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diff --git a/cgi-bin/classifier.py b/cgi-bin/classifier.py
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+#!/usr/bin/python
+
+#import packages to be used
+from sklearn.svm import SVC
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.preprocessing import StandardScaler
+from sklearn.externals import joblib
+import cgi, cgitb
+
+cgitb.enable()
+form=cgi.FieldStorage()
+if form.getvalue('fasta'):
+	alignment = form.getvalue('fasta')
+	alignment=[alignment]
+	name=form.getvalue('seqname')
+	size=len(alignment[0])
+else:
+	alignment = ["MPSKKSGPQPHKRWVFTLNNPSEEEKNKIRELPISLFDYFVCGEEGLEEGRTAHLQGFANFAKKQTFNKVKWYFGARCHIEKAKGTDQQNKEYCSKEGHILIECGAPRNQGKRSDLSTAYFDYQQSGPPGMVLLNCCPSCRSSLSEDYYFAILEDCWRTINGGTRRPI"]
+	name='demo'
+	size=len(alignment[0])
+	
+html = open("./www.html/CRESSresults.html")
+page=html.read()
+
+
+AAs=['a','c','d','e','f','g','h','i','k','l','m','n','p','q','r','s','t','v','w','y']
+clf=joblib.load("./cgi-bin/SVM_linear_aa_clf.pkl")
+StSc=joblib.load("./cgi-bin/UniqRepsGemys_6089_StSCALER.pkl")
+cv=CountVectorizer(analyzer='char',ngram_range=(1,1),vocabulary=AAs)
+
+
+#initialize text data vectorizer
+
+dataVect=cv.transform(alignment)
+ 	
+#Scale the data to the training set
+X=StSc.transform(dataVect.astype("float64"))
+
+#make predictions for the original dataset
+results=",".join([name,clf.predict(X)[0]])
+results=",".join([results,str(size)])
+#for i in results:
+	#print(i[0],"\t",i[1])
+
+output = page.format(prediction=results)
+"""f=open('test.html','w')
+f.write(output)
+f.close()"""
+print (output)	
+
+	
+quit()
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