Fixup various CGI problems.

  * Rename cgi-bin to bin, to bypass default cgi-bin alias.
  * Change assignment to equality check in classifier.
  * Add .htaccess file to bin dir to allow CGI execution.
  * Point index.html form to bin.

7 files changed, 316 insertions, 314 deletions

diff --git a/bin/.htaccess b/bin/.htaccess
new file mode 100644
index 0000000..698afb4
--- /dev/null
+++ b/bin/.htaccess
@@ -0,0 +1,2 @@
+Options +ExecCGI
+SetHandler cgi-script
diff --git a/SVM_linear_aa_clf.pkl b/bin/SVM_linear_aa_clf.pkl
index 1afce0a..1afce0a 100644
--- a/SVM_linear_aa_clf.pkl
+++ b/bin/SVM_linear_aa_clf.pkl
diff --git a/UniqRepsGemys_6089_StSCALER.pkl b/bin/UniqRepsGemys_6089_StSCALER.pkl
index 3a098bd..3a098bd 100644
--- a/UniqRepsGemys_6089_StSCALER.pkl
+++ b/bin/UniqRepsGemys_6089_StSCALER.pkl
diff --git a/cgi-bin/classifier.py b/bin/classifier.py
index ecf7c15..0ae13b5 100755
--- a/cgi-bin/classifier.py
+++ b/bin/classifier.py
@@ -1,313 +1,313 @@
-#!/home/erik/bin/python3.6

-

-#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

-

-#----------------------------------------------\

-#  Parse the web-form information to variables  \

-#                                                \_______________________________________________________

-#																										 |

-cgitb.enable()

-form=cgi.FieldStorage()

-alignment = form.getvalue('fasta')

-if alignment.startswith(">"):		#naive check for FASTA format

-	list=alignment.split(">")

-	book={}

-	for a in list:

-		tempList=a.splitlines()

-		nameLine=tempList.pop(0)

-		name=nameLine.split(" ")[0]

-		seq="".join(tempList)

-		book[name]=seq

-	seqList=[]

-	lenList=[]

-	nameList=[]

-	for i in book:

-		nameList.append(i)

-		seqList.append(book[i])

-		lenList.append(str(len(book[i])))

-	

-	if len(seqList)=0:				#check for empty sequence list

-		seqList = ["MPSKKSGPQPHKRWVFTLNNPSEEEKNKIRELPISLFDYFVCGEEGLEEGRTAHLQGFANFAKKQTFNKVKWYFGARCHIEKAKGTDQQNKEYCSKEGHILIECGAPRNQGKRSDLSTAYFDYQQSGPPGMVLLNCCPSCRSSLSEDYYFAILEDCWRTINGGTRRPI"]

-		nameList=['demo']

-		lenList=[str(len(alignment[0]))]

-		

-else:

-	seqList = ["MPSKKSGPQPHKRWVFTLNNPSEEEKNKIRELPISLFDYFVCGEEGLEEGRTAHLQGFANFAKKQTFNKVKWYFGARCHIEKAKGTDQQNKEYCSKEGHILIECGAPRNQGKRSDLSTAYFDYQQSGPPGMVLLNCCPSCRSSLSEDYYFAILEDCWRTINGGTRRPI"]

-	nameList=['demo']

-	lenList=[str(len(alignment[0]))]

-

-#--------------------------------------------------------------------------------------------------------+	

-

-#----------------------------------------------\

-#  predict genus of input sequences             \

-#                                                \_______________________________________________________	

-#																										 |

-#list of amino acids as vocabulary for the CountVectorizer

-AAs=['a','c','d','e','f','g','h','i','k','l','m','n','p','q','r','s','t','v','w','y']

-

-#load the classifier and scaler

-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(seqList)

- 	

-#Scale the data to the training set

-X=StSc.transform(dataVect.astype("float64"))

-

-#make predictions for the original dataset

-predictions=clf.predict(X)

-	

-

-#----------------------------------------------\

-#  Build HTML table of results                  \

-#                                                \_______________________________________________________	

-# 																										 |

-results=""""""																									

-for k in len(seqList):

-	results+="""<tr><td>{0}</td><td>{1}</td><td>{2}</td></tr>""".format(nameList[k],lenList[k],predictions[k])

-if "demo" in nameList:

-	results+="""<p>There seems to have been an error.<br>If you are expecting more than one prediction or 

-	do not see the name you entered please try the submission form again, making sure that the input is in FASTA format."""

-

-#----------------------------------------------\

-#  Build output page                            \

-#                                                \_______________________________________________________	

-# 																										 |

-#build output page parts

-#Header and CSS Style bits

-header="""

-<!DOCTYPE html>

-

-<html>

-<head>

-<style>

-* {box-sizing: border-box}

-body {font-family: "Lato", sans-serif;}

-/* Style the tab */

-div.tab {

-    float: left;

-    border: 1px solid #ccc;

-    background-color: #f1f1f1;

-    width: 20%;

-    height: 250px;

-}

-/* Style the buttons inside the tab */

-div.tab button {

-    display: block;

-    background-color: inherit;

-    color: black;

-    padding: 22px 16px;

-    width: 100%;

-    border: none;

-    outline: none;

-    text-align: left;

-    cursor: pointer;

-    transition: 0.3s;

-    font-size: 17px;

-}

-/* Change background color of buttons on hover */

-div.tab button:hover {

-    background-color: #ddd;

-}

-/* Create an active/current "tab button" class */

-div.tab button.active {

-    background-color: #1acefc;

-}

-/* Style the tab content */

-.tabcontent {

-    float: left;

-    padding: 0px 12px;

-    border: 1px solid #ccc;

-    width: 80%;

-    min-height: 250px;

-}

-table {

-    border-collapse: collapse;

-    width: 80%;

-}

-

-th, td {

-    text-align: left;

-    padding: 8px;

-}

-

-tr:nth-child(even){background-color: #f2f2f2}

-

-th {

-    background-color: #ff0000;

-    color: white;

-}

-

-</style>

-</head>

-"""

-

-#Page contents, first part

-body1="""

-<body>

-

-<p>Welcome to CRESSdna.org</p>

-

-<div class="tab">

-  <button class="tablinks" onclick="openTab(event, 'Home')" id="defaultOpen">Home</button>

-  <button class="tablinks" onclick="openTab(event, 'Taxonomy')">Taxonomy</button>

-  <button class="tablinks" onclick="openTab(event, 'Contact')">Contact</button>

-  <button class="tablinks" onclick="openTab(event, 'Results')">Results</button>

- </div>

-

-<div id="Home" class="tabcontent">

-  <h3>Home</h3>

-  <p>Part of the <a href='http://www.nsf.gov/pubs/2010/nsf10513/nsf10513.htm'>National Science Foundation's Assembling the Tree of Life</a>.</p>

-    <img src='nsf1.jpg' alt='Sponsored with a Grant from the National Science Foundation'>

-</div>

-

-<div id="Taxonomy" class="tabcontent">

-  <h3>Taxonomy</h3>

-  <p>Please enter only one word as the name(no space) and only one Rep sequence</p>

-  <form action="./cgi-bin/classifier.py" method="post"><br>

-	<input type="text" name="seqname" value="seqID"><br>

-	<textarea rows="4" cols="50" name="fasta" input type="submit">

-Enter ONE Rep protein sequence here...</textarea>

-			<br>

-		<input type="reset">

-		<input type="submit">

-</form>	

-  		<p>

-  			<ul>

-	<li>This classifier requires Rep protein sequence to be:</li>

-		<ul>

-		<li>Complete</li>

-		<li>Unaligned</li>

-		<li>in FASTA format</li>

-		</ul>

-	<p>And has been trained on the following Genera:</p>

-		<li>Circoviridae</li>	

-			<ul>

-			<li>Circovirus</li>

-			<li>Cyclovirus</li>

-			</ul>

-		<li>Nanoviridae</li>

-			<ul>

-			<li>Babuvirus</li>

-			<li>Nanovirus</li>

-			</ul>

-		<li>Genomoviridae</li>	

-			<ul>

-			<li>Gemycircularvirus</li>

-			<li>Gemygorvirus</li>

-			<li>Gemykibivirus</li>

-			<li>Gemykolovirus</li>

-			<li>Gemykrogvirus</li>

-			<li>Gemyvongvirus</li>

-			</ul>

-		<li>Geminiviridae</li>

-			<ul>

-			<li>Becurtovirus</li>

-			<li>Begomovirus</li>

-			<li>Capulavirus</li>

-			<li>Curtovirus</li>

-			<li>Eragrovirus</li>

-			<li>Grablovirus</li>

-			<li>Mastrevirus</li>

-			<li>Turncurtovirus</li>

-			</ul>

-		<li>Smacovirus</li>

-</ul>  		</p>

-</div>

-<div id="Contact" class="tabcontent">

-  <h3>Contact</h3>

-  <p>Questions or comments? Send us an email:</p>

-	<p>email At domain Dot something</p>

-</div> 

-

-<div id="Results" class="tabcontent">

-  <h3>Results</h3>

-  <p>Results from Taxonomy prediction</p>

-  <table>

-  <tr>

-    <th>Sequence Name</th>

-    <th>Length</th>

-    <th>Prediction</th>

-  </tr>

-"""  

-

-#Page contents, second part (results fit between body1 and body2)

-body2="""

-</table>

-  <p>This classifier will return the best fit of the submitted sequence to the training data.<br>

-Currently included in the training data:<br>

-<li>Circoviridae</li>	

-			<ul>

-			<li>Circovirus</li>

-			<li>Cyclovirus</li>

-			</ul>

-		<li>Nanoviridae</li>

-			<ul>

-			<li>Babuvirus</li>

-			<li>Nanovirus</li>

-			</ul>

-		<li>Genomoviridae</li>	

-			<ul>

-			<li>Gemycircularvirus</li>

-			<li>Gemygorvirus</li>

-			<li>Gemykibivirus</li>

-			<li>Gemykolovirus</li>

-			<li>Gemykrogvirus</li>

-			<li>Gemyvongvirus</li>

-			</ul>

-		<li>Geminiviridae</li>

-			<ul>

-			<li>Becurtovirus</li>

-			<li>Begomovirus</li>

-			<li>Capulavirus</li>

-			<li>Curtovirus</li>

-			<li>Eragrovirus</li>

-			<li>Grablovirus</li>

-			<li>Mastrevirus</li>

-			<li>Turncurtovirus</li>

-			</ul>

-		<li>Smacovirus</li>

-<br><br>	

-</p>	

-</div> 

- 

-<script>

-function openTab(evt, tabTitle) {

-    var i, tabcontent, tablinks;

-    tabcontent = document.getElementsByClassName("tabcontent");

-    for (i = 0; i < tabcontent.length; i++) {

-        tabcontent[i].style.display = "none";

-    }

-    tablinks = document.getElementsByClassName("tablinks");

-    for (i = 0; i < tablinks.length; i++) {

-        tablinks[i].className = tablinks[i].className.replace(" active", "");

-    }

-    document.getElementById(tabTitle).style.display = "block";

-    evt.currentTarget.className += " active";

-}

-// Get the element with id="defaultOpen" and click on it

-document.getElementById("defaultOpen").click();

-</script>

-</body>

-"""

-

-#close the Page

-footer="""

-</html> 

-"""

-

-#build the output page

-page=header+body1+results+body2+footer

-	

-#send the output as html 	

-output = page.format()

-print (output)	

-

-quit()
\ No newline at end of file
+#!/home/erik/bin/python3.6
+
+#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
+
+#----------------------------------------------\
+#  Parse the web-form information to variables  \
+#                                                \_______________________________________________________
+#																										 |
+cgitb.enable()
+form=cgi.FieldStorage()
+alignment = form.getvalue('fasta')
+if alignment.startswith(">"):		#naive check for FASTA format
+	list=alignment.split(">")
+	book={}
+	for a in list:
+		tempList=a.splitlines()
+		nameLine=tempList.pop(0)
+		name=nameLine.split(" ")[0]
+		seq="".join(tempList)
+		book[name]=seq
+	seqList=[]
+	lenList=[]
+	nameList=[]
+	for i in book:
+		nameList.append(i)
+		seqList.append(book[i])
+		lenList.append(str(len(book[i])))
+	
+	if len(seqList)==0:				#check for empty sequence list
+		seqList = ["MPSKKSGPQPHKRWVFTLNNPSEEEKNKIRELPISLFDYFVCGEEGLEEGRTAHLQGFANFAKKQTFNKVKWYFGARCHIEKAKGTDQQNKEYCSKEGHILIECGAPRNQGKRSDLSTAYFDYQQSGPPGMVLLNCCPSCRSSLSEDYYFAILEDCWRTINGGTRRPI"]
+		nameList=['demo']
+		lenList=[str(len(alignment[0]))]
+		
+else:
+	seqList = ["MPSKKSGPQPHKRWVFTLNNPSEEEKNKIRELPISLFDYFVCGEEGLEEGRTAHLQGFANFAKKQTFNKVKWYFGARCHIEKAKGTDQQNKEYCSKEGHILIECGAPRNQGKRSDLSTAYFDYQQSGPPGMVLLNCCPSCRSSLSEDYYFAILEDCWRTINGGTRRPI"]
+	nameList=['demo']
+	lenList=[str(len(alignment[0]))]
+
+#--------------------------------------------------------------------------------------------------------+	
+
+#----------------------------------------------\
+#  predict genus of input sequences             \
+#                                                \_______________________________________________________	
+#																										 |
+#list of amino acids as vocabulary for the CountVectorizer
+AAs=['a','c','d','e','f','g','h','i','k','l','m','n','p','q','r','s','t','v','w','y']
+
+#load the classifier and scaler
+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(seqList)
+ 	
+#Scale the data to the training set
+X=StSc.transform(dataVect.astype("float64"))
+
+#make predictions for the original dataset
+predictions=clf.predict(X)
+	
+
+#----------------------------------------------\
+#  Build HTML table of results                  \
+#                                                \_______________________________________________________	
+# 																										 |
+results=""""""																									
+for k in len(seqList):
+	results+="""<tr><td>{0}</td><td>{1}</td><td>{2}</td></tr>""".format(nameList[k],lenList[k],predictions[k])
+if "demo" in nameList:
+	results+="""<p>There seems to have been an error.<br>If you are expecting more than one prediction or 
+	do not see the name you entered please try the submission form again, making sure that the input is in FASTA format."""
+
+#----------------------------------------------\
+#  Build output page                            \
+#                                                \_______________________________________________________	
+# 																										 |
+#build output page parts
+#Header and CSS Style bits
+header="""
+<!DOCTYPE html>
+
+<html>
+<head>
+<style>
+* {box-sizing: border-box}
+body {font-family: "Lato", sans-serif;}
+/* Style the tab */
+div.tab {
+    float: left;
+    border: 1px solid #ccc;
+    background-color: #f1f1f1;
+    width: 20%;
+    height: 250px;
+}
+/* Style the buttons inside the tab */
+div.tab button {
+    display: block;
+    background-color: inherit;
+    color: black;
+    padding: 22px 16px;
+    width: 100%;
+    border: none;
+    outline: none;
+    text-align: left;
+    cursor: pointer;
+    transition: 0.3s;
+    font-size: 17px;
+}
+/* Change background color of buttons on hover */
+div.tab button:hover {
+    background-color: #ddd;
+}
+/* Create an active/current "tab button" class */
+div.tab button.active {
+    background-color: #1acefc;
+}
+/* Style the tab content */
+.tabcontent {
+    float: left;
+    padding: 0px 12px;
+    border: 1px solid #ccc;
+    width: 80%;
+    min-height: 250px;
+}
+table {
+    border-collapse: collapse;
+    width: 80%;
+}
+
+th, td {
+    text-align: left;
+    padding: 8px;
+}
+
+tr:nth-child(even){background-color: #f2f2f2}
+
+th {
+    background-color: #ff0000;
+    color: white;
+}
+
+</style>
+</head>
+"""
+
+#Page contents, first part
+body1="""
+<body>
+
+<p>Welcome to CRESSdna.org</p>
+
+<div class="tab">
+  <button class="tablinks" onclick="openTab(event, 'Home')" >Home</button>
+  <button class="tablinks" onclick="openTab(event, 'Taxonomy')">Taxonomy</button>
+  <button class="tablinks" onclick="openTab(event, 'Contact')">Contact</button>
+  <button class="tablinks" onclick="openTab(event, 'Results')"id="defaultOpen">Results</button>
+ </div>
+
+<div id="Home" class="tabcontent">
+  <h3>Home</h3>
+  <p>Part of the <a href='http://www.nsf.gov/pubs/2010/nsf10513/nsf10513.htm'>National Science Foundation's Assembling the Tree of Life</a>.</p>
+    <img src='nsf1.jpg' alt='Sponsored with a Grant from the National Science Foundation'>
+</div>
+
+<div id="Taxonomy" class="tabcontent">
+  <h3>Taxonomy</h3>
+  <p>Please enter only one word as the name(no space) and only one Rep sequence</p>
+  <form action="./cgi-bin/classifier.py" method="post"><br>
+	<input type="text" name="seqname" value="seqID"><br>
+	<textarea rows="4" cols="50" name="fasta" input type="submit">
+Enter ONE Rep protein sequence here...</textarea>
+			<br>
+		<input type="reset">
+		<input type="submit">
+</form>	
+  		<p>
+  			<ul>
+	<li>This classifier requires Rep protein sequence to be:</li>
+		<ul>
+		<li>Complete</li>
+		<li>Unaligned</li>
+		<li>in FASTA format</li>
+		</ul>
+	<p>And has been trained on the following Genera:</p>
+		<li>Circoviridae</li>	
+			<ul>
+			<li>Circovirus</li>
+			<li>Cyclovirus</li>
+			</ul>
+		<li>Nanoviridae</li>
+			<ul>
+			<li>Babuvirus</li>
+			<li>Nanovirus</li>
+			</ul>
+		<li>Genomoviridae</li>	
+			<ul>
+			<li>Gemycircularvirus</li>
+			<li>Gemygorvirus</li>
+			<li>Gemykibivirus</li>
+			<li>Gemykolovirus</li>
+			<li>Gemykrogvirus</li>
+			<li>Gemyvongvirus</li>
+			</ul>
+		<li>Geminiviridae</li>
+			<ul>
+			<li>Becurtovirus</li>
+			<li>Begomovirus</li>
+			<li>Capulavirus</li>
+			<li>Curtovirus</li>
+			<li>Eragrovirus</li>
+			<li>Grablovirus</li>
+			<li>Mastrevirus</li>
+			<li>Turncurtovirus</li>
+			</ul>
+		<li>Smacovirus</li>
+</ul>  		</p>
+</div>
+<div id="Contact" class="tabcontent">
+  <h3>Contact</h3>
+  <p>Questions or comments? Send us an email:</p>
+	<p>email At domain Dot something</p>
+</div> 
+
+<div id="Results" class="tabcontent">
+  <h3>Results</h3>
+  <p>Results from Taxonomy prediction</p>
+  <table>
+  <tr>
+    <th>Sequence Name</th>
+    <th>Length</th>
+    <th>Prediction</th>
+  </tr>
+"""  
+
+#Page contents, second part (results fit between body1 and body2)
+body2="""
+</table>
+  <p>This classifier will return the best fit of the submitted sequence to the training data.<br>
+Currently included in the training data:<br>
+<li>Circoviridae</li>	
+			<ul>
+			<li>Circovirus</li>
+			<li>Cyclovirus</li>
+			</ul>
+		<li>Nanoviridae</li>
+			<ul>
+			<li>Babuvirus</li>
+			<li>Nanovirus</li>
+			</ul>
+		<li>Genomoviridae</li>	
+			<ul>
+			<li>Gemycircularvirus</li>
+			<li>Gemygorvirus</li>
+			<li>Gemykibivirus</li>
+			<li>Gemykolovirus</li>
+			<li>Gemykrogvirus</li>
+			<li>Gemyvongvirus</li>
+			</ul>
+		<li>Geminiviridae</li>
+			<ul>
+			<li>Becurtovirus</li>
+			<li>Begomovirus</li>
+			<li>Capulavirus</li>
+			<li>Curtovirus</li>
+			<li>Eragrovirus</li>
+			<li>Grablovirus</li>
+			<li>Mastrevirus</li>
+			<li>Turncurtovirus</li>
+			</ul>
+		<li>Smacovirus</li>
+<br><br>	
+</p>	
+</div> 
+ 
+<script>
+function openTab(evt, tabTitle) {
+    var i, tabcontent, tablinks;
+    tabcontent = document.getElementsByClassName("tabcontent");
+    for (i = 0; i < tabcontent.length; i++) {
+        tabcontent[i].style.display = "none";
+    }
+    tablinks = document.getElementsByClassName("tablinks");
+    for (i = 0; i < tablinks.length; i++) {
+        tablinks[i].className = tablinks[i].className.replace(" active", "");
+    }
+    document.getElementById(tabTitle).style.display = "block";
+    evt.currentTarget.className += " active";
+}
+// Get the element with id="defaultOpen" and click on it
+document.getElementById("defaultOpen").click();
+</script>
+</body>
+"""
+
+#close the Page
+footer="""
+</html> 
+"""
+
+#build the output page
+page=header+body1+results+body2+footer
+	
+#send the output as html 	
+output = page.format()
+print (output)	
+
+quit()
diff --git a/cgi-bin/SVM_linear_aa_clf.pkl b/cgi-bin/SVM_linear_aa_clf.pkl
deleted file mode 100644
index 1afce0a..0000000
--- a/cgi-bin/SVM_linear_aa_clf.pkl
+++ /dev/null
diff --git a/cgi-bin/UniqRepsGemys_6089_StSCALER.pkl b/cgi-bin/UniqRepsGemys_6089_StSCALER.pkl
deleted file mode 100644
index 3a098bd..0000000
--- a/cgi-bin/UniqRepsGemys_6089_StSCALER.pkl
+++ /dev/null
diff --git a/index.html b/index.html
index b90789a..6fbf4a8 100644
--- a/index.html
+++ b/index.html
@@ -64,7 +64,7 @@ div.tab button.active {
 
 <div id="Taxonomy" class="tabcontent">
   <h3>Taxonomy</h3>
-  <form action="./cgi-bin/classifier.py" method="post"><br>
+  <form action="bin/classifier.py" method="post"><br>
 	<textarea rows="4" cols="50" name="fasta" input type="submit">
 >Demo
 MPSKKSGPQPHKRWVFTLNNPSEEEKNKIRELPISLFDYFVCGEEGLEEGRTAHLQGFANFAKKQTFNKVKWYFGARCHIEKAKGTDQQNKEYCSKEGHILIECGAPRNQGKRSDLSTAYFDYQQSGPPGMVLLNCCPSCRSSLSEDYYFAILEDCWRTINGGTRRPI</textarea>