Rewrote classtoemotion in views.py; completed the following user story:As a user, I want that when I ask the system for a prediction, the system prompts me the index of the website with the prediction inserted in the string The emotion predicted for your file is. Actually, it is prompting an API view.

Author: marcogdepinto

App/__pycache__/forms.cpython-36.pyc

@@ -10,4 +10,3 @@ class FileForm(ModelForm):
     class Meta:
         model = FileModel
         fields = ['file']
-

App/__pycache__/models.cpython-36.pyc

@@ -6,4 +6,3 @@ class FileModel(models.Model):
     file = models.FileField(null=True, blank=True)
     timestamp = models.DateTimeField(auto_now_add=True)
     path = models.FilePathField(path=settings.MEDIA_ROOT, default=settings.MEDIA_ROOT)
-

App/__pycache__/urls.cpython-36.pyc

@@ -17,20 +17,27 @@
         <div class="jumbotron text-center" id="myJumbotron" style="background-color:white">
           <h2>Deep Learning to predict human emotions.</h2>
         </div>
-
         <div class="container" id="Container1">
           <div class="row">
-            <div class="col-sm-4" id="FileUpload">
+            <div class="col-sm-6 text-center" id="FileUpload">
               <a href="{% url 'upload_file' %}" class="btn btn-primary">Upload your audio file</a>
             </div>
-            <div class="col-sm-4" id="makePredictions">
+            <div class="col-sm-6 text-center" id="makePredictions">
               <a href="{% url 'file_select' %}" class="btn btn-primary">Make your prediction</a>
             </div>
-            <div class="col-sm-4" id="PredictionsOutput">
-              <h4>The emotion predicted for your file is: </h4>
-            </div>
           </div>
         </div>
+         <div class="jumbotron text-center" id="myJumbotron2" style="background-color:white">
+             <div class="container" id="Container2">
+               <div class="row">
+                  <div class="col-sm-12 text-center" id="PredictionsOutput">
+                  {% for pred in predictions %}
+                    <h4>The emotion of the speaker in your file is: <strong>{{ pred }}</strong> </h4>
+                  {% endfor %}
+                  </div>
+               </div>
+             </div>
+         </div>
     </body>
-{% endblock %}
-</html>
+</html>
+{% endblock %}

App/__pycache__/views.cpython-36.pyc

@@ -1,11 +1,9 @@
-from django.urls import path
-from App.views import Predict, FileView, IndexView
+from App.views import Predict, FileView
 from django.conf.urls import url
 
 app_name = 'App'
 
 urlpatterns = [
-    path('index/', IndexView.as_view(), name="index"),
     url(r'^predict/$', Predict.as_view(), name="APIpredict"),
     url(r'^upload/$', FileView.as_view(), name='APIupload'),
 ]

App/forms.py

@@ -13,6 +13,7 @@
 from rest_framework.response import Response
 from django.views.generic import TemplateView
 from django.views.generic.edit import CreateView
+from rest_framework.renderers import TemplateHTMLRenderer
 from rest_framework.parsers import MultiPartParser, FormParser
 
 
@@ -97,6 +98,10 @@ class Predict(views.APIView):
     Example of output:
     [["neutral"]]
     """
+
+    template_name = 'index.html'
+    renderer_classes = [TemplateHTMLRenderer]
+
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
         modelname = 'Emotion_Voice_Detection_Model.h5'
@@ -105,24 +110,37 @@ def __init__(self, **kwargs):
         self.loaded_model = keras.models.load_model(os.path.join(settings.MODEL_ROOT, modelname))
         self.predictions = []
 
+    def file_elaboration(self, filepath):
+        """
+        This function is used to elaborate the file used for the predictions with librosa.
+        :param filepath:
+        :return: predictions
+        """
+        data, sampling_rate = librosa.load(filepath)
+        try:
+            mfccs = np.mean(librosa.feature.mfcc(y=data, sr=sampling_rate, n_mfcc=40).T, axis=0)
+            x = np.expand_dims(mfccs, axis=2)
+            x = np.expand_dims(x, axis=0)
+            numpred = self.loaded_model.predict_classes(x)
+            self.predictions.append([self.classtoemotion(numpred)])
+            return self.predictions
+        except Exception as err:
+            return Response(str(err), status=status.HTTP_400_BAD_REQUEST)
+
     def post(self, request):
         """
         This method is used to making predictions on audio files previously loaded with FileView.post
         """
         with graph.as_default():
             filename = request.POST.getlist('file_name').pop()
             filepath = str(os.path.join(settings.MEDIA_ROOT, filename))
-            data, sampling_rate = librosa.load(filepath)
+            predictions = self.file_elaboration(filepath)
             try:
-                mfccs = np.mean(librosa.feature.mfcc(y=data, sr=sampling_rate, n_mfcc=40).T, axis=0)
-                x = np.expand_dims(mfccs, axis=2)
-                x = np.expand_dims(x, axis=0)
-                numpred = self.loaded_model.predict_classes(x)
-                self.predictions.append([self.classtoemotion(numpred)])
+                return Response({'predictions': predictions.pop()})
             except Exception as err:
                 return Response(str(err), status=status.HTTP_400_BAD_REQUEST)
 
-        return Response(self.predictions, status=status.HTTP_200_OK)
+        return Response(predictions, status=status.HTTP_200_OK)
 
     @staticmethod
     def classtoemotion(pred):
@@ -134,29 +152,18 @@ def classtoemotion(pred):
         Example:
         >>> classtoemotion(0) == neutral
         """
-        if pred == 0:
-            pred = "neutral"
-            return pred
-        elif pred == 1:
-            pred = "calm"
-            return pred
-        elif pred == 2:
-            pred = "happy"
-            return pred
-        elif pred == 3:
-            pred = "sad"
-            return pred
-        elif pred == 4:
-            pred = "angry"
-            return pred
-        elif pred == 5:
-            pred = "fearful"
-            return pred
-        elif pred == 6:
-            pred = "disgust"
-            return pred
-        elif pred == 7:
-            pred = "surprised"
-            return pred
-        else:
-            return "Prediction out of the expected range (1-7)"
+
+        label_conversion = {"0": "neutral",
+                            "1": "calm",
+                            "2": "happy",
+                            "3": "sad",
+                            "4": "angry",
+                            "5": "fearful",
+                            "6": "disgust",
+                            "7": "surprised"}
+
+        for key in label_conversion.keys():
+            if int(key) == pred:
+                return label_conversion[key]
+            else:
+                continue