Update app.py

app.py

@@ -63,13 +63,13 @@ def gr_cosine_similarity(sentence1, sentence2):
   print(temp)
   # extract scores (as many entries as exist in pred_texts)
   for i in range(len(lines_s)):
-    anger.append(temp[i][0])
-    disgust.append(temp[i][1])
-    fear.append(temp[i][2])
-    joy.append(temp[i][3])
-    neutral.append(temp[i][4])
-    sadness.append(temp[i][5])
-    surprise.append(temp[i][6])
+    anger.append(round(temp[i][0], 3))
+    disgust.append(round(temp[i][1], 3))
+    fear.append(round(temp[i][2], 3))
+    joy.append(round(temp[i][3], 3))
+    neutral.append(round(temp[i][4], 3))
+    sadness.append(round(temp[i][5], 3))
+    surprise.append(round(temp[i][6], 3))
 
   # define both vectors for the dot product
   # each include all values for both predictions
@@ -80,7 +80,8 @@ def gr_cosine_similarity(sentence1, sentence2):
   dot_product = dot(v1, v2)
 
    # define df
-  df = pd.DataFrame(list(zip(lines_s,labels, anger, disgust, fear, joy, neutral, sadness, surprise)), columns=['text','label', 'anger', 'disgust', 'fear', 'joy', 'neutral', 'sadness', 'surprise'])
+  df = pd.DataFrame(list(zip(lines_s,labels, anger, disgust, fear, joy, neutral, sadness, surprise)), 
+  columns=['text', 'max_label', 'anger', 'disgust', 'fear', 'joy', 'neutral', 'sadness', 'surprise'])
 
   # compute cosine similarity
   # is dot product of vectors n / norms 1*..*n vectors
@@ -94,8 +95,8 @@ def gr_cosine_similarity(sentence1, sentence2):
 
 gr.Interface(gr_cosine_similarity,
                    [
-                    gr.inputs.Textbox(lines=1, placeholder="This movie always makes me cry..", default="", label="Text 1"),
-                    gr.inputs.Textbox(lines=1, placeholder="Her dog is sad.", default="", label="Text 2"),
+                    gr.inputs.Textbox(lines=1, placeholder="", default="This movie always makes me cry..", label="Text 1"),
+                    gr.inputs.Textbox(lines=1, placeholder="", default="Her dog is sad.", label="Text 2"),
 
                                          ],
                    ["dataframe","text"]