refactor1

classifier/__init__.py

@@ -1,6 +1,7 @@
-from classifier import in_training, print_answers
+from classifier import  print_answers
 from classifier import get_pipe, test_pipe, get_training_threads
-from classifier import store_training_data
+#from classifier import store_training_data
+#in_training,
 
 from training import train_single_thread
 

classifier/classifier.py

@@ -8,87 +8,17 @@ from sklearn.neural_network import MLPClassifier
 from sklearn.model_selection import train_test_split
 import numpy as np
 import yaml
-from storage import MailThread,db_session
 from sklearn.metrics import accuracy_score, confusion_matrix
+from collections import namedtuple
 
+from storage import get_training_threads #MailThread,db_session
 
-#with open("data.yml", 'r') as stream:
+PredictTool=namedtuple("PredictTool",["Pipeline","LabelEncoder"])
-#    try:
-#        train=yaml.load(stream)
-#    except yaml.YAMLError as exc:
-#        print(exc)
-
-data_types= { "answered": bool, "maintopic": str, "lang": str}
-
-def set_train_data(i,d,key=b"answered"):
-    global train
-    #------------------------------------    
-    if not data_types.has_key(key):
-        raise ValueError("Key "+str(key)+" unknown")
-    if not train.has_key(i) or train[i] is None:
-        train[i]={}
-    if not type(d)  is data_types[key]:
-        raise TypeError("Data - %s - for key "% d +str(key)+" must be " +str(data_types[key])+ " but it is "+ str(type(d)))
-    #------------------------------------
-    train[i][key]=d
-
-        
-def store_training_data(i, d,key=b"answered"):
-    set_train_data(i,d,key)
-    with open("data.yml","w") as file:
-        file.write(yaml.dump(train,default_flow_style=True))
-        file.close()
-
-
-# Lade Trainingsdaten fuer einen angegebenen key (Label/Eigenschaft) 
-def get_training_threads(key="answered", filters=[]):
-    if not data_types.has_key(key):
-        raise ValueError("Key "+str(key)+" unknown")
-    #------------------------------------
-    t_a=[]
-    d_a=[]
-    d_a2=[]
-    #------------------------------------
-    if "db" in filters:
-        q=db_session.query(MailThread).filter(MailThread.istrained.is_(True))
-        if "de" in filters:
-            q=q.filter(MailThread.lang=="de")
-        elif "en" in filters:
-            q=q.filter(MailThread.lang=="en")
-        tt=q.all()
-        for t in tt:
-               t_a.append(t)
-               if key =="answered":
-                   d_a.append(t.is_answered())
-               elif key=="maintopic":
-                   d_a.append(t.maintopic)
-               elif key=="lang":
-                   d_a.append(t.lang)
-    else:
-        raise ValueError("Database Filter now required")
-    le=LabelEncoder()
-    d_a2=le.fit_transform(d_a)
-    return (t_a,d_a2,le)
-                   
-               
- #   else:
- #       for i in train:
- #           if train[i].has_key(key): # In den Trainingsdaten muss der relevante Key sein
-#                t=db_session.query(MailThread).filter(MailThread.firstmail==i).first#()
-#                if not t is None:   # Thread muss in der Datenbank sein
-#                    t_a.append(t)
-#                    d_a.append(train[i][key])
-
-
-def in_training(i, key="answered"):
-    return train.has_key(i) and train[i].has_key(key)
-
 
 def print_answers(l):
-    
+    classes=l.classes_
-    cc=l.classes_
+    classes_encoded=l.transform(cc)
-    c_id=l.transform(cc)
+    for i, c in zip(classes_encoded,classes):
-    for i,c in enumerate(cc):
         print str(i) + ":  " + str(c)    
     return None
 
@@ -117,15 +47,14 @@ class ThreadFirstTextExtractor(BaseEstimator, TransformerMixin):
     def transform(self, X,y=None):
         return [t.text("first") for t in X]
 
-def get_pipe(p=b"pipe1",k=b"answered",filters=[]):
+def get_pipe(p=b"pipe1",key=b"answered",filters=["db"]):
     p=build_pipe(p)
-    tt= get_training_threads(k,filters)
+    threads, labels, labelenc= get_training_threads(key,filters)
-    #print tt
+    if len(threads) > 0:
-    if len(tt[0]) > 0:
+        p.fit(threads,labels)
-        p.fit(tt[0],tt[1])
+        return PredictTool(p,labelenc)
-        return p,tt[2]
     else:
-        return None, None
+        return PredictTool(None, None)
 
 def test_pipe(pp,k,f=[]):
     tt= get_training_threads(k,f)
@@ -142,13 +71,13 @@ def test_pipe(pp,k,f=[]):
 
 
 
-def build_pipe(p=b"pipe1"):
+def build_pipe(pipe=b"pipe1"):
-    if p == "pipe1":
+    if pipe == "pipe1":
         p=Pipeline([('tde', ThreadDictExtractor()),
                     ('dv',DictVectorizer()),
                     ('clf', MultinomialNB())
         ])
-    elif p=="pipe2":
+    elif pipe=="pipe2":
         p = Pipeline([
             ('union', FeatureUnion(transformer_list=[
                 ('subject', Pipeline([('tse', ThreadSubjectExtractor()),
@@ -169,7 +98,7 @@ def build_pipe(p=b"pipe1"):
             } )),
             ('clf', MultinomialNB())
         ])
-    elif p=="pipe2b":
+    elif pipe=="pipe2b":
         p = Pipeline([
             ('union', FeatureUnion(transformer_list=[
             ('subject', Pipeline([('tse', ThreadSubjectExtractor()),
@@ -190,7 +119,7 @@ def build_pipe(p=b"pipe1"):
             } )),
             ('mlc', MLPClassifier())
         ])
-    elif p=="pipe2d":
+    elif pipe=="pipe2d":
         p = Pipeline([
             ('union', FeatureUnion(transformer_list=[
             ('subject', Pipeline([('tse', ThreadSubjectExtractor()),
@@ -217,7 +146,7 @@ def build_pipe(p=b"pipe1"):
             ('mlc', MLPClassifier())
         ])
         
-    elif p=="pipe2e":
+    elif pipe=="pipe2e":
         p = Pipeline([
             ('union', FeatureUnion(transformer_list=[
             ('subject', Pipeline([('tse', ThreadSubjectExtractor()),
@@ -243,7 +172,7 @@ def build_pipe(p=b"pipe1"):
             } )),
             ('mlc', MLPClassifier(hidden_layer_sizes=(100,100)))
         ])
-    elif p=="pipe2e1":
+    elif pipe=="pipe2e1":
         p = Pipeline([
             ('union', FeatureUnion(transformer_list=[
             ('subject', Pipeline([('tse', ThreadSubjectExtractor()),
@@ -269,7 +198,7 @@ def build_pipe(p=b"pipe1"):
             } )),
             ('mlc', MLPClassifier(hidden_layer_sizes=(100,100,50)))
         ])
-    elif p=="pipe2f":
+    elif pipe=="pipe2f":
         p = Pipeline([
             ('union', FeatureUnion(transformer_list=[
             ('subject', Pipeline([('tse', ThreadSubjectExtractor()),
@@ -295,7 +224,7 @@ def build_pipe(p=b"pipe1"):
             } )),
             ('mlc', MLPClassifier(hidden_layer_sizes=(100,100)))
         ])        
-    elif p=="pipe2g":
+    elif pipe=="pipe2g":
         p = Pipeline([
             ('union', FeatureUnion(transformer_list=[
             ('subject', Pipeline([('tse', ThreadSubjectExtractor()),
@@ -321,7 +250,7 @@ def build_pipe(p=b"pipe1"):
             } )),
             ('mlc', MLPClassifier(hidden_layer_sizes=(100,100,100)))
         ])
-    elif p=="pipe2c":
+    elif pipe=="pipe2c":
         p = Pipeline([
             ('union', FeatureUnion(transformer_list=[
             ('subject', Pipeline([('tse', ThreadSubjectExtractor()),

classifier/oldstuff.py

@@ -0,0 +1,23 @@
+def set_train_data(i,d,key=b"answered"):
+    global train
+    #------------------------------------    
+    if not data_types.has_key(key):
+        raise ValueError("Key "+str(key)+" unknown")
+    if not train.has_key(i) or train[i] is None:
+        train[i]={}
+    if not type(d)  is data_types[key]:
+        raise TypeError("Data - %s - for key "% d +str(key)+" must be " +str(data_types[key])+ " but it is "+ str(type(d)))
+    #------------------------------------
+    train[i][key]=d
+
+        
+def store_training_data(i, d,key=b"answered"):
+    set_train_data(i,d,key)
+    with open("data.yml","w") as file:
+        file.write(yaml.dump(train,default_flow_style=True))
+        file.close()
+                  
+
+def in_training(i, key="answered"):
+    return train.has_key(i) and train[i].has_key(key)
+data_types= { "answered": bool, "maintopic": str, "lang": str}

classifier/prediction.py

@@ -1,22 +1,31 @@
-from classifier import get_training_threads,  print_answers, in_training, store_training_data, get_pipe
+from classifier import  get_pipe
 from storage import db_session, MailThread
 
 def predict_threads():
-    pipe1,le=get_pipe("pipe1",b"answered",["db"])
+    """
+    Predicts the language, topic and if a thread is anwered and writes that to the database. This function doesn't have a return value.
+    """
+    # Loading pipes for the prediction of each thread
+    pipe1,le=get_pipe("pipe1",key=b"answered",filter=["db"])
     pipe2,le2=get_pipe("pipe2g", b"maintopic",["db"])
     pipe3,le3=get_pipe("pipe2b", b"lang",["db"])
+
+    # Loading untrained MailThreads:
     q=db_session.query(MailThread).filter(MailThread.istrained.op("IS NOT")(True))
     mail_threads=q.all()
+    
     if len(mail_threads) ==0:
-        raise ValueError("no untrained threads found")
+        raise StandardError("no untrained threads found in database")
+    
     answered=le.inverse_transform(pipe1.predict(mail_threads))
     maintopic=le2.inverse_transform(pipe2.predict(mail_threads))
     lang=le3.inverse_transform(pipe3.predict(mail_threads))
 
     for i, t in enumerate(mail_threads):
-        t.answered=bool(answered[i])
+        t.answered, t.opened, t.maintopic, t.lang = ( bool(answered[i]),
-        t.opened=bool(answered[i])
+                                                      bool(answered[i]),
-        t.maintopic=str(maintopic[i])
+                                                      str(maintopic[i]),
-        t.lang=str(lang[i])
+                                                      str(lang[i])
+        )
         db_session.add(t)
         db_session.commit()

classifier/training.py

@@ -2,7 +2,7 @@ from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import LabelEncoder
 import numpy
 from storage import Mail, MailThread, db_session
-from classifier import store_training_data, print_answers
+from classifier import  print_answers
 
 
 

flaskapp/__init__.py

@@ -49,13 +49,16 @@ def store_value(id,key,value):
         mth.opened=bool(value)
     if key=="maintopic" and value in maintopic_values:
         mth.maintopic=str(value)
-    if key=="lang" and value in maintopic_values:
+    if key=="lang" and value in ["de", "en"]:
         mth.lang=str(value)
     if key =="trained":
         value = value in ["true", "True", "1", "t"]
         mth.istrained=bool(value)
     db_session.add(mth)
     db_session.commit()
+
+
+
 @app.route("/<int:id>")
 def store_answered(id):
 

flaskapp/templates/_macros.html

@@ -0,0 +1,89 @@
+{# -*-jinja2-*- #}
+
+
+{% macro render_nav(m, maintopics) %}
+
+  <div class="nav">
+    <div class="nav-item dropdown">
+      <a class="nav-link dropdown-toggle" id="dropdownMenuLink1_{{m.firstmail}}"  data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">
+	{% if m.is_answered() %}
+	  IS answered
+	{% else %}
+	  ISNOT answered 
+	{% endif %}
+      </a>
+      <div class="dropdown-menu" arialabelledby="dropdownMenuLink1_{{m.firstmail}}">
+	<a class="btn" href="{{m.firstmail}}?key=answered&value={{(not m.is_answered())}}">
+	  answered:{{(not m.is_answered())}}
+	</a>
+      </div>
+    </div>
+
+    <div class="dropdown nav-item">
+      <a class="nav-link dropdown-toggle" id="dropdownMenuLink2_{{m.firstmail}}"  data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">
+	{{m.maintopic}}
+      </a>
+      <div class="dropdown-menu" arialabelledby="dropdownMenuLink2_{{m.firstmail}}">
+	{% for t in maintopics %}
+	  <a class="dropdown-item" href="{{m.firstmail}}?key=maintopic&value={{t}}">{{t}}</a>
+	{% endfor %}
+      </div>
+    </div>
+
+    <div class="nav-item dropdown">
+      <a class="nav-link dropdown-toggle" id="dropdownMenuLink3_{{m.firstmail}}"  data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#"> {% if m.istrained %}
+	IS trained
+      {% else %}
+	ISNOT trained 
+      {% endif %}
+      </a>
+      <div class="dropdown-menu" arialabelledby="dropdownMenuLink3_{{m.firstmail}}">
+	<a class="btn" href="{{m.firstmail}}?key=trained&value={{(not m.istrained)}}">TRAINED:{{(not m.istrained)}}</a> 
+      </div>
+    </div>
+
+    <div class="nav-item dropdown">
+      <a class="nav-link dropdown-toggle" id="dropdownMenuLink4_{{m.firstmail}}"  data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#"> {{ m.lang }}
+      </a>
+      <div class="dropdown-menu" arialabelledby="dropdownMenuLink3_{{m.firstmail}}">
+	{% for l in ["de", "en"] %}
+
+	  <a class="btn" href="{{m.firstmail}}?key=lang&value={{l}}">{{(l)}}</a> 
+	{% endfor %}
+
+      </div>
+    </div>			 
+    
+  </div>
+{% endmacro %}
+
+{% macro render_inline_thread(m, maintopics) %}
+
+  <div class="card card-thread answ-{{m.is_answered()}} trained-{{m.istrained}}">
+    <div class="" role="tab" id="heading{{m.firstmail}}">
+      <b class="mb-0">
+	<a data-toggle="collapse"
+	   data-parent="#accordion"
+	   href="#collapse{{m.firstmail}}"
+	   aria-expanded="true" aria-controls="collapse1"
+	   >
+	   {% if m.istrained %} trained: {% endif %} {{m.tstr()}}
+	</a>
+      </b>
+    </div>
+    <div id="collapse{{m.firstmail}}"
+	 class="collapse {{'show' if m.firstmail==opened}}"
+	 role="tabpanel"
+	 aria-labelledby="headingOne"
+	 >
+      <div class="card-block">
+	{{render_nav(m,maintopics) }}
+      </div>
+      
+      {% for (h,txt) in m.print_mail() %}
+	<div> {{h}}</div>
+	<div class="textbox"> {{ txt }}	</div>
+      {% endfor %}
+    </div>
+  </div>
+{% endmacro %}

flaskapp/templates/index.html

@@ -1,13 +1,8 @@
+{# -*-jinja2-*- #}
+{% from "_macros.html" import render_inline_thread %}
+{% extends "layout.html" %}
 
-
+{% block header %}
-<html>
-  <head>
-    <title>{{title}}</title>
-    <script src="/static/jquery-3.2.0.min.js" ></script>
-    <link rel="stylesheet" href="/static/bootstrap/css/bootstrap.min.css"/>
-    <script src="/static/bootstrap/js/bootstrap.min.js" ></script>
-  </head>
-  <body>
   <style>
     .card.answ-1,.card.answ-True {
     background: lightgreen;
@@ -15,90 +10,36 @@
     .card.trained-1,.card.trained-True {
     border: solid blue;
     }
+    .card.card-thread {
+    padding-top: 2pt;
+    padding-bottom:2pt;
+    border-radius:0;
+    margin-top:1pt;
+    margin-bottom:1pt
+    }
+    div.textbox {
+    white-space: pre-wrap;
+    font:Courier, monospace;
+    font-size:small;
+    width:50em;
+    border: thin blue solid;
+    }
   </style>
+{% endblock %}
+
+{% block content %}
   <div class="container">
     <div class="row">
       <div class="col-12">
+
 	<h1>{{title}}</h1>
 
 	<div id="accordion" role="tablist" aria-multiselectable="true">
-	    
 	  {% for m in mths %}
-	    <div class="card answ-{{m.is_answered()}} trained-{{m.istrained}}" style="padding-top: 2pt; padding-bottom:2pt; border-radius:0;margin-top:1pt; margin-bottom:1pt">
+	    {{ render_inline_thread(m, maintopics) }}
-	      <div class="" role="tab" id="heading{{m.firstmail}}">
-		<b class="mb-0">
-		  <a data-toggle="collapse" data-parent="#accordion" href="#collapse{{m.firstmail}}" aria-expanded="true" aria-controls="collapse1">
-		    {% if m.istrained %} trained: {% endif %} 				      {{m.tstr()}}
-		  </a>
-		</b>
-	      </div>
-
-	      <div id="collapse{{m.firstmail}}" class="collapse {{'show' if m.firstmail==opened}}" role="tabpanel" aria-labelledby="headingOne">
-		
-		<div class="card-block">
-		  
-		  <div class="nav">
-		    <div class="nav-item dropdown">
-		      <a class="nav-link dropdown-toggle" id="dropdownMenuLink1_{{m.firstmail}}"  data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#"> {% if m.is_answered() %}
-			IS answered
-			{% else %}
-			ISNOT answered 
-			{% endif %}
-		      </a>
-		      <div class="dropdown-menu" arialabelledby="dropdownMenuLink1_{{m.firstmail}}">
-
-			<a class="btn" href="{{m.firstmail}}?key=answered&value={{(not m.is_answered())}}">answered:{{(not m.is_answered())}}</a> 
-		      </div>
-		    </div>
-
-		    <div class="dropdown nav-item">
-		      <a class="nav-link dropdown-toggle" id="dropdownMenuLink2_{{m.firstmail}}"  data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#"> {{m.maintopic}}</a>
-		      <div class="dropdown-menu" arialabelledby="dropdownMenuLink2_{{m.firstmail}}">
-			{% for t in maintopics %}
-			<a class="dropdown-item" href="{{m.firstmail}}?key=maintopic&value={{t}}">{{t}}</a>
 	  {% endfor %}  
 	</div>
       </div>
-
-		    <div class="nav-item dropdown">
-		      <a class="nav-link dropdown-toggle" id="dropdownMenuLink3_{{m.firstmail}}"  data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#"> {% if m.istrained %}
-			IS trained
-			{% else %}
-			ISNOT trained 
-			{% endif %}
-		      </a>
-		      <div class="dropdown-menu" arialabelledby="dropdownMenuLink3_{{m.firstmail}}">
-			<a class="btn" href="{{m.firstmail}}?key=trained&value={{(not m.istrained)}}">TRAINED:{{(not m.istrained)}}</a> 
     </div>
   </div>
-
+{% endblock %}
-		    <div class="nav-item dropdown">
-		      <a class="nav-link dropdown-toggle" id="dropdownMenuLink4_{{m.firstmail}}"  data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#"> {{ m.lang }}
-		      </a>
-		      <div class="dropdown-menu" arialabelledby="dropdownMenuLink3_{{m.firstmail}}">
-			{% for l in ["de", "en"] %}
-
-			<a class="btn" href="{{m.firstmail}}?key=lang&value={{l}}">{{(l)}}</a> 
-			{% endfor %}
-
-		      </div>
-		    </div>			 
-		    
-  		  </div>
-		</div>
-
-		{% for txt in m.print_text() %}
-		<div style="white-space: pre-wrap;font:Courier, monospace; font-size:small; width:50em; border: thin blue solid;">
-		  {{ txt }}
-		</div>
-		{% endfor %}
-	      </div>
-	    </div>
-	  {% endfor %}  
-	  </div>
-	  
-	</div>
-      </div>
-    </div>
-</div>
-</body>

flaskapp/templates/layout.html

@@ -0,0 +1,17 @@
+{# -*-jinja2-*- #}
+
+<html>
+  <head>
+    <title>{{title}}</title>
+    <script src="/static/jquery-3.2.0.min.js" ></script>
+    <link rel="stylesheet" href="/static/bootstrap/css/bootstrap.min.css"/>
+    <script src="/static/bootstrap/js/bootstrap.min.js" ></script>
+    {% block header %}
+    {% endblock %}
+  </head>
+  <body>
+    {% block content %}
+    {% endblock %}
+  </body>
+</html>
+

run.py

@@ -70,29 +70,6 @@ if len(sys.argv)>1:
             from flaskapp import app
             app.run(port=3000,debug=True)
 
-    if sys.argv[1] == "print_threads":
-        mth=db_session.query(MailThread).all()
-        for t in mth:
-            print t.firstmail
-            print t.mail_flat_dict()
-
-    if sys.argv[1] == "print_thrd":
-        if len(sys.argv)<3:
-            mth=db_session.query(MailThread).all()
-            for t in mth:
-                print t.firstmail
-        else:
-            t=db_session.query(MailThread).filter(MailThread.firstmail==sys.argv[2]).first()
-            
-            print t.firstmail
-            print t.subject()
-            print t.text()
-    if sys.argv[1] == "compile_threads":
-        mth=db_session.query(MailThread).all()
-        l=len(mth)
-        for i,t in enumerate(mth):
-                print "%d/%d" % (i,l)
-                t.compile()
 
     if  sys.argv[1] == "trained_threads_from_yml":
         from classifier.classifier import train
@@ -188,8 +165,7 @@ if len(sys.argv)>1:
                     th.body=yaml.dump(t)
                     th.islabeled=False
                     th.opened=True
-                else:
+                    th.compile()
-                    th.body=yaml.dump(t)
                     db_session.add(th)
                     db_session.commit()
         print thrds

storage/__init__.py

@@ -2,7 +2,41 @@
 from database import db_session, init_db
 from database import Base
 metadata=Base.metadata
-
+from sklearn.preprocessing import LabelEncoder
+from collections import namedtuple
 # Two main objects: Mail & MailThread
 from mail_model import Mail
 from thread_model import MailThread
+def str_bool(s):
+    return s in ["1", "true", "True", "t","T"]
+
+# Lade Trainingsdaten fuer einen angegebenen key (Label/Eigenschaft) 
+def get_training_threads(key="answered", filters=[]):
+    #------------------------------------
+    
+    db_fields= {"answered": lambda t: t.is_answered(),
+                "lang": lambda t: t.lang,
+                "maintopic": lambda t: t.maintopic}
+    
+    if not db_fields.has_key(key):
+        raise ValueError("Key "+str(key)+" unknown")
+        
+    q=db_session.query(MailThread)
+    q=q.filter(MailThread.istrained.is_(True))
+
+    if "de" in filters:
+        q=q.filter(MailThread.lang=="de")
+    elif "en" in filters:
+        q=q.filter(MailThread.lang=="en")
+
+    # load and extract thread fields
+    threads=q.all()
+    labels = map(db_fields[key], threads)
+
+    # encode using LabelEncoder
+    le=LabelEncoder()
+    labels=le.fit_transform(labels)
+    
+    TrainingThreads=namedtuple("TrainingThreads", ["MailThreads","EncodedLabels","LabelEncoder"])
+    
+    return TrainingThreads(threads,labels,le)

storage/mail_model.py

@@ -128,10 +128,14 @@ class Mail(Base):
                     self.text= yaml.dump(b4.get_text())
                 else:
                     self.text =yaml.dump( pl)
+    def print_head(self):
+        fr=yaml.load(self.from_)
+        return "Gesendet von: "+str(fr[0]["mail"])+"@"+str(fr[0]["host"])+" am "+ str(self.date)
+                    
     def print_text(self):
         txt="" 
         fr=yaml.load(self.from_)
-        txt= txt+ "Gesendet von: "+str(fr[0]["mail"])+"@"+str(fr[0]["host"])+" am "+ str(self.date) + "\n" 
+#        txt= txt+ "Gesendet von: "+str(fr[0]["mail"])+"@"+str(fr[0]["host"])+" am "+ str(self.date) + "\n" 
         t=yaml.load(self.text)
         if type(t) is unicode:
             #txt=txt

storage/thread_model.py

@@ -105,6 +105,14 @@ class MailThread(Base):
             db_session.commit()
         self.date=self.mails()[0].date
 
+    def print_mail(self, filter="all"):
+        a=[]
+        if filter=="all":
+            mm=self.mails()
+            for m in mm:
+                a.append((m.print_head(), m.print_text()))
+        return a
+        
     def print_text(self,filter="all"):
         a=[]
         if filter=="all":
@@ -114,6 +122,16 @@ class MailThread(Base):
         elif filter=="first":
             a.append(m[0].print_text())
         return a
+    def print_head(self,filter="all"):
+        a=[]
+        if filter=="all":
+            mm=self.mails()
+            for m in mm:
+                a.append(m.print_head())
+        elif filter=="first":
+            a.append(m[0].print_head())
+        return a
+    
     def text(self,filter="all"):
         a=u""
         def mail_txt(m):