new batch mode with single calls

7 files changed, 557 insertions, 261 deletions

diff --git a/application.rb b/application.rb
index 70eecb5..a4f632a 100644
--- a/application.rb
+++ b/application.rb
@@ -71,10 +71,173 @@ get '/predict/dataset/:name' do
   csv
 end
 
-get '/predict/:tmppath/:filename/?' do
+get '/predict/:tmppath/:model/:filename?' do
   response['Content-Type'] = "text/csv"
-  path = "/tmp/#{params[:tmppath]}"
-  send_file path, :filename => "lazar_batch_prediction_#{params[:filename]}", :type => "text/csv", :disposition => "attachment"
+  path = File.join("tmp", params[:tmppath])
+  `sort -gk1 #{path} -o #{path}`
+
+  send_file path, :filename => "#{Time.now.strftime("%Y-%m-%d")}_lazar_batch_prediction_#{params[:model]}_#{params[:filename]}", :type => "text/csv", :disposition => "attachment"
+end
+
+get '/batch/:model/' do
+
+  if params[:model] == "Cramer"
+    dataset = Dataset.find params[:dataset]
+    compounds = dataset.compounds.collect{|c| c.smiles}
+    
+    prediction = [Toxtree.predict(compounds, "Cramer rules"), Toxtree.predict(compounds, "Cramer rules with extensions")]
+    output = {}
+    output["model_name"] = "Oral toxicity (Cramer rules)"
+    output["model_type"] = false
+    output["model_unit"] = false
+    ["measurements", "converted_measurements", "prediction_value", "converted_value", "interval", "converted_interval", "probability", "db_hit", "warnings", "info", "toxtree", "sa_prediction", "sa_matches", "confidence"].each do |key|
+      output["#{key}"] = false
+    end
+    output["toxtree"] = true
+    output["cramer_rules"] = prediction.collect{|array| array.collect{|hash| hash["Cramer rules"]}}.flatten.compact
+    output["cramer_rules_extensions"] = prediction.collect{|array| array.collect{|hash| hash["Cramer rules, with extensions"]}}.flatten.compact
+
+    # td paths to insert results in GUI
+    compound_ids = dataset.compounds.collect{|c| c.id}
+    output["tds"] = compound_ids.each_with_index.map{|cid,idx| "prediction_#{cid}_Cramer_#{idx}"}
+    
+    # write to file
+    # header
+    csv = "ID,Endpoint,Unique SMILES,Cramer rules,Cramer rules with extensions\n"
+    
+    compounds.each_with_index do |smiles, idx|
+      csv << "#{idx+1},#{output["model_name"]},#{smiles},"\
+        "#{output["cramer_rules"][idx] != "nil" ? output["cramer_rules"][idx] : "none" },"\
+        "#{output["cramer_rules_extensions"][idx] != "nil" ? output["cramer_rules_extensions"][idx] : "none"}\n"
+    end
+    File.open(File.join("tmp", params[:tmppath]),"a+"){|file| file.write(csv)}
+
+    # cleanup
+    dataset.delete
+
+    # return output
+    response['Content-Type'] = "application/json"
+    return JSON.pretty_generate output
+
+  else
+    idx = params[:idx].to_i
+    compound = Compound.find params[:compound]
+
+    model = Model::Validation.find params[:model]
+    prediction = model.predict(compound)
+    output = {}
+    output["model_name"] = "#{model.endpoint.gsub('_', ' ')} (#{model.species})"
+    output["model_type"] = model.model.class.to_s.match("Classification") ? type = "Classification" : type = "Regression"
+    output["model_unit"] = (type == "Regression") ? "(#{model.unit})" : ""
+    output["converted_model_unit"] = (type == "Regression") ? "#{model.unit =~ /\b(mmol\/L)\b/ ? "(mg/L)" : "(mg/kg_bw/day)"}" : ""
+    ["measurements", "converted_measurements", "prediction_value", "converted_value", "interval", "converted_interval", "probability", "db_hit", "warnings", "info", "toxtree", "sa_prediction", "sa_matches", "confidence"].each do |key|
+      output["#{key}"] = false
+    end
+
+    if prediction[:value]
+      inApp = prediction[:neighbors] ? "yes" : "no"
+      inT = prediction[:info] =~ /\b(identical)\b/i ? "yes" : "no"
+      if prediction[:info] =~ /\b(identical)\b/i
+        prediction[:info] = "This compound was part of the training dataset. All information "\
+          "from this compound was removed from the training data before the "\
+          "prediction, to obtain unbiased results."
+      end
+      note = "\"#{prediction[:warnings].uniq.join(" ")}" + ( prediction[:info] ? "#{prediction[:info]}\"" : "\"" )
+
+      output["prediction_value"] = (type == "Regression") ? "#{prediction[:value].delog10.signif(3)}" : "#{prediction[:value]}"
+      output["converted_value"] = "#{compound.mmol_to_mg(prediction[:value].delog10).signif(3)}" if type == "Regression"
+
+      output["db_hit"] = prediction[:info] if prediction[:info]
+      
+      if prediction[:measurements].is_a?(Array)
+        output["measurements"] = (type == "Regression") ? prediction[:measurements].collect{|value| "#{value.delog10.signif(3)} (#{model.unit})"} : prediction[:measurements].collect{|value| "#{value}"}
+        output["converted_measurements"] = (type == "Regression") ? prediction[:measurements].collect{|value| "#{compound.mmol_to_mg(value.delog10).signif(3)} #{model.unit =~ /mmol\/L/ ? "(mg/L)" : "(mg/kg_bw/day)"}"} : false
+      else
+        output["measurements"] = (type == "Regression") ? "#{prediction[:measurements].delog10.signif(3)} (#{model.unit})}" : "#{prediction[:measurements]}"
+        output["converted_measurements"] = (type == "Regression") ? "#{compound.mmol_to_mg(prediction[:measurements].delog10).signif(3)} #{(model.unit =~ /\b(mmol\/L)\b/) ? "(mg/L)" : "(mg/kg_bw/day)"}" : false
+
+      end #db_hit
+
+      if type == "Regression"
+
+        if !prediction[:prediction_interval].nil?
+          interval = prediction[:prediction_interval]
+          output['interval'] = "#{interval[1].delog10.signif(3)} - #{interval[0].delog10.signif(3)}"
+          output['converted_interval'] = "#{compound.mmol_to_mg(interval[1].delog10).signif(3)} - #{compound.mmol_to_mg(interval[0].delog10).signif(3)}"
+        end #prediction interval
+
+        csv = "#{idx+1},#{output['model_name']},#{output['model_type']},#{compound.smiles},"\
+          "#{output['prediction_value'] != false ? output['prediction_value'] : "-"},"\
+          "#{output['converted_value'] != false ? output['converted_value'] : "-"},"\
+          "#{output['interval'].split(" - ").first.strip unless output['interval'] == false},"\
+          "#{output['interval'].split(" - ").last.strip unless output['interval'] == false},"\
+          "#{output['converted_interval'].split(" - ").first.strip unless output['converted_interval'] == false},"\
+          "#{output['converted_interval'].split(" - ").last.strip unless output['converted_interval'] == false},"\
+          "#{inApp},#{inT},#{note.nil? ? "" : note.chomp}\n"
+      else # Classification
+
+        # consensus mutagenicity
+          
+        sa_prediction = KaziusAlerts.predict(compound.smiles)
+        lazar_mutagenicity = prediction
+        confidence = 0
+        lazar_mutagenicity_val = (lazar_mutagenicity[:value] == "non-mutagenic" ? false : true)
+        if sa_prediction[:prediction] == false && lazar_mutagenicity_val == false
+          confidence = 0.85
+        elsif sa_prediction[:prediction] == true && lazar_mutagenicity_val == true
+          confidence = 0.85 * ( 1 - sa_prediction[:error_product] )
+        elsif sa_prediction[:prediction] == false && lazar_mutagenicity_val == true
+          confidence = 0.11
+        elsif sa_prediction[:prediction] == true && lazar_mutagenicity_val == false
+          confidence = ( 1 - sa_prediction[:error_product] ) - 0.57
+        end
+        output["sa_prediction"] = sa_prediction
+        output["sa_matches"] = sa_prediction[:matches].flatten.first unless sa_prediction[:matches].blank?
+        output["confidence"] = confidence.signif(3)
+        output["model_name"] = "Lazar #{model.endpoint.gsub('_', ' ').downcase} (#{model.species}):"
+        output["probability"] = prediction[:probabilities] ? prediction[:probabilities].collect{|k,v| "#{k}: #{v.signif(3)}"} : false
+
+        csv = "#{idx+1},Consensus mutagenicity,#{compound.smiles},"\
+          "#{output['sa_prediction']['prediction'] == false ? "non-mutagenic" : "mutagenic"},"\
+          "#{output['confidence']},#{output['sa_matches'] != false ? "\"#{output['sa_matches']}\"" : "none"}, ,"\
+          "#{output['model_type']},#{output['prediction_value']},"\
+          "#{output['probability'][0] != false ? output['probability'][0].split(":").last : ""},"\
+          "#{output['probability'][1] != false ? output['probability'][1].split(":").last : ""},"\
+          "#{inApp},#{inT},#{note.nil? ? "" : note}\n"
+
+      end
+      
+      output["warnings"] = prediction[:warnings] if prediction[:warnings]
+
+    else #no prediction value
+      inApp = "no"
+      inT = prediction[:info] =~ /\b(identical)\b/i ? "yes" : "no"
+      if prediction[:info] =~ /\b(identical)\b/i
+        prediction[:info] = "This compound was part of the training dataset. All information "\
+          "from this compound was removed from the training data before the "\
+          "prediction, to obtain unbiased results."
+      end
+      note = "\"#{prediction[:warnings].join(" ")}\"" + ( prediction[:info] ? "\"#{prediction[:info]}\"" : "" )
+
+      output["warnings"] = prediction[:warnings]
+      output["info"] = prediction[:info] if prediction[:info]
+
+      if type == "Regression"
+        csv = "#{idx+1},#{output['model_name']},#{output['model_type']},#{compound.smiles},,,,,,,"+ [inApp,inT,note].join(",")+"\n"
+      else
+        csv = "#{idx+1},Consensus mutagenicity,#{compound.smiles},,,,,#{output['model_type']},,,,"+ [inApp,inT,note].join(",")+"\n"
+      end
+
+    end #prediction value
+
+    # write to file
+    File.open(File.join("tmp", params[:tmppath]),"a"){|file| file.write(csv)}
+
+    # return output
+    response['Content-Type'] = "application/json"
+    return JSON.pretty_generate output
+
+  end# if Cramer
 end
 
 post '/predict/?' do
@@ -90,152 +253,56 @@ post '/predict/?' do
     @filename = params[:fileselect][:filename]
     begin
       input = Dataset.from_csv_file File.join("tmp", params[:fileselect][:filename]), true
+      $logger.debug "save dataset #{params[:fileselect][:filename]}"
       if input.class == OpenTox::Dataset
-        dataset = Dataset.find input
+        @dataset = Dataset.find input
+        @compounds = @dataset.compounds
       else
         bad_request_error "Could not serialize file '#{@filename}'."
       end
     rescue
       bad_request_error "Could not serialize file '#{@filename}'."
     end
-    @compounds = dataset.compounds
+
     if @compounds.size == 0
       message = dataset[:warnings]
-      dataset.delete
+      @dataset.delete
       bad_request_error message
     end
-
-    # for csv export
-    @batch = {}
-    # for haml table
-    @view = {}
-
-    @compounds.each{|c| @view[c] = []}
-    params[:selection].keys.each do |model_id|
-      model = Model::Validation.find model_id
-      @batch[model] = []
-      @compounds.each_with_index do |compound,idx|
-        prediction = model.predict(compound)
-        @batch[model] << [compound, prediction]
-        @view[compound] << [model,prediction]
-      end
-    end
-
-    @csvhash = {}
-    @warnings = dataset[:warnings]
-    dupEntries = {}
-    delEntries = ""
     
-    # split duplicates and deleted entries
-    @warnings.each do |w|
-      substring = w.match(/line .* of/)
-      unless substring.nil?
-        delEntries += "\"#{w.sub(/\b(tmp\/)\b/,"")}\"\n"
+    @models = params[:selection].keys
+    @tmppaths = {}
+    @models.each do |model|
+      m = Model::Validation.find model
+      type = (m.regression? ? "Regression" : "Classification") unless model == "Cramer"
+      # add header for regression
+      if type == "Regression"
+        unit = (type == "Regression") ? "(#{m.unit})" : ""
+        converted_unit = (type == "Regression") ? "#{m.unit =~ /\b(mmol\/L)\b/ ? "(mg/L)" : "(mg/kg_bw/day)"}" : ""
+        header = "ID,Endpoint,Type,Unique SMILES,Prediction #{unit},Prediction #{converted_unit},"\
+          "Interval Low #{unit},Interval High #{unit},Interval Low #{converted_unit},Interval High #{converted_unit},"\
+          "inApplicabilityDomain,inTrainningSet,Note\n"
       end
-      substring = w.match(/rows .* Entries/)
-      unless substring.nil?
-        lines = []
-        substring[0].split(",").each{|s| lines << s[/\d+/]}
-        lines.shift
-        lines.each{|l| dupEntries[l.to_i] = w.split(".").first}
+      # add header for classification
+      if type == "Classification"
+        av = m.prediction_feature.accept_values
+        header = "ID,Endpoint,Unique SMILES,Structural alerts prediction,Structural alerts confidence,"\
+          "Structural alerts for mutagenicity,Lazar mutagenicity (Salmonella typhimurium),Type,Prediction,"\
+          "predProbability #{av[0]},predProbability #{av[1]},inApplicabilityDomain,inTrainningSet,Note\n"
       end
+      path = File.join("tmp", "#{Time.now.strftime("%Y-%m-%d")}_#{SecureRandom.urlsafe_base64(5)}")
+      File.open(path, "w"){|f| f.write(header) if header}
+      @tmppaths[model] = path.split("/").last
     end
 
-    @batch.each_with_index do |hash, idx|
-      @csvhash[idx] = ""
-      model = hash[0]
-      # create header
-      if model.regression?
-        predAunit = "(#{model.unit})"
-        predBunit = "(#{model.unit =~ /mmol\/L/ ? "(mol/L)" : "(mg/kg_bw/day)"})"
-        @csvhash[idx] = "\"ID\",\"Endpoint\",\"Type\",\"Unique SMILES\",\"Prediction #{predAunit}\",\"Prediction #{predBunit}\",\"95% Prediction interval (low) #{predAunit}\",\"95% Prediction interval (high) #{predAunit}\",\"95% Prediction interval (low) #{predBunit}\",\"95% Prediction interval (high) #{predBunit}\",\"inApplicabilityDomain\",\"inTrainningSet\",\"Note\"\n"
-      else #classification
-        av = model.prediction_feature.accept_values
-        probFirst = av[0].capitalize
-        probLast = av[1].capitalize
-        @csvhash[idx] = "\"ID\",\"Endpoint\",\"Type\",\"Unique SMILES\",\"Prediction\",\"predProbability#{probFirst}\",\"predProbability#{probLast}\",\"inApplicabilityDomain\",\"inTrainningSet\",\"Note\"\n"
-      end
-      values = hash[1]
-      dupEntries.keys.each{|k| values.insert(k-1, dupEntries[k])}.compact!
-      
-      values.each_with_index do |array, id|
-        type = (model.regression? ? "Regression" : "Classification")
-        endpoint = "#{model.endpoint.gsub('_', ' ')} (#{model.species})"
-      
-        if id == 0
-          @csvhash[idx] += delEntries unless delEntries.blank?
-        end
-        unless array.kind_of? String
-          compound = array[0]
-          prediction = array[1]
-          smiles = compound.smiles
-          
-          if prediction[:neighbors]
-            if prediction[:value]
-              pred = prediction[:value].numeric? ? "#{prediction[:value].delog10.signif(3)}" : prediction[:value]
-              predA = prediction[:value].numeric? ? "#{prediction[:value].delog10.signif(3)}" : prediction[:value]
-              predAunit = prediction[:value].numeric? ? "(#{model.unit})" : ""
-              predB = prediction[:value].numeric? ? "#{compound.mmol_to_mg(prediction[:value].delog10).signif(3)}" : prediction[:value]
-              predBunit = prediction[:value].numeric? ? "#{model.unit =~ /\b(mmol\/L)\b/ ? "(mg/L)" : "(mg/kg_bw/day)"}" : ""
-              int = (prediction[:prediction_interval].nil? ? nil : prediction[:prediction_interval])
-              intervalLow = (int.nil? ? "" : "#{int[1].delog10.signif(3)}")
-              intervalHigh = (int.nil? ? "" : "#{int[0].delog10.signif(3)}")
-              intervalLowMg = (int.nil? ? "" : "#{compound.mmol_to_mg(int[1].delog10).signif(3)}")
-              intervalHighMg = (int.nil? ? "" : "#{compound.mmol_to_mg(int[0].delog10).signif(3)}")
-              inApp = "yes"
-              inT = prediction[:info] =~ /\b(identical)\b/i ? "yes" : "no"
-              note = prediction[:warnings].join("\n") + ( prediction[:info] ? prediction[:info].sub(/\'.*\'/,"") : "\n" )
-              
-              unless prediction[:probabilities].nil?
-                av = model.prediction_feature.accept_values
-                propA = "#{prediction[:probabilities][av[0]].to_f.signif(3)}"
-                propB = "#{prediction[:probabilities][av[1]].to_f.signif(3)}"
-              end
-            else
-              # no prediction value only one neighbor
-              inApp = "no"
-              inT = prediction[:info] =~ /\b(identical)\b/i ? "yes" : "no"
-              note = prediction[:warnings].join("\n") + ( prediction[:info] ? prediction[:info].sub(/\'.*\'/,"") : "\n" )
-            end
-          else
-            # no prediction value
-            inApp = "no"
-            inT = prediction[:info] =~ /\b(identical)\b/i ? "yes" : "no"
-            note = prediction[:warnings].join("\n") + ( prediction[:info] ? prediction[:info].sub(/\'.*\'/,"") : "\n" )
-          end
-          if @warnings
-            @warnings.each do |w|
-              note += (w.split(".").first + ".") if /\b(#{Regexp.escape(smiles)})\b/ === w
-            end
-          end
-        else
-          # string note for duplicates
-          endpoint = type = smiles = pred = predA = predB = propA = propB = intervalLow = intervalHigh = intervalLowMg = intervalHighMg = inApp = inT = ""
-          note = array
-        end
-        if model.regression?
-          @csvhash[idx] += "\"#{id+1}\",\"#{endpoint}\",\"#{type}\",\"#{smiles}\",\"#{predA}\",\"#{predB}\",\"#{intervalLow}\",\"#{intervalHigh}\",\"#{intervalLowMg}\",\"#{intervalHighMg}\",\"#{inApp}\",\"#{inT}\",\"#{note.chomp}\"\n"
-        else
-          @csvhash[idx] += "\"#{id+1}\",\"#{endpoint}\",\"#{type}\",\"#{smiles}\",\"#{pred}\",\"#{propA}\",\"#{propB}\",\"#{inApp}\",\"#{inT}\",\"#{note.chomp}\"\n"
-        end
-      end
-    end
-    t = Tempfile.new
-    @csvhash.each do |model, csv|
-      t.write(csv)
-      t.write("\n")
-    end
-    t.rewind
-    @tmppath = t.path.split("/").last
-
-    dataset.delete
     File.delete File.join("tmp", params[:fileselect][:filename])
     return haml :batch
   end
 
+  # single compound prediction
   # validate identifier input
   if !params[:identifier].blank?
-    @identifier = params[:identifier]
+    @identifier = params[:identifier].strip
     $logger.debug "input:#{@identifier}"
     # get compound from SMILES
     @compound = Compound.from_smiles @identifier
diff --git a/tmp/.gitignore b/tmp/.gitignore
new file mode 100644
index 0000000..d6b7ef3
--- /dev/null
+++ b/tmp/.gitignore
@@ -0,0 +1,2 @@
+*
+!.gitignore
diff --git a/unicorn.rb b/unicorn.rb
index 010ebbb..e60b2bd 100644
--- a/unicorn.rb
+++ b/unicorn.rb
@@ -1,7 +1,3 @@
-#worker_processes 4
+worker_processes 4
 timeout 6000
 listen 8088
-log_dir = "#{ENV['HOME']}"
-log_file = File.join log_dir, "lazar.log"
-stderr_path log_file
-stdout_path log_file
diff --git a/views/batch.haml b/views/batch.haml
index 0e7efc7..aaa09bf 100644
--- a/views/batch.haml
+++ b/views/batch.haml
@@ -1,10 +1,273 @@
+:javascript
+  function progress(model,idx,total) {
+    var p = 100/total;
+    var percent = Math.round((idx+1)*p);
+    var bar = document.getElementById("bar_"+model);
+    var prog = document.getElementById("progress_"+model);
+    bar.style.width = percent + '%';
+    if (percent == 100){
+      prog.style.display = "none";
+    };
+  }
+
+  var HttpClient = function() {
+    this.get = function(aUrl, aCallback) {
+    var anHttpRequest = new XMLHttpRequest();
+    anHttpRequest.onreadystatechange = function() {
+    if (anHttpRequest.readyState == 4 && anHttpRequest.status == 200)
+      aCallback(anHttpRequest.responseText);
+    }
+      anHttpRequest.open( "GET", aUrl, true );
+      anHttpRequest.send( null );
+    }
+  };  
+
+  function renderResponse(model,idx,compound,last,total,tmppath) {
+    // create request
+    if (model == "Cramer"){
+      var dataset = '#{@dataset.id}';
+      var uri = "#{to("/batch/")}" + model + '/?dataset=' + escape(dataset) + '&tmppath=' + tmppath + '&last=' + last;
+    } else {
+      var dataset = '#{@dataset.id}';
+      var uri = "#{to("/batch/")}" + model + '/?compound=' + escape(compound) + '&dataset=' + dataset + '&idx=' + idx + '&tmppath=' + tmppath + '&last=' + last;
+    };
+    var aClient = new HttpClient();
+    aClient.get(uri, function(res) {
+      // progress bar function
+      progress(model,idx,total);
+
+      var response = JSON.parse(res);
+      var td = document.getElementById("prediction_"+compound+'_'+model+'_'+idx);
+      //td.innerHTML = "";
+      if (model != "Cramer"){
+        // init general structure
+        a = ["title", "type", "db_hit", "measurements", "prediction", "interval", "probability", "warnings", "info"];
+        
+        // handles consesus mutagenicity
+        if (response['sa_prediction'] != false){
+          var p = document.createElement("p");
+          var h = document.createElement("h5");
+          p.id = "sa";
+          // Consensus mutagenicity
+          var t = document.createTextNode("Consensus mutagenicity");
+          h.appendChild(t);
+          p.appendChild(h);
+          // handle db_hit first
+          if (response['db_hit'] != false){
+            var value = document.createTextNode(response['db_hit']);
+            p.appendChild(value);
+          };
+          // Structural alerts
+          var h2 = document.createElement("h5");
+          var t = document.createTextNode("Structural alerts:");
+          h2.appendChild(t);
+          p.appendChild(h2);
+          // prediction
+          var h3 = document.createElement("h5");
+          var t = document.createTextNode("Prediction:");
+          if (response['sa_prediction']['prediction'] == false){
+            var value = document.createTextNode("non-mutagenic");
+          };
+          if (response['sa_prediction']['prediction'] == true){
+            var value = document.createTextNode("mutagenic");
+          };
+          h3.appendChild(t);
+          p.appendChild(h3);
+          p.appendChild(value);
+          // confidence
+          var h4 = document.createElement("h5");
+          var t = document.createTextNode("Confidence:");
+          var value = document.createTextNode(response['confidence']);
+          h4.appendChild(t);
+          p.appendChild(h4);
+          p.appendChild(value);
+          // Structural alerts for mutagenicity
+          var h5 = document.createElement("h5");
+          var t = document.createTextNode("Structural alerts for mutagenicity:");
+          h5.appendChild(t);
+          p.appendChild(h5);
+          if (response['sa_matches'] != false){
+            var value = document.createTextNode(response['sa_matches']);
+            p.appendChild(value);
+          } else {
+            var span = document.createElement("span");
+            var value = document.createTextNode("none");
+            span.style.fontStyle = "italic";
+            span.appendChild(value);
+            p.appendChild(span);
+          };
+          
+          // append to td
+          td.appendChild(p);
+        };
+
+        // proceed to general structure
+        for (val of a){
+          var p = document.createElement("p");
+          var h = document.createElement("h5");
+          p.id = val;
+          if (val == "title") {
+            var value = document.createTextNode(response['model_name']);
+            h.appendChild(value);
+            p.appendChild(h);
+          } else if (val == "type") {
+            var t = document.createTextNode("Type: ");
+            var value = document.createTextNode(response['model_type']);
+            h.appendChild(t);
+            p.appendChild(h);
+            p.appendChild(value);
+            h.style.display = "inline";
+          } else if (val == "db_hit" && response['db_hit'] != false && response['sa_prediction'] == false) {
+            var value = document.createTextNode(response['db_hit']);
+            p.appendChild(value);
+          } else if (val == "measurements" && response['measurements'] != false) {
+            var t = document.createTextNode("Measurements:");
+            h.appendChild(t);
+            p.appendChild(h);
+            if (response['model_type'] == "Regression"){
+              for (var i=0; i<response['measurements'].length; i++){
+                var value = document.createTextNode(response['measurements'][i]);
+                var br = document.createElement("br");
+                p.appendChild(value);
+                p.appendChild(br);
+                if (response['converted_measurements'] != false) {
+                  var value = document.createTextNode(response['converted_measurements'][i]);
+                  var br = document.createElement("br");
+                  p.appendChild(value);
+                  p.appendChild(br);
+                  if (i+1 != response['measurements'].length){
+                    var br2 = document.createElement("br");
+                    p.appendChild(br2);
+                  };
+                };
+              };
+            } else if (response['model_type'] == "Classification"){
+              for (var i=0; i<response['measurements'].length; i++){
+                var value = document.createTextNode(response['measurements'][i]);
+                var br = document.createElement("br");
+                p.appendChild(value);
+                p.appendChild(br);
+              };
+            };
+          } else if (val == "prediction" && response['prediction_value'] != false) {
+            var t = document.createTextNode("Prediction:");
+            var value = document.createTextNode(response['prediction_value']+" "+response['model_unit']);
+            h.appendChild(t);
+            p.appendChild(h);
+            p.appendChild(value);
+            if (response['converted_value'] != false) {
+              var br = document.createElement("br");
+              var value = document.createTextNode(response['converted_value']+" "+response['converted_model_unit']);
+              p.appendChild(br);
+              p.appendChild(value);
+            };
+            if (response['model_type'] == "Classification" && response['probability'] != false){
+              var h = document.createElement("h5");
+              var t = document.createTextNode("Probability:");
+              h.appendChild(t);
+              p.appendChild(h);
+              for (var i=0; i<response['probability'].length; i++){
+                var value = document.createTextNode(response['probability'][i]);
+                var br = document.createElement("br");
+                p.appendChild(value);
+                p.appendChild(br);
+              };
+            };
+          } else if (val == "interval" && response['interval'] != false) {
+            var t = document.createTextNode("95% Prediction interval:");
+            var value = document.createTextNode(response['interval']+" "+response['model_unit']);
+            h.appendChild(t);
+            p.appendChild(h);
+            p.appendChild(value);
+            if (response['converted_interval'] != false) {
+              var br = document.createElement("br");
+              var value = document.createTextNode(response['converted_interval']+" "+response['converted_model_unit']);
+              p.appendChild(br);
+              p.appendChild(value);
+            };
+          } else if (val == "warnings" && response['warnings'] != false && response['db_hit'] == false) {
+            var t = document.createTextNode("Warnings: ");
+            h.appendChild(t);
+            p.appendChild(h);
+            for (var i=0; i<response['warnings'].length; i++){
+              var value = document.createTextNode(response['warnings'][i]);
+              var br = document.createElement("br");
+              p.appendChild(value);
+              p.appendChild(br);
+            };
+          } else if (val == "info" && response['info'] != false) {
+            var t = document.createTextNode("Info: ");
+            var value = document.createTextNode(response['info']);
+            h.appendChild(t);
+            p.appendChild(h);
+            p.appendChild(value);
+          };
+
+          // append to td
+          td.appendChild(p);
+        };
+      };// if model != Cramer
+      if (model == "Cramer"){
+        for(i=0; i < response['tds'].length; i++){
+          progress('Cramer',i,response['tds'].length);
+          var td = document.getElementById(response['tds'][i]);
+          var p = document.createElement("p");
+          var h = document.createElement("h5");
+          var t = document.createTextNode("Oral toxicity (Cramer rules)");
+          p.id = "cramer";
+          h.appendChild(t);
+          p.appendChild(h);
+          var h2 = document.createElement("h5");
+          var t = document.createTextNode("Cramer rules:");
+          h2.appendChild(t);
+          p.appendChild(h2);
+          if (response['cramer_rules'][i] != "nil"){
+            var value = document.createTextNode(response['cramer_rules'][i]);
+            p.appendChild(value);
+          } else {
+            var span = document.createElement("span");
+            var value = document.createTextNode("none");
+            span.style.fontStyle = "italic";
+            span.appendChild(value);
+            p.appendChild(span);
+          };
+          var h3 = document.createElement("h5");
+          var t = document.createTextNode("Cramer rules, with extensions:");
+          h3.appendChild(t);
+          p.appendChild(h3);
+          if (response['cramer_rules_extensions'][i] != "nil"){
+            var value = document.createTextNode(response['cramer_rules_extensions'][i]);
+            p.appendChild(value);
+          } else {
+            var span = document.createElement("span");
+            var value = document.createTextNode("none");
+            span.style.fontStyle = "italic";
+            span.appendChild(value);
+            p.appendChild(span);
+          };
+
+          // append to td
+          td.appendChild(p);
+          if (i === response['tds'].length-1) {
+            $("img#circle_Cramer").hide();
+            $("a#downbutton_Cramer").show();
+          };
+
+        };
+      };// if model == Cramer
+      // if last compound change progress to download;
+      if (last == true) {
+        $("img#circle_"+model).hide();
+        $("a#downbutton_"+model).show();
+      };
+    });
+  };
+
+
 %div.well
   %a.btn.btn-warning{:href => to('/predict')}
     %span.glyphicon.glyphicon-menu-left{:aria=>{:hidden=>"true"}}
     New Prediction
-  %a.btn.btn-success{:id => "downbutton", :href=>"#{to("/predict/#{@tmppath}/#{@filename}")}", :title=>"download"}
-    %span.glyphicon.glyphicon-download-alt
-    Download CSV
 
   / show file name
   %topline
@@ -14,96 +277,62 @@
       %div.col-md-8
         %h3= @filename
   
-  / displays all prediction result in one table
-  %div.table-responsive
-    %table.table.table-bordered{:id=>"batch", :style=>"background-color:white;"}
-      %tbody
-        - if @warnings
-          - @warnings.each do |warning|
-            %tr
-              %td
-                %b Warning
-              %td
-                = warning.sub(/\b(tmp\/)\b/,"")
-        - @view.each do |compound, array|
-          %tr
-            %td{:style=>"vertical-align:top;"}
-              %p= compound.svg
-              %p= compound.smiles
-            - array.each do |model,prediction|
-              %td{:style=>"vertical-align:top;white-space:nowrap;"}
-                - model.model.class.to_s.match("Classification") ? type = "Classification" : type = "Regression"
-                - unit = model.unit
-                
-                %b{:class => "title"}
-                  = "#{model.endpoint.gsub('_', ' ')} (#{model.species})"
-                    
-                / check for prediction
-                - if prediction[:value]
-                  %p
-                  / show model type (classification|regression)
-                  %b Type:
-                  = type
-                  %p 
-                  / check for database hit
-                  - if prediction[:info] =~ /\b(identical)\b/i
-                    
-                    / show message about dbhit and measurements
-                    %p
-                      %b Compound is part of the training dataset
-                      %p
-                      %b Measured activity:
-                      %br
-                      - if prediction[:measurements].is_a?(Array)
-                        = (type == "Regression") ? prediction[:measurements].collect{|value| "#{value.delog10.signif(3)} (#{unit})</br>#{compound.mmol_to_mg(value.delog10).signif(3)} #{unit =~ /mmol\/L/ ? "(mg/L)" : "(mg/kg_bw/day)"}"}.join("</br>") : prediction[:measurements].join(", ")
-                      - else
-                        = (type == "Regression") ? "#{prediction[:measurements].delog10.signif(3)} (#{unit})</br>#{compound.mmol_to_mg(prediction[:measurements].delog10).signif(3)} #{(unit =~ /\b(mmol\/L)\b/) ? "(mg/L)" : "(mg/kg_bw/day)"}" : prediction[:measurements]
-                    
-                    
-                  / show prediction
-                  %p
-                    %b Prediction:
-                    %br
-                    = (type == "Regression") ? "#{prediction[:value].delog10.signif(3)} (#{unit})</br>#{compound.mmol_to_mg(prediction[:value].delog10).signif(3)} #{(unit =~ /\b(mmol\/L)\b/) ? "(mg/L)" : "(mg/kg_bw/day)"}" : prediction[:value]
-                    
-                    / show prediction interval or probability
-                    %p
-                    - if type == "Regression"
-                      %b 95% Prediction interval:
-                      - interval = (prediction[:prediction_interval].nil? ? nil : prediction[:prediction_interval])
-                      %br
-                      = interval.nil? ? "" : "#{interval[1].delog10.signif(3)} - #{interval[0].delog10.signif(3)} (#{unit})"
-                      %br
-                      = "#{compound.mmol_to_mg(interval[1].delog10).signif(3)} - #{compound.mmol_to_mg(interval[0].delog10).signif(3)} #{(unit =~ /\b(mmol\/L)\b/) ? "(mg/L)" : "(mg/kg_bw/day)"}" if !prediction[:prediction_interval].nil?
-                    - else
-                      %b Probability:
-                      - unless prediction[:probabilities].nil?
-                        - probabilities = ""
-                        - prediction[:probabilities].each{|k,v| probabilities += "#{k}: #{v.signif(3)}<br>"}
-                        %br
-                        = probabilities
-                  / show warnings
-                  %p
-                  - if !prediction[:info].blank?
-                    %b Info:
-                    %br
-                    %p=prediction[:info].sub(/\'.*\'/,"").sub(/,/, ",<br>")
-                  - if !prediction[:warnings].blank?
-                    %b Warnings:
-                    - prediction[:warnings].uniq.each do |warning|
-                      %br
-                      %p=warning.sub(/substances/, "substances<br>").sub(/prediction\:/, "prediction\:<br>")
-                        
-                  / no prediction  
-                - else
-                  %br
-                  - if !prediction[:info].blank?
-                    %b Info:
-                    %br
-                    %p=prediction[:info].sub(/\'.*\'/,"").sub(/,/, ",<br>")
-                  - if !prediction[:warnings].blank?
-                    %b Warnings:
-                    - prediction[:warnings].uniq.each do |warning|
-                      %br
-                      %p=warning.sub(/substances/, "substances<br>").sub(/prediction\:/, "prediction\:<br>")
-            %tr
+  / displays prediction result in table tabs
+  #tabs
+    %ul.nav.nav-tabs.nav-justified{:id=>"batchTabs", :role=>"tablist"}
+      - @models.each_with_index do |model,i|
+        - m = Model::Validation.find model unless model == "Cramer"
+        %li{:class => ("active" if i == 0)}
+          %a{:href => "#results_#{i+1}", :id => "linkTab#{i+1}", data: {toggle:"tab"}}
+            = (model == "Cramer") ? "Oral toxicity (Cramer rules)" : "#{m.endpoint} (#{m.species})"
+          %img.dlwait{:src=>"/images/wait30trans.gif", :id=>"circle_#{model}", :class=>"circle", :alt=>"wait", :style=>"display:none;"}
+          %a.csv.btn{:id => "downbutton_#{model}", :href=>"#{to("/predict/#{@tmppaths[model]}/#{model}/#{@filename}")}", :title=>"download", :style=>"display:none;"}
+            %span.glyphicon.glyphicon-download-alt
+            CSV
+
+    %div.tab-content
+      - csize = @compounds.size
+      - msize = @models.size
+      - timer = 0
+      - @models.each_with_index do |model,j|
+        #results.tab-pane{:id=>"#{j+1}", :class => ("active" if j == 0)}
+          %div{:id=>"progress_"+model, :style=>"width:100%;height:2px;position:relative;background-color:#ccc;"}
+            %div{:id=>"bar_"+model, :style=>"background-color: #4CAF50;width:10px;height:2px;position:absolute;"}
+          %table.table.table-bordered
+            %tbody
+              - @compounds.each_with_index do |compound,cidx|
+                - timer += 800
+                :javascript
+                  $(document).ready(function() {
+                    $("img.circle").show();
+                    var model = '#{model}',
+                        msize = #{msize},
+                        idx = #{j+1},
+                        last = false,
+                        compound = '#{compound.id}',
+                        csize = #{csize},
+                        cidx = #{cidx},
+                        timer = #{timer},
+                        tmppath = '#{@tmppaths[model]}';
+                    // check for last request;
+                    if (cidx+1 == csize) {
+                      last = true;
+                    };
+                    setTimeout(function(){
+                      if (model != "Cramer"){
+                        renderResponse(model,cidx,compound,last,csize,tmppath);
+                      } else if (model == "Cramer" && cidx == 0){
+                        renderResponse(model,cidx,compound,last,csize,tmppath);
+                      };
+                    }, timer );
+                  });
+                // table layout:
+                // one row per prediction;
+                // first col compound; sec col results
+                %tr{:id=>model}
+                  // compound
+                  %td.col-md-6{:id=>"compound"}
+                    %p=embedded_svg(compound.svg, title: compound.smiles)
+                    %p=compound.smiles
+                  // prediction values from js function
+                  %td.col-md-6{:id=>"prediction_#{compound.id}_#{model}_#{cidx}", :style => "vertical-align:top;"}
diff --git a/views/neighbors.haml b/views/neighbors.haml
index 9c12be9..37c99d0 100644
--- a/views/neighbors.haml
+++ b/views/neighbors.haml
@@ -60,7 +60,7 @@
                     / Compound
                     - c = Compound.find(neighbor)
                     %td{:style =>"vertical-align:middle;padding-left:1em;width:50%;"}
-                      = c.svg
+                      = embedded_svg(c.svg, title: c.smiles)
                       %p= c.smiles
                     
                     / Measured Activity
diff --git a/views/prediction.haml b/views/prediction.haml
index 62bed8b..b5a7e82 100644
--- a/views/prediction.haml
+++ b/views/prediction.haml
@@ -4,12 +4,6 @@
     $('.modal').on('hidden.bs.modal', function () {
       $(this).removeData('bs.modal');
     });
-    /*TableExport.prototype.bootstrap = ["btn", "btn-default", "btn-toolbar"];
-    var BootstrapTable = document.getElementById('overview');
-    new TableExport(BootstrapTable, {
-      ignoreCSS: ".ignore",
-      bootstrap: true,
-    });*/
   });
 
 %div.well
@@ -24,7 +18,7 @@
         %tr
           %td{:id=>"compound"}
             %b.title Compound
-            %p= embedded_svg @compound.svg, class: '.ignore', title: "#{@compound.smiles}"
+            %p= embedded_svg(@compound.svg, title: @compound.smiles)
             %p= @compound.smiles
           - @model_types = {}
           - @dbhit = {}
@@ -47,6 +41,16 @@
                   %p
                   %b Structural alerts:
                   %p
+                  / check for database hit
+                  - if prediction[:info] =~ /\b(identical)\b/i
+                    - @dbhit[i] = true
+                    / show message about dbhit and measurements
+                    %p
+                      :plain
+                        This compound was part of the training dataset. <i>All</i> information </br>
+                        from this compound was removed from the training data before the </br>
+                        prediction, to obtain unbiased results.
+                      %p
                   %b Prediction:
                   %br
                   =(hash[:prediction] == true ? "mutagenic" : "non-mutagenic")
@@ -71,19 +75,20 @@
                   - if prediction[:info] =~ /\b(identical)\b/i
                     - @dbhit[i] = true
                     / show message about dbhit and measurements
-                    %p
-                      :plain
-                        This compound was part of the training dataset. <i>All</i> information </br>
-                        from this compound was removed from the training data before the </br>
-                        prediction, to obtain unbiased results.
+                    - unless sa_prediction
                       %p
-                      %b Measured activity:
-                      %br
-                      - if prediction[:measurements].is_a?(Array)
-                        = (type == "Regression") ? prediction[:measurements].collect{|value| "#{value.delog10.signif(3)} (#{unit})</br>#{@compound.mmol_to_mg(value.delog10).signif(3)} #{unit =~ /mmol\/L/ ? "(mg/L)" : "(mg/kg_bw/day)"}"}.join("</br>") : prediction[:measurements].join(", ")
-                      - else
-                        = (type == "Regression") ? "#{prediction[:measurements].delog10.signif(3)} (#{unit})</br>#{@compound.mmol_to_mg(prediction[:measurements].delog10).signif(3)} #{(unit =~ /\b(mmol\/L)\b/) ? "(mg/L)" : "(mg/kg_bw/day)"}" : prediction[:measurements]
-                  
+                        :plain
+                          This compound was part of the training dataset. <i>All</i> information </br>
+                          from this compound was removed from the training data before the </br>
+                          prediction, to obtain unbiased results.
+                        %p
+                        %b Measured activity:
+                        %br
+                        - if prediction[:measurements].is_a?(Array)
+                          = (type == "Regression") ? prediction[:measurements].collect{|value| "#{value.delog10.signif(3)} (#{unit})</br>#{@compound.mmol_to_mg(value.delog10).signif(3)} #{unit =~ /mmol\/L/ ? "(mg/L)" : "(mg/kg_bw/day)"}"}.join("</br>") : prediction[:measurements].join(", ")
+                        - else
+                          = (type == "Regression") ? "#{prediction[:measurements].delog10.signif(3)} (#{unit})</br>#{@compound.mmol_to_mg(prediction[:measurements].delog10).signif(3)} #{(unit =~ /\b(mmol\/L)\b/) ? "(mg/L)" : "(mg/kg_bw/day)"}" : prediction[:measurements]
+                    
                   - else
                     - @dbhit[i] = false
                   
@@ -111,17 +116,6 @@
                           %br
                           = "#{prediction[:probabilities].keys[1]}: #{prediction[:probabilities].values[1].signif(3)}"
                   
-                  / show warnings and info
-                  -#%p
-                    - if !prediction[:info].blank?
-                      %b Info:
-                      %br
-                      %p=prediction[:info].sub(/\'.*\'/,"").sub(/,/, ",<br>")
-                    - if !prediction[:warnings].blank?
-                      %b Warnings:
-                      - prediction[:warnings].uniq.each do |warning|
-                        %br
-                        %p=warning.sub(/,/, ",<br>")
                 - else
                   %p
                   - if !prediction[:info].blank?
@@ -148,6 +142,6 @@
 
   / always show the neighbors table, message is given there. Except only Cramer is selected.
   - unless @predictions.blank?
-    = haml :neighbors, :layout => false, :model_type => @model_types, :dbhit => @dbhit
+    = haml :neighbors, :layout => false
     
 
diff --git a/views/style.scss b/views/style.scss
index ac070a1..2a8f9d4 100644
--- a/views/style.scss
+++ b/views/style.scss
@@ -89,7 +89,15 @@ supporters{
     margin: 1em;
   }
 }
-
+.csv.btn{
+  width: 100%;
+  height: 30px;
+}
+.dlwait{
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
 .footer{
   margin-top:3em;
 }