Removed all libraries

iris/Dockerfile

@@ -17,10 +17,6 @@ RUN chmod +x /webapp.xml
 
 RUN pip3 install -r /requirements.txt --break-system-packages
 
-RUN spacy download es_dep_news_trf --break-system-packages
-
-RUN spacy download en_core_web_md --break-system-packages
-
 USER irisowner
 
 # copy files to image

requirements.txt

@@ -1,4 +1,3 @@
 sentence-transformers
 numpy
-pandas
-spacy
+pandas

src/ENCODER/BP/AnalyzeTextProcess.cls

@@ -30,13 +30,12 @@ Method AnalyzeText(text As %String, analysisId As %String, language As %String)
         model = sentence_transformers.SentenceTransformer('/iris-shared/model/')
         phrases = analyzedText['response'].replace('"',"").split(",")
         sqlsentence = ""
-        iris.cls("Ens.Util.Log").LogInfo("ENCODER.BP.AnalyzeTextProcess", "AnalyzeText", "Starting process")
+        # iris.cls("Ens.Util.Log").LogInfo("ENCODER.BP.AnalyzeTextProcess", "AnalyzeText", "Starting process")
         for phraseToAnalyze in phrases :
             if phraseToAnalyze != "":
                 embedding = model.encode(phraseToAnalyze, normalize_embeddings=True).tolist()
                 sqlsentence = "INSERT INTO ENCODER_Object.TextMatches (CodeId, Description, Similarity, AnalysisId, RawText) SELECT TOP 50 * FROM (SELECT CodeId, Description, VECTOR_DOT_PRODUCT(VectorDescription, TO_VECTOR('"+str(embedding)+"', DECIMAL)) AS Similarity, '"+analysisId+"', '"+phraseToAnalyze+"' FROM ENCODER_Object.Codes) ORDER BY Similarity DESC"                    
                 iris.cls("ENCODER.Utils.Manager").ExecuteInsertQuery(sqlsentence)
-                iris.cls("Ens.Util.Log").LogInfo("ENCODER.BP.AnalyzeTextProcess", "AnalyzeText", sqlsentence)                    
     except Exception as err:
         iris.cls("Ens.Util.Log").LogInfo("ENCODER.BP.AnalyzeTextProcess", "AnalyzeText", repr(err))
         return repr(err)

src/ENCODER/Utils/Manager.cls

@@ -39,17 +39,4 @@ ClassMethod DownloadModel() As %String [ Language = python ]
     return "Downloaded"
 }
 
-ClassMethod LematizeText(textToLematize As %String) As %String [ Language = python ]
-{
-    import spacy
-
-    nlp = spacy.load('es_dep_news_trf')
-
-    doc = nlp(textToLematize)
-    words = [t.orth_ for t in doc if not t.is_punct | t.is_stop]
-    lexical_tokens = [t.lower() for t in words if t.isalpha()]
-
-    return " ".join(lexical_tokens)
-}
-
 }