updates "Play Storytelling" mode and adds next_sentence function

app.py

@@ -8,7 +8,6 @@ import numpy as np
 st.set_page_config(page_title='Storytelling ' +
                    u'\U0001F5BC', page_icon=u'\U0001F5BC', layout="wide")
 gen = StoryGenerator()
-
 container_mode = st.sidebar.container()
 container_guide = st.sidebar.container()
 container_param = st.sidebar.container()
@@ -34,14 +33,15 @@ if cfs == 'Custom':
 else:
     st.session_state.first_sentence = cfs
     story_till_now = cfs
+first_sentence = story_till_now
+first_emotion = gen.get_emotion(first_sentence)
 
-
-num_generation = container_param.slider(
-    label='Number of generation', min_value=1, max_value=100, value=5, step=1)
 length = container_param.slider(label='Length of the generated sentence',
                                 min_value=1, max_value=100, value=10, step=1)
 if mode == 'Create Statistics':
 
+    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(
@@ -85,23 +85,66 @@ if mode == 'Create Statistics':
     else:
         container_guide.markdown(
             '### You selected statistics. Now set your parameters and click the `Analyse` button.')
-# elif mode == 'Play Storytelling':
+elif mode == 'Play Storytelling':
+
+    if 'sentence_list' not in st.session_state:
+        st.session_state.sentence_list = [{'sentence': first_sentence,
+                                           'emotion': first_emotion['label'],
+                                           'score': first_emotion['score']}]
+    if 'full_story' not in st.session_state:
+        st.session_state.full_story = story_till_now
+    # # , placeholder="Start writing your story...")
+    # story_till_now = st.text_input(
+    #     label='First Sentence', value='Hello, I\'m a language model,')
+
+    # num_generation = st.sidebar.slider(
+    #     label='Number of generation', min_value=1, max_value=100, value=10, step=1)
+    # length = st.sidebar.slider(label='Length of the generated sentence',
+    #                            min_value=1, max_value=100, value=20, step=1)
+    container_button = container_button.columns([1, 1, 1])
+    heading_container = st.container()
+    col_turn, col_sentence, col_emo = st.columns([1, 8, 2])
+    if container_button[0].button('Run'):
+        heading_container.markdown(f'### Story')
+
+        # st.text(story_till_now)
+        full_story, emotion, new_sentence = gen.next_sentence(
+            st.session_state.full_story, length)
+        st.session_state.full_story = story_till_now
+        st.session_state.sentence_list.append({
+            'sentence': new_sentence,
+            'emotion': emotion["label"],
+            'score': emotion["score"]})
+        # col_sentence.markdown(st.session_state.sentence_list)
+        for step in st.session_state.sentence_list:
+            col_sentence.markdown(step['sentence'])
+            col_emo.markdown(
+                f'{step["emotion"]} {np.round(step["score"], 3)}', unsafe_allow_html=False)
+        # i=0
+        # while True:
+        #     story_till_now, emotion, new_sentence = gen.next_sentence(
+        #         story_till_now, length)
+        #     col_sentence.text(new_sentence)
+        #     col_emo.markdown(f'{emotion["label"]} {np.round(emotion["score"], 3)}', unsafe_allow_html=False)
+        #     # col_emo.markdown(f'The last sentence has the "{emotion["label"]}" **Emotion** with a confidence score of {emotion["score"]}.')
+        #     new_input_sentence = st.text_input(label='Next Sentence', key=f'next_sentence_{i}')
+        #     story_till_now += ' ' + new_input_sentence
+
+        #     i+=1
+
+    else:
+        step = st.session_state.sentence_list[0]
+        col_sentence.markdown(step['sentence'])
+        col_emo.markdown(
+            f'{step["emotion"]} {np.round(step["score"], 3)}', unsafe_allow_html=False)
+        st.markdown(
+            '### Write the first sentence and then hit the `Run` button')
 
-#     # # , placeholder="Start writing your story...")
-#     # story_till_now = st.text_input(
-#     #     label='First Sentence', value='Hello, I\'m a language model,')
+    if container_button[2].button('Clear'):
 
-#     # num_generation = st.sidebar.slider(
-#     #     label='Number of generation', min_value=1, max_value=100, value=10, step=1)
-#     # length = st.sidebar.slider(label='Length of the generated sentence',
-#     #                            min_value=1, max_value=100, value=20, step=1)
-#     if container_button.button('Run'):
-#         story_till_now, emotion = gen.story(
-#             story_till_now, num_generation, length)
-#         st.markdown(f'### Story')
-#         st.text(story_till_now)
-#         st.markdown(f'The last sentence has the "{emotion["label"]}" **Emotion** with a confidence score of {emotion["score"]}.')
-#     else:
-#         container_guide.markdown('### Write the first sentence and then hit the `Run` button')
+        st.session_state.full_story = first_sentence
+        st.session_state.sentence_list = [{'sentence': first_sentence,
+                                           'emotion': first_emotion['label'],
+                                           'score': first_emotion['score']}]
 # elif mode == 'Analyse Emotions':
 #     container_mode.write('Let\'s play storytelling.')

story_gen.py

@@ -83,6 +83,18 @@ class StoryGenerator:
             # print(story_to_print)
             # printj.purple(f'Emotion: {emotion}')
         return story_till_now, emotion
+    
+    def next_sentence(self,
+            story_till_now="Hello, I'm a language model,",
+            length=10):
+        last_length = len(story_till_now)
+        genreate_robot_sentence = self.generator(story_till_now, max_length=self.get_num_token(story_till_now) +
+                                                    length, num_return_sequences=1)
+        story_till_now = genreate_robot_sentence[0]['generated_text']
+        new_sentence = story_till_now[last_length:]
+        emotion = self.get_emotion(new_sentence)
+        return story_till_now, emotion, new_sentence
+        
 
     def auto_ist(self,
                  story_till_now="Hello, I'm a language model,",