refactor widgets

src/create_statistics.py

@@ -4,7 +4,7 @@ import numpy as np
 import plotly.express as px
 import streamlit as st
 
-from .lib import initialise_storytelling
+from .lib import initialise_storytelling, set_input
 
 
 def run_create_statistics(gen, container_guide, container_param, container_button):
@@ -12,15 +12,21 @@ def run_create_statistics(gen, container_guide, container_param, container_butto
     first_sentence, first_emotion, length = initialise_storytelling(
         gen, container_guide, container_param, container_button)
     # story_till_now = first_sentence
-    num_generation = container_param.slider(
-        label='Number of generation', min_value=1, max_value=100, value=5, step=1)
-    num_tests = container_param.slider(
-        label='Number of tests', min_value=1, max_value=1000, value=3, step=1)
-    reaction_weight_mode = container_param.select_slider(
+
+    num_generation, num_generation0 = set_input(container_param,
+                                                label='Number of generation', min_value=1, max_value=100, value=5, step=1,
+                                                key_slider='num_generation_slider', key_input='num_generation_input',)
+
+    num_tests, num_tests0 = set_input(container_param,
+                                      label='Number of tests', min_value=1, max_value=1000, value=3, step=1,
+                                      key_slider='num_tests_slider', key_input='num_tests_input',)
+
+    reaction_weight_mode = container_param.radio(
         "Reaction Weight w:", ["Random", "Fixed"])
     if reaction_weight_mode == "Fixed":
-        reaction_weight = container_param.slider(
-            label='Reaction Weight w', min_value=0.0, max_value=1.0, value=0.5, step=0.01)
+        reaction_weight, reaction_weight0 = set_input(container_param,
+                                                      label='Reaction Weight w', min_value=0.0, max_value=1.0, value=0.5, step=0.01,
+                                                      key_slider='w_slider', key_input='w_input',)
     elif reaction_weight_mode == "Random":
         reaction_weight = -1
     if container_button.button('Analyse'):

src/lib.py

@@ -27,6 +27,39 @@ def initialise_storytelling(gen, container_guide, container_param, container_but
     first_sentence = story_till_now
     first_emotion = gen.get_emotion(first_sentence)
 
-    length = container_param.slider(label='Length of the generated sentence',
-                                    min_value=1, max_value=100, value=10, step=1)
+    length, length0 = set_input(container_param,
+                                label='Length of the sentence',
+                                    min_value=1, max_value=100, value=10, step=1,
+              key_slider='length_slider', key_input='length_input',)
     return first_sentence, first_emotion, length
+
+
+def set_input(container_param,
+              label, key_slider, key_input,
+              min_value=0.,
+              max_value=1.,
+              value=.5,
+              step=.01,):
+    def slider2input():
+        st.session_state[key_input] = st.session_state[key_slider]
+
+    def input2slider():
+        st.session_state[key_slider] = st.session_state[key_input]
+    container_param = container_param.columns([1.1, 1])
+    number_input = container_param[0].number_input(
+        label=label,
+        min_value=min_value,
+        max_value=max_value,
+        value=value,
+        step=step,
+        key=key_input,
+        on_change=input2slider)
+    slider_input = container_param[1].slider(
+        label='',
+        min_value=min_value,
+        max_value=max_value,
+        value=value,
+        step=step,
+        key=key_slider,
+        on_change=slider2input)
+    return slider_input, number_input

src/probability_emote.py

@@ -1,7 +1,7 @@
 import numpy as np
 import plotly.graph_objects as go
 import streamlit as st
-
+from .lib import set_input
 
 @st.cache
 def get_w(f, ec=0.86, rv=0.50):
@@ -40,7 +40,6 @@ def proper_float(i):
 def get_text():
     return '''  
                  
-    ## Description
     ### Eric's proposal
     > I would propose a scoring metric something like this:
     > * `probability_emote = w * emotion_confidence + (1 - w) * frequency_penalty`
@@ -89,35 +88,6 @@ def get_equation_text(w=0.5, ec=0.7, rand=None, emotion_frequency=None):
     return text
 
 
-def set_input(container_param,
-              label, key_slider, key_input,
-              min_value=0.,
-              max_value=1.,
-              value=.5,
-              step=.01,):
-    def slider2input():
-        st.session_state[key_input] = st.session_state[key_slider]
-
-    def input2slider():
-        st.session_state[key_slider] = st.session_state[key_input]
-    container_param = container_param.columns([1.1, 1])
-    slider_input = container_param[1].slider(
-        label=label,
-        min_value=min_value,
-        max_value=max_value,
-        value=value,
-        step=step,
-        key=key_slider,
-        on_change=slider2input)
-    number_input = container_param[0].number_input(
-        label='',
-        min_value=min_value,
-        max_value=max_value,
-        value=value,
-        step=step,
-        key=key_input,
-        on_change=input2slider)
-    return slider_input, number_input
 
 
 def run_probability_emote(container_param):