Runnable.

.gitignore

@@ -0,0 +1,4 @@
+# Created by venv; see https://docs.python.org/3/library/venv.html
+.idea
+.venv
+.DS_Store

README.md

@@ -14,10 +14,26 @@ tags:
 # AISOP-fundid-classifiers
 
 This is a series of spacy models for the classification tasks.
-It is part of the AISOP-domain-fundid https://gitlab.com/aisop/aisop-domain-fundid which is designed to serve for the [AISOP-webapp](https://gitlab.com/aisop/aisop-webapp).
+## Try it here
+
+- Install spaCy
+- `python python recognize.py l1-model l2-models "this is a text"` 
+
+...outputs the recognition in JSON e.g.:
+        {
+         "Algorithmization": {"score": 0.39, 
+           "subs": {"Algorithmization": 0.94, "Variable": 0.58, "programming concepts": 0.49}}, 
+         "Encryption": {"score": 0.26, 
+            "subs": {"Encryption": 0.66, "Symmetric key systems": 0.5, "Substitution ciphers": 0.44}}
+         }
+
+
+## Web-App Packaging
+
+This model is part of the AISOP-domain-fundid https://gitlab.com/aisop/aisop-domain-fundid which is designed to serve for the [AISOP-webapp](https://gitlab.com/aisop/aisop-webapp).
 
 The [python scripts](https://gitlab.com/aisop/aisop-webapp/-/tree/main/scripts/python?ref_type=heads) there use the models and the spaCy library to classify each "paragraph" of e-portfolios stored in HTML and even generate words using tesseract (if the picture is available) and annotate them too.
 
 The scripts enrich the HTML with `data-topic-*` attributes, indicating the presence of topics in the paragraphs.
 
-The scripts can be tested in the web-app in the `/debug/` road.
+The scripts can be tested in the web-app in the `/debug/` road.

recognize.py

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+# recognize: processes two-levels-topic-recognition
+# Authors: Paul Libbrecht, Pierre Günthner and Alexander Gantikow from the AISOP project
+
+# Installation: Install spacy then...
+# Usage: python recognize.py l1-model l2-models "this is a text"
+# l1-model path: a relative path (starting from this script) pointing to the level-1 model folder
+# l2-model path: a relative path (starting from this script) pointing to the folder containing a folder for each L2-label
+# "this is a text": the text to recognize
+
+
+import sys                       # System-specific parameters and functions, part of Py
+import spacy                     # Natural language processing
+from pathlib import Path         # Object-oriented filesystem paths, part of Py
+import json                      # JSON object dumping functions
+
+
+RoundTo = 2                      # Round to precision of n decimals
+Encoding = 'utf8'                # Encoding of the html file to be read and parsed via BeautifulSoup
+ScoreThreshold = 0.2             # Min. spacy probability value for an element to be analysed/enriched.
+MaxResults = 3                   # Max. number of concepts/labels added to an html element
+ParagraphMinLetters = 10         # Min. number of letters of paragraph to be considered in analysis
+ListMinLetters = 10              # Min. number of letters of <ul> and <ol> to be considered in analysis
+
+SubModels = {}
+
+Nlp = spacy.load(sys.argv[1])
+SubModelDir = Path(__file__).parent.joinpath(sys.argv[2]).absolute()
+input = sys.argv[3]
+
+
+def filterDoc(doc, scoreThreshold, maxResults, roundTo=2):
+    cats = doc.cats.items()
+    filt = list(filter(lambda c: c[1] > scoreThreshold, cats))
+    sort = sorted(filt, key=lambda c: c[1], reverse=True)
+    maxi = sort[0:maxResults]
+    rund = [(l[0], round(l[1], roundTo)) for l in maxi ]
+    return dict(rund)
+
+
+
+def recognize(text):
+    global Nlp
+    # find l1 labels
+    labels = filterDoc(Nlp(text), ScoreThreshold, MaxResults)
+
+    # find L2 labels
+    relabels = dict()
+    for label in labels.keys():
+        label2 = label.strip()
+        SubModelPath = SubModelDir.joinpath(label2).absolute()
+        if SubModelPath.exists():
+            Nlp = spacy.load(SubModelPath)
+            docSub = filterDoc(Nlp(text), ScoreThreshold, MaxResults)
+            relabels[label2] = {'score': labels[label], 'subs': docSub}
+        else:
+            relabels[label2] = {'score': labels[label]}
+            relabels["messages"] = "Submodel path \"" + str(SubModelPath) + "\" not found"
+
+    return relabels
+
+print(json.dumps(recognize(input)))