Update app.py

app.py

@@ -49,9 +49,17 @@ def predict_emotions(sentence):
     result = le_departure.inverse_transform(
         np.argmax(model.predict(sentence), axis=-1))[0]
     proba = np.max(model.predict(sentence))
-    print(result)
-    print(proba)
-    return result, proba
+    print()
+
+    return result, proba, get_all_result(model.predict(sentence))
+
+
+def get_all_result(prediction):
+    dict = {}
+    for element in prediction:
+        for i in range(0, len(element)):
+            dict[element[i]] = le_departure.inverse_transform([i])
+    return dict
 
 
 def sentence_cleaning(sentence):
@@ -70,18 +78,7 @@ def sentence_cleaning(sentence):
 
 
 def main():
-    emoji = { "anger":"😠",
-					    "disgust":"🤮",
-					    "fear":"😨😱",
-						"happy":"🤗",
-						"joy":"😂",
-						"neutral":"😐",
-						"sad":"😔", 
-						"sadness":"😔",
-						"shame":"😳",
-						"surprise":"😮"
-					}
-    st.title("🤮😨Emotion Classifier😱😂")
+    st.title("Emotion Classifier")
     menu = ["Home", "Monitor"]
     choice = st.sidebar.selectbox("Menu", menu)
     if choice == "Home":
@@ -92,20 +89,26 @@ def main():
             submit_text = st.form_submit_button(label='Submit')
 
         if submit_text:
-            col1, col2 = st.columns(2)
+            col1, col2 = st.beta_columns(2)
 
             # Apply Fxn Here
-            res, proba = predict_emotions(raw_text)
+            res, proba, total_result = predict_emotions(raw_text)
 
             with col1:
                 st.success("Original Text")
                 st.write(raw_text)
 
                 st.success("Prediction")
-                st.write("{}:{}".format(res, emoji[res]))
+                st.write("{}:{}".format(res, proba))
                 st.write("Confidence:{}".format(proba))
- 
-  
+                print(total_result.keys())
+                print(total_result.values())
+
+                source = pd.DataFrame({'Proba': list(total_result.keys()), 'Emotion': list(total_result.values())})
+
+                fig = alt.Chart(source).mark_bar().encode(x='Emotion',y='Proba',color='Emotion')
+                st.altair_chart(fig,use_container_width=True)
+                
 
     else:
         st.subheader("About")