
app.py CHANGED
@@ -9,7 +9,10 @@ import time
9
 
10
  DESCRIPTION = '''# <a href="https://github.com/THUDM/CogVLM">VisualGLM</a>'''
11
 
12
- MAINTENANCE_NOTICE1 = 'Hint 1: If the app report "Something went wrong, connection error out", please turn off your proxy and retry.\nHint 2: If you upload a large size of image like 10MB, it may take some time to upload and process. Please be patient and wait.'
 
 
 
13
 
14
  NOTES = 'This app is adapted from <a href="https://github.com/THUDM/CogVLM">https://github.com/THUDM/CogVLM</a>. It would be recommended to check out the repo if you want to see the detail of our model.'
15
 
@@ -17,6 +20,7 @@ import json
17
  import requests
18
  import base64
19
  import hashlib
 
20
 
21
  default_chatbox = [("", "Hi, What do you want to know about this image?")]
22
 
@@ -45,6 +49,7 @@ def process_image_without_resize(image_prompt):
45
  timestamp = int(time.time())
46
  file_ext = os.path.splitext(image_prompt)[1]
47
  filename = f"examples/{timestamp}{file_ext}"
 
48
  image.save(filename)
49
  print(f"temporal filename {filename}")
50
  with open(filename, "rb") as image_file:
@@ -52,7 +57,7 @@ def process_image_without_resize(image_prompt):
52
  encoded_img = str(bytes, encoding='utf-8')
53
  image_hash = hashlib.sha256(bytes).hexdigest()
54
  os.remove(filename)
55
- return encoded_img, image_hash
56
 
57
 
58
  def is_chinese(text):
@@ -66,11 +71,12 @@ def post(
66
  top_p,
67
  image_prompt,
68
  result_previous,
69
- hidden_image
 
70
  ):
71
  result_text = [(ele[0], ele[1]) for ele in result_previous]
72
  for i in range(len(result_text)-1, -1, -1):
73
- if result_text[i][0] == "":
74
  del result_text[i]
75
  print(f"history {result_text}")
76
 
@@ -93,7 +99,7 @@ def post(
93
  "User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.87 Safari/537.36",
94
  }
95
  if image_prompt:
96
- encoded_img, image_hash = process_image_without_resize(image_prompt)
97
  print(f"image_hash:{image_hash}, hidden_image_hash:{hidden_image}")
98
 
99
  if hidden_image is not None and image_hash != hidden_image:
@@ -103,13 +109,14 @@ def post(
103
  else:
104
  encoded_img = None
105
 
106
- print('开始请求...')
107
  data = json.dumps({
108
  'text': input_text,
109
  'image': encoded_img,
110
  'temperature': temperature,
111
  'top_p': top_p,
112
- 'history': result_text
 
113
  })
114
  try:
115
  response = requests.request("POST", URL, headers=headers, data=data, timeout=(60, 100)).json()
@@ -120,11 +127,17 @@ def post(
120
  else:
121
  result_text.append((input_text, 'Timeout! Please wait a few minutes and retry.'))
122
  return "", result_text, hidden_image
123
- print('请求完毕...')
124
  # response = {'result':input_text}
125
 
126
  answer = str(response['result'])
127
- result_text.append((input_text, answer))
 
 
 
 
 
 
128
  print(result_text)
129
  print('finished')
130
  return "", result_text, hidden_image
@@ -157,15 +170,14 @@ def main():
157
  clear_button = gr.Button('Clear')
158
 
159
  image_prompt = gr.Image(type="filepath", label="Image Prompt", value=None)
 
 
 
 
 
160
  with gr.Row():
161
  temperature = gr.Slider(maximum=1, value=0.8, minimum=0, label='Temperature')
162
  top_p = gr.Slider(maximum=1, value=0.4, minimum=0, label='Top P')
163
- with gr.Group():
164
- with gr.Row():
165
- with gr.Column(scale=7):
166
- maintenance_notice = gr.Markdown(MAINTENANCE_NOTICE1)
167
- with gr.Column(scale=2):
168
- change_button = gr.Button('Change hint to English', visible=False)
169
  with gr.Column(scale=5.5):
170
  result_text = gr.components.Chatbot(label='Multi-round conversation History', value=[("", "Hi, What do you want to know about this image?")]).style(height=550)
171
  hidden_image_hash = gr.Textbox(visible=False)
@@ -173,14 +185,15 @@ def main():
173
  gr_examples = gr.Examples(examples=[[example["text"], example["image"]] for example in examples],
174
  inputs=[input_text, image_prompt],
175
  label="Example Inputs (Click to insert an examplet into the input box)",
176
- examples_per_page=3)
177
 
 
178
  gr.Markdown(NOTES)
179
 
180
  print(gr.__version__)
181
- run_button.click(fn=post,inputs=[input_text, temperature, top_p, image_prompt, result_text, hidden_image_hash],
182
  outputs=[input_text, result_text, hidden_image_hash])
183
- input_text.submit(fn=post,inputs=[input_text, temperature, top_p, image_prompt, result_text, hidden_image_hash],
184
  outputs=[input_text, result_text, hidden_image_hash])
185
  clear_button.click(fn=clear_fn, inputs=clear_button, outputs=[input_text, result_text, image_prompt])
186
  image_prompt.upload(fn=clear_fn2, inputs=clear_button, outputs=[result_text])
 
9
 
10
  DESCRIPTION = '''# <a href="https://github.com/THUDM/CogVLM">VisualGLM</a>'''
11
 
12
+ MAINTENANCE_NOTICE1 = 'Hint 1: If the app report "Something went wrong, connection error out", please turn off your proxy and retry.<br>Hint 2: If you upload a large size of image like 10MB, it may take some time to upload and process. Please be patient and wait.'
13
+
14
+ GROUNDING_NOTICE = 'Hint: When you check "Grounding", please use the <a href="https://github.com/THUDM/CogVLM/blob/main/utils/template.py#L344">corresponding prompt</a> or the examples below.'
15
+
16
 
17
  NOTES = 'This app is adapted from <a href="https://github.com/THUDM/CogVLM">https://github.com/THUDM/CogVLM</a>. It would be recommended to check out the repo if you want to see the detail of our model.'
18
 
 
20
  import requests
21
  import base64
22
  import hashlib
23
+ from utils import parse_response
24
 
25
  default_chatbox = [("", "Hi, What do you want to know about this image?")]
26
 
 
49
  timestamp = int(time.time())
50
  file_ext = os.path.splitext(image_prompt)[1]
51
  filename = f"examples/{timestamp}{file_ext}"
52
+ filename_grounding = f"examples/{timestamp}_grounding{file_ext}"
53
  image.save(filename)
54
  print(f"temporal filename {filename}")
55
  with open(filename, "rb") as image_file:
 
57
  encoded_img = str(bytes, encoding='utf-8')
58
  image_hash = hashlib.sha256(bytes).hexdigest()
59
  os.remove(filename)
60
+ return image, encoded_img, image_hash, filename_grounding
61
 
62
 
63
  def is_chinese(text):
 
71
  top_p,
72
  image_prompt,
73
  result_previous,
74
+ hidden_image,
75
+ grounding
76
  ):
77
  result_text = [(ele[0], ele[1]) for ele in result_previous]
78
  for i in range(len(result_text)-1, -1, -1):
79
+ if result_text[i][0] == "" or result_text[i][0] == None:
80
  del result_text[i]
81
  print(f"history {result_text}")
82
 
 
99
  "User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.87 Safari/537.36",
100
  }
101
  if image_prompt:
102
+ pil_img, encoded_img, image_hash, image_path_grounding = process_image_without_resize(image_prompt)
103
  print(f"image_hash:{image_hash}, hidden_image_hash:{hidden_image}")
104
 
105
  if hidden_image is not None and image_hash != hidden_image:
 
109
  else:
110
  encoded_img = None
111
 
112
+ print('request chat model...' if not grounding else 'request grounding model...')
113
  data = json.dumps({
114
  'text': input_text,
115
  'image': encoded_img,
116
  'temperature': temperature,
117
  'top_p': top_p,
118
+ 'history': result_text,
119
+ 'is_grounding': grounding
120
  })
121
  try:
122
  response = requests.request("POST", URL, headers=headers, data=data, timeout=(60, 100)).json()
 
127
  else:
128
  result_text.append((input_text, 'Timeout! Please wait a few minutes and retry.'))
129
  return "", result_text, hidden_image
130
+ print('request done...')
131
  # response = {'result':input_text}
132
 
133
  answer = str(response['result'])
134
+ if grounding:
135
+ parse_response(pil_img, answer, image_path_grounding)
136
+ new_answer = answer.replace(input_text, "")
137
+ result_text.append((input_text, new_answer))
138
+ result_text.append((None, (image_path_grounding,)))
139
+ else:
140
+ result_text.append((input_text, answer))
141
  print(result_text)
142
  print('finished')
143
  return "", result_text, hidden_image
 
170
  clear_button = gr.Button('Clear')
171
 
172
  image_prompt = gr.Image(type="filepath", label="Image Prompt", value=None)
173
+ with gr.Row():
174
+ grounding = gr.Checkbox(label="Grounding")
175
+ with gr.Row():
176
+ grounding_notice = gr.Markdown(GROUNDING_NOTICE)
177
+
178
  with gr.Row():
179
  temperature = gr.Slider(maximum=1, value=0.8, minimum=0, label='Temperature')
180
  top_p = gr.Slider(maximum=1, value=0.4, minimum=0, label='Top P')
 
 
 
 
 
 
181
  with gr.Column(scale=5.5):
182
  result_text = gr.components.Chatbot(label='Multi-round conversation History', value=[("", "Hi, What do you want to know about this image?")]).style(height=550)
183
  hidden_image_hash = gr.Textbox(visible=False)
 
185
  gr_examples = gr.Examples(examples=[[example["text"], example["image"]] for example in examples],
186
  inputs=[input_text, image_prompt],
187
  label="Example Inputs (Click to insert an examplet into the input box)",
188
+ examples_per_page=6)
189
 
190
+ gr.Markdown(MAINTENANCE_NOTICE1)
191
  gr.Markdown(NOTES)
192
 
193
  print(gr.__version__)
194
+ run_button.click(fn=post,inputs=[input_text, temperature, top_p, image_prompt, result_text, hidden_image_hash, grounding],
195
  outputs=[input_text, result_text, hidden_image_hash])
196
+ input_text.submit(fn=post,inputs=[input_text, temperature, top_p, image_prompt, result_text, hidden_image_hash, grounding],
197
  outputs=[input_text, result_text, hidden_image_hash])
198
  clear_button.click(fn=clear_fn, inputs=clear_button, outputs=[input_text, result_text, image_prompt])
199
  image_prompt.upload(fn=clear_fn2, inputs=clear_button, outputs=[result_text])
en_core_web_sm-3.6.0/LICENSE ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Copyright 2021 ExplosionAI GmbH
2
+
3
+ Permission is hereby granted, free of charge, to any person obtaining a copy of
4
+ this software and associated documentation files (the "Software"), to deal in
5
+ the Software without restriction, including without limitation the rights to
6
+ use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
7
+ of the Software, and to permit persons to whom the Software is furnished to do
8
+ so, subject to the following conditions:
9
+
10
+ The above copyright notice and this permission notice shall be included in all
11
+ copies or substantial portions of the Software.
12
+
13
+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
14
+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
15
+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
16
+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
17
+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
18
+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
19
+ SOFTWARE.
en_core_web_sm-3.6.0/LICENSES_SOURCES ADDED
@@ -0,0 +1,66 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # OntoNotes 5
2
+
3
+ * Author: Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston
4
+ * URL: https://catalog.ldc.upenn.edu/LDC2013T19
5
+ * License: commercial (licensed by Explosion)
6
+
7
+ ```
8
+ ```
9
+
10
+
11
+
12
+
13
+ # ClearNLP Constituent-to-Dependency Conversion
14
+
15
+ * Author: Emory University
16
+ * URL: https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md
17
+ * License: Citation provided for reference, no code packaged with model
18
+
19
+ ```
20
+ ```
21
+
22
+
23
+
24
+
25
+ # WordNet 3.0
26
+
27
+ * Author: Princeton University
28
+ * URL: https://wordnet.princeton.edu/
29
+ * License: WordNet 3.0 License
30
+
31
+ ```
32
+ WordNet Release 3.0
33
+
34
+ This software and database is being provided to you, the LICENSEE, by
35
+ Princeton University under the following license. By obtaining, using
36
+ and/or copying this software and database, you agree that you have
37
+ read, understood, and will comply with these terms and conditions.:
38
+
39
+ Permission to use, copy, modify and distribute this software and
40
+ database and its documentation for any purpose and without fee or
41
+ royalty is hereby granted, provided that you agree to comply with
42
+ the following copyright notice and statements, including the disclaimer,
43
+ and that the same appear on ALL copies of the software, database and
44
+ documentation, including modifications that you make for internal
45
+ use or for distribution.
46
+
47
+ WordNet 3.0 Copyright 2006 by Princeton University. All rights reserved.
48
+
49
+ THIS SOFTWARE AND DATABASE IS PROVIDED "AS IS" AND PRINCETON
50
+ UNIVERSITY MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
51
+ IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, PRINCETON
52
+ UNIVERSITY MAKES NO REPRESENTATIONS OR WARRANTIES OF MERCHANT-
53
+ ABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE
54
+ OF THE LICENSED SOFTWARE, DATABASE OR DOCUMENTATION WILL NOT
55
+ INFRINGE ANY THIRD PARTY PATENTS, COPYRIGHTS, TRADEMARKS OR
56
+ OTHER RIGHTS.
57
+
58
+ The name of Princeton University or Princeton may not be used in
59
+ advertising or publicity pertaining to distribution of the software
60
+ and/or database. Title to copyright in this software, database and
61
+ any associated documentation shall at all times remain with
62
+ Princeton University and LICENSEE agrees to preserve same.```
63
+
64
+
65
+
66
+
en_core_web_sm-3.6.0/README.md ADDED
@@ -0,0 +1,47 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ### Details: https://spacy.io/models/en#en_core_web_sm
2
+
3
+ English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.
4
+
5
+ | Feature | Description |
6
+ | --- | --- |
7
+ | **Name** | `en_core_web_sm` |
8
+ | **Version** | `3.6.0` |
9
+ | **spaCy** | `>=3.6.0,<3.7.0` |
10
+ | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
11
+ | **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
12
+ | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
13
+ | **Sources** | [OntoNotes 5](https://catalog.ldc.upenn.edu/LDC2013T19) (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston)<br />[ClearNLP Constituent-to-Dependency Conversion](https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md) (Emory University)<br />[WordNet 3.0](https://wordnet.princeton.edu/) (Princeton University) |
14
+ | **License** | `MIT` |
15
+ | **Author** | [Explosion](https://explosion.ai) |
16
+
17
+ ### Label Scheme
18
+
19
+ <details>
20
+
21
+ <summary>View label scheme (113 labels for 3 components)</summary>
22
+
23
+ | Component | Labels |
24
+ | --- | --- |
25
+ | **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, `_SP`, ```` |
26
+ | **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` |
27
+ | **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
28
+
29
+ </details>
30
+
31
+ ### Accuracy
32
+
33
+ | Type | Score |
34
+ | --- | --- |
35
+ | `TOKEN_ACC` | 99.86 |
36
+ | `TOKEN_P` | 99.57 |
37
+ | `TOKEN_R` | 99.58 |
38
+ | `TOKEN_F` | 99.57 |
39
+ | `TAG_ACC` | 97.25 |
40
+ | `SENTS_P` | 92.02 |
41
+ | `SENTS_R` | 89.21 |
42
+ | `SENTS_F` | 90.59 |
43
+ | `DEP_UAS` | 91.75 |
44
+ | `DEP_LAS` | 89.87 |
45
+ | `ENTS_P` | 84.55 |
46
+ | `ENTS_R` | 84.57 |
47
+ | `ENTS_F` | 84.56 |
en_core_web_sm-3.6.0/accuracy.json ADDED
@@ -0,0 +1,330 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "token_acc": 0.9986194413,
3
+ "token_p": 0.9956819193,
4
+ "token_r": 0.9957659295,
5
+ "token_f": 0.9957239226,
6
+ "tag_acc": 0.97246532,
7
+ "sents_p": 0.9201877934,
8
+ "sents_r": 0.8921432812,
9
+ "sents_f": 0.9059485531,
10
+ "dep_uas": 0.9175304332,
11
+ "dep_las": 0.89874821,
12
+ "dep_las_per_type": {
13
+ "prep": {
14
+ "p": 0.853521338,
15
+ "r": 0.8635932461,
16
+ "f": 0.8585277532
17
+ },
18
+ "det": {
19
+ "p": 0.9763930156,
20
+ "r": 0.9781048683,
21
+ "f": 0.9772481923
22
+ },
23
+ "pobj": {
24
+ "p": 0.9613764045,
25
+ "r": 0.967681131,
26
+ "f": 0.9645184649
27
+ },
28
+ "nsubj": {
29
+ "p": 0.9565737052,
30
+ "r": 0.9467250821,
31
+ "f": 0.9516239128
32
+ },
33
+ "aux": {
34
+ "p": 0.9815061794,
35
+ "r": 0.9827294578,
36
+ "f": 0.9821174377
37
+ },
38
+ "advmod": {
39
+ "p": 0.8548033091,
40
+ "r": 0.8519266364,
41
+ "f": 0.8533625485
42
+ },
43
+ "relcl": {
44
+ "p": 0.7571736011,
45
+ "r": 0.7659651669,
46
+ "f": 0.7615440115
47
+ },
48
+ "root": {
49
+ "p": 0.9195942266,
50
+ "r": 0.8910218352,
51
+ "f": 0.9050825879
52
+ },
53
+ "xcomp": {
54
+ "p": 0.8836222144,
55
+ "r": 0.8966259871,
56
+ "f": 0.8900766079
57
+ },
58
+ "amod": {
59
+ "p": 0.9174389766,
60
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en_core_web_sm-3.6.0/vocab/strings.json ADDED
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en_core_web_sm-3.6.0/vocab/vectors ADDED
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en_core_web_sm-3.6.0/vocab/vectors.cfg ADDED
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+ {
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+ "mode":"default"
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+ }
examples/4.png ADDED
examples/5.jpg ADDED
examples/6.jpg ADDED
examples/example_inputs.jsonl CHANGED
@@ -1,3 +1,6 @@
1
  {"id":1, "text": "Describe this image", "image": "examples/1.png"}
2
  {"id":2, "text": "What is written in the image?", "image": "examples/2.jpg"}
3
- {"id":3, "text": "How many houses are there in this cartoon?", "image": "examples/3.jpg"}
 
 
 
 
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  {"id":1, "text": "Describe this image", "image": "examples/1.png"}
2
  {"id":2, "text": "What is written in the image?", "image": "examples/2.jpg"}
3
+ {"id":3, "text": "How many houses are there in this cartoon?", "image": "examples/3.jpg"}
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+ {"id":4, "text": "Can you provide a description of the image and include the coordinates [[x0,y0,x1,y1]] for each mentioned object?", "image": "examples/4.png"}
5
+ {"id":5, "text": "Where is the tree closer to the sun?", "image": "examples/5.jpg"}
6
+ {"id":6, "text": "What color are the clothes of the girl whose hands are holding flowers? Let's think step by step", "image": "examples/6.jpg"}
requirements.txt ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ gradio
2
+ seaborn
3
+ PIL
4
+ base64
5
+ matplotlib
6
+ spacy==3.6.0
7
+ requests
8
+ hashlib
utils.py ADDED
@@ -0,0 +1,86 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import seaborn as sns
2
+ from PIL import Image, ImageDraw, ImageFont
3
+ import matplotlib.font_manager
4
+ import spacy
5
+ import re
6
+
7
+ nlp = spacy.load("en_core_web_sm-3.6.0")
8
+
9
+ def draw_boxes(image, boxes, texts, output_fn='output.png'):
10
+ box_width = 5
11
+ color_palette = sns.color_palette("husl", len(boxes))
12
+ colors = [(int(r*255), int(g*255), int(b*255)) for r, g, b in color_palette]
13
+
14
+ width, height = image.size
15
+ absolute_boxes = [[(int(box[0] * width), int(box[1] * height), int(box[2] * width), int(box[3] * height)) for box in b] for b in boxes]
16
+
17
+ overlay = Image.new('RGBA', image.size, (255, 255, 255, 0))
18
+ draw = ImageDraw.Draw(overlay)
19
+ font_path = sorted(matplotlib.font_manager.findSystemFonts(fontpaths=None, fontext='ttf'))[0]
20
+ font = ImageFont.truetype(font_path, size=26)
21
+
22
+ for box, text, color in zip(absolute_boxes, texts, colors):
23
+ for b in box:
24
+ draw.rectangle(b, outline=color, width=box_width)
25
+ if not text:
26
+ continue
27
+ splited_text = text.split('\n')
28
+ num_lines = len(splited_text)
29
+ text_width, text_height = font.getbbox(splited_text[0])[-2:]
30
+ y_start = b[3] - text_height * num_lines - box_width
31
+ if b[2] - b[0] < 100 or b[3] - b[1] < 100:
32
+ y_start = b[3]
33
+ for i, line in enumerate(splited_text):
34
+ text_width, text_height = font.getbbox(line)[-2:]
35
+ x = b[0] + box_width
36
+ y = y_start + text_height * i
37
+ draw.rectangle([x, y, x+text_width, y+text_height], fill=(128, 128, 128, 160))
38
+ draw.text((x, y), line, font=font, fill=(255, 255, 255))
39
+ img_with_overlay = Image.alpha_composite(image.convert('RGBA'), overlay).convert('RGB')
40
+ img_with_overlay.save(output_fn)
41
+
42
+ def boxstr_to_boxes(box_str):
43
+ boxes = [[int(y)/1000 for y in x.split(',')] for x in box_str.split(';') if x.replace(',', '').isdigit()]
44
+ return boxes
45
+
46
+ def text_to_dict(text):
47
+ doc = nlp(text)
48
+
49
+ box_matches = list(re.finditer(r'\[\[([^\]]+)\]\]', text))
50
+ box_positions = [match.start() for match in box_matches]
51
+
52
+ noun_phrases = []
53
+ boxes = []
54
+
55
+ for match, box_position in zip(box_matches, box_positions):
56
+ nearest_np_start = max([0] + [chunk.start_char for chunk in doc.noun_chunks if chunk.end_char <= box_position])
57
+ noun_phrase = text[nearest_np_start:box_position].strip()
58
+ if noun_phrase and noun_phrase[-1] == '?':
59
+ noun_phrase = text[:box_position].strip()
60
+ box_string = match.group(1)
61
+
62
+ noun_phrases.append(noun_phrase)
63
+ boxes.append(boxstr_to_boxes(box_string))
64
+
65
+ pairs = []
66
+ for noun_phrase, box_string in zip(noun_phrases, boxes):
67
+ pairs.append((noun_phrase.lower(), box_string))
68
+ return dict(pairs)
69
+
70
+ def parse_response(img, response, output_fn='output.png'):
71
+ img = img.convert('RGB')
72
+ width, height = img.size
73
+ ratio = min(1920 / width, 1080 / height)
74
+ new_width = int(width * ratio)
75
+ new_height = int(height * ratio)
76
+ new_img = img.resize((new_width, new_height), Image.LANCZOS)
77
+ pattern = r"\[\[(.*?)\]\]"
78
+ positions = re.findall(pattern, response)
79
+ boxes = [[[int(y) for y in x.split(',')] for x in pos.split(';') if x.replace(',', '').isdigit()] for pos in positions]
80
+ dic = text_to_dict(response)
81
+ if not dic:
82
+ texts = []
83
+ boxes = []
84
+ else:
85
+ texts, boxes = zip(*dic.items())
86
+ draw_boxes(new_img, boxes, texts, output_fn=output_fn)