Wiki source code of The AISOP recipe

Version 10.1 by Andreas Isking on 2025/02/28 18:15
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Paul Libbrecht 1.1 1 (% class="jumbotron" %)
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AISOP Admin 1.4 5 = The AISOP recipe =
Paul Libbrecht 1.1 6
AISOP Admin 1.4 7 The AISOP webapp is a service built as the result of various training and configurations.
8 This recipe explains how to extract the content fragments, annotate them, and create model trained on it. This will let us create a pipeline and a seminar on which we can analyse portfolios.
Paul Libbrecht 1.1 9 )))
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AISOP Admin 1.5 12
AISOP Admin 1.4 13 == Basic Terms ==
Paul Libbrecht 1.1 14
AISOP Admin 1.4 15 The context of the AISOP-web-app usage is that of a course at learning institution which typically has fixed students and fixed contents. A course can contain multiple courses or modules.
Paul Libbrecht 1.1 16
AISOP Admin 1.4 17 * **AISOP Web-app:** The nodeJS server that interfaces with the portfolio-composing system.
18 * **Portfolio:** the content written by a student in order to represent his or her progress, learning and knowledge using a textual and graphical form. Generally expressed in HTML, can be embedded in various web-pages.
19 * **Course-contents:** The set of slides, their annotations, the videos and handouts that normally read by students and teachers.
20 * **Analysis:** The set of programmes that recognize and measure the contents of a portfolio. Often also the name of the resulting interactive presentation (which can feature summaries or enriched portfolio views).
21 * **Composition Platform:** A space where the portfolio is written. Normally a web-space. In AISOP we have focussed on the classical e-portfol;io composition platform Mahara (a PHP server).
Paul Libbrecht 1.1 22
AISOP Admin 1.4 23 ----
Paul Libbrecht 1.1 24
AISOP Admin 1.4 25 == 1) Data Preparation ==
Paul Libbrecht 1.1 26
AISOP Admin 1.4 27 === 1.1: Make a Concept Map ===
Paul Libbrecht 1.1 28
AISOP Admin 1.4 29 Employing tools such as CMapTools, create a graphical concept map that represents the topics of the course. This concept map can be familiar with the teachers and learners of this course as a way to show the paths through the content.
Paul Libbrecht 1.1 30
AISOP Admin 1.4 31 From the concept map, extract a .cxl file which carries the same information and will be presented on the web-page.
Paul Libbrecht 1.1 32
AISOP Admin 1.4 33 From the concept map, also extract a hierarchy of topics, assuming there is more than (approx) 10 topics in the map. The hierarchy should be a text file with a label per line and the label indented to the right in case of children relation as in the following example:
Paul Libbrecht 1.1 34
AISOP Admin 1.4 35 >Algorithmization
36 > Flow Charts
37 > Programming
38 > Programming Paradigm
AISOP Admin 1.6 39 > Imperative Programming
40 >Data-Structure
41 > ....
42 >Operating System
43 > ....
Paul Libbrecht 1.1 44
AISOP Admin 1.6 45 We'll name this file labels-all-depths.txt. From this text file, extract a text file with only the top labels (in the extract above only Algorithmization, Data-Structure and Operating System), named labels-depth1.txt.
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AISOP Admin 1.4 47 === 1.2: Extract Text of the Course Content ===
Paul Libbrecht 1.1 48
AISOP Admin 1.4 49 In order for the topic recognition to work, a model needs to be trained that will recognize the words used by the students to denote a part or another of the course. This allows to create relations between the concepts of the course and the paragraphs of the portfolio and offer these in the interactive dashboards. The training is the result of annotating fragments of texts which, first, need to be extracted from their media, be them PDF files, PowerPoint slides, scanned texts or student works. These texts will not be shared so that even protected material or even personal-information carrying texts can be used.
Paul Libbrecht 1.1 50
AISOP Admin 1.4 51 Practically:
Paul Libbrecht 1.1 52
AISOP Admin 1.6 53 * Make all documents accessible for you to open and browse (e.g. download them or get the authorized accesses)
54 * Install and launch the [[clipboard extractor>>https://gitlab.com/aisop/aisop-hacking/-/tree/main/aisop-clipboard-extractor?ref_type=heads]] which will gather the fragments in a text file
55 * Go through all contents and copy each fragment. A fragment is expected to be the size of a paragraph so this is what you should copy.
56 * The extractor should have copied all the fragments in one file. Which we shall call extraction.json.
57 * The least amount of content to be extracted is the complete set of slides and their comments. We recommend to use past students' e-portfolios too. We had rather good experience with about 1000 fragments for a course.
58 * If interrupted, the process may create several JSON files. You can combine them using the [[merge-tool>>https://gitlab.com/aisop/aisop-hacking/-/tree/main/merge-json-files]].
Paul Libbrecht 1.1 59
Andreas Isking 10.1 60
61 ==== 1.2.1: Extract Text from PDF or PNG (PDF → PNG → Text) ====
62
63 Text extraction from PDFs is sometimes faulty. Additionally, many PDFs contain images. To capture this text, Tesseract can be used. A brief explanation of how to use it is provided here.
64
65 **Prerequisites**
66
67 ~1. Tesseract must be installed
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71 ##tesseract ~-~-version##
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74 2. Poppler must be installed
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76 (% class="box" %)
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78 ##brew install poppler##
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80
81 **Code**
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85 ##for pdf in *.pdf; do
86 # Extract the base name of the PDF without the extension
87 basename="${pdf%.pdf}"
88 \\# Convert PDF to PNGs
89 pdftoppm -png "$pdf" "$basename"
90 \\# Create a text file with the same name as the PDF.
91 for png in "$basename"-*.png; do
92 tesseract "$png" stdout -l deu ~-~-oem 1 | tr '~\~\n' ' ' | sed 's/  */ /g' >> "$basename.txt"
93 echo -e "~\~\n~\~\n~-~--~\~\n~\~\n" >> "$basename.txt"
94 done
95 done
96 rm *.png##
97 )))
98
99 **Explanation**
100
101 ~1. ##for pdf in *.pdf; do ...; done##
102
103 • Loops through all PDF files in the directory.
104
105 2. ##basename="${pdf%.pdf}"##
106
107 • Extracts the filename of the PDF without the .pdf extension.
108
109 3. ##pdftoppm -png "$pdf" "$basename"##
110
111 • Converts the PDF into PNG images, named in the format BASENAME-1.png, BASENAME-2.png, etc.
112
113 4. ##for png in "$basename"-*.png; do ...; done##
114
115 • Processes only the PNG files generated from the current PDF.
116
117 5. ##tesseract "$png" stdout -l deu ~-~-oem 1##
118
119 • Performs OCR on the PNG file.
120
121 6. ##tr '\n' ' '##
122
123 • Replaces line breaks with spaces.
124
125 7. ##sed 's/  */ /g'##
126
127 • Reduces multiple spaces to a single space.
128
129 8. ##>> "$basename.txt"##
130
131 • Appends the recognized text to a text file with the same name as the PDF.
132
133 9. ##echo -e "\n\n~-~--\n\n" >> "$basename.txt"##
134
135 • Adds a separator line (~-~--) after each page.
136
137 **Result**
138
139 • A separate text file is created for each PDF, e.g.:
140
141 • file1.txt for file1.pdf
142
143 • file2.txt for file2.pdf
144
145 • The OCR results of all pages from the respective PDF are written into this text file.
146
147 • Each page is separated by a separator line (~-~--).
148
149 • Temporary PNG files are deleted at the end.
150
AISOP Admin 1.4 151 === 1.3: Annotate Text Fragments ===
152
AISOP Admin 1.6 153 It is time to endow the fragments with topics so that we can recognize students' paragraphs' topics. In AISOP, we have used the (commercial) [[prodigy>>https://prodi.gy/]] for this task in two steps which, both, iterate through all fragments to give them topics.
Paul Libbrecht 1.1 154
AISOP Admin 8.1 155 **The first step: top-level-labels:** This is the simple [["text classifier" recipe>>https://prodi.gy/docs/recipes#textcat]] of prodigy: we can invoke the following command for this: ##prodigy textcat.manual the-course-name-l1 ./fragments.jsonl ~-~-label labels-depth1.txt##  which will offer a web-interface on which each fragment is annotated with the (top-level) label. This web-interface can be left running for several days.
156 Then extract the content into a file: ##prodigy db-out the-course-name-l1 > the-course-name-dbout.jsonl##
AISOP Admin 1.6 157
AISOP Admin 9.1 158 **The second step is the hierarchical annotation** [[custom recipe>>https://gitlab.com/aisop/aisop-nlp/-/tree/main/hierarchical_annotation?ref_type=heads]] (link to become public soon): The same fragments are now annotated with the top-level annotation and all their children. E.g. using the command ##python -m prodigy subcat_annotate_with_top2 the-course-name-l2 \
159 the-course-name-dbout.jsonl labels-all-depths.txt  -F ./subcat_annotate_with_top2.py##.
AISOP Admin 1.6 160
AISOP Admin 1.7 161 The resulting data-set can be extracted out of prodigy using the db-out recipe, e.g. prodigy db-out the-course-name-l2 the-course-name-l2-dbout or can be converted to a spaCy dataset for training e.g. using the command xxxxx (see [[here>>https://gitlab.com/aisop/aisop-nlp/-/tree/main/it3/fundamental-principles]])
AISOP Admin 1.6 162
163
AISOP Admin 1.4 164 ----
Paul Libbrecht 1.1 165
AISOP Admin 1.4 166 == 2) Deployment ==
Paul Libbrecht 1.1 167
AISOP Admin 1.4 168 === 2.1 Train a Recognition Model ===
Paul Libbrecht 1.1 169
AISOP Admin 1.7 170 See [[here>>https://gitlab.com/aisop/aisop-nlp/-/tree/main/it3/fundamental-principles]].
AISOP Admin 1.4 171
172 === 2.2 Create a Pipeline ===
173
AISOP Admin 1.7 174 ... write down the configuration JSON of the pipeline, get inspired [[pipeline-medieninderlehre.json>>https://gitlab.com/aisop/aisop-webapp/-/blob/main/config/couchdb/pipeline-medieninderlehre.json?ref_type=heads]]
AISOP Admin 1.4 175
176 === 2.3 Create a Seminar and Import Content ===
177
178 ...
179
AISOP Admin 1.7 180 Create a seminar with the web-interface, associate the appropriate pipeline.
181
AISOP Admin 1.4 182 === 2.4 Interface with the composition platform ===
183
AISOP Admin 1.7 184 See the Mahara authorization configuration.
AISOP Admin 1.4 185
186 ----
187
188 == 3) Usage ==
189
190 === 3.1 Invite Users ===
191
192 ...
193
194 === 3.2 Verify Imports and Analyses ===
195
196 ...
197
198 === 3.3 Observe Usage and Reflect on Quality ===
199
200 ...
201
202 === 3.4 Gather Enhancements ===
203
204 ... on the web-app, on the creation process, and on the course

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