fixes error gen

app.py

@@ -1,6 +1,5 @@
 import streamlit as st
-from story_gen import StoryGenerator
-import plotly.figure_factory as ff
+from lib.story_gen import StoryGenerator
 import plotly.express as px
 import random
 import numpy as np
@@ -12,9 +11,11 @@ container_mode = st.sidebar.container()
 container_guide = st.container()
 container_param = st.sidebar.container()
 container_button = st.sidebar.container()
+
 mode = container_mode.radio(
     "Select a mode",
-    ('Create Statistics', 'Play Storytelling'), index=0)
+    ('Probability Emote', 'Create Statistics', 'Play Storytelling'), index=0)
+
 choices_first_sentence = [
     'Custom',
     'Hello, I\'m a language model,',
@@ -93,14 +94,6 @@ elif mode == 'Play Storytelling':
                                            '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])
@@ -121,17 +114,6 @@ elif mode == 'Play Storytelling':
             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]

story_gen.py

@@ -1,221 +0,0 @@
-
-import sys
-import time
-
-import printj
-from transformers import pipeline  # , set_seed
-import numpy as np
-import pandas as pd
-# import nltk
-import re
-import streamlit as st
-
-
-class StoryGenerator:
-    def __init__(self):
-        self.initialise_models()
-        self.stats_df = pd.DataFrame(data=[], columns=[])
-        self.stories = []
-        self.data = []
-
-    @staticmethod
-    @st.cache(allow_output_mutation=True)
-    def get_generator():
-        return pipeline('text-generation', model='gpt2')
-
-    @staticmethod
-    @st.cache(allow_output_mutation=True)
-    def get_classifier():
-        return pipeline("text-classification",
-                        model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
-
-    def initialise_models(self):
-        # start = time.time()
-        self.generator = self.get_generator()
-        self.classifier = self.get_classifier()
-        # initialising_time = time.time()-start
-        # print(f'Initialising Time: {initialising_time}')
-        # set_seed(42)
-        # sys.exit()
-
-    def reset():
-        self.clear_stories()
-        self.clear_stats()
-
-    def clear_stories(self):
-        self.data = []
-        self.stories = []
-
-    def clear_stats(self):
-        self.stats_df = pd.DataFrame(data=[], columns=[])
-
-    def get_emotion(self, text):
-        emotions = self.classifier(text)
-        emotion = max(emotions[0], key=lambda x: x['score'])
-        return emotion
-
-    @staticmethod
-    def get_num_token(text):
-        # return len(nltk.word_tokenize(text))
-        return len(re.findall(r'\w+', text))
-
-    @staticmethod
-    def check_show_emotion(confidence_score, frequency, w):
-        frequency_penalty = 1 - frequency
-        probability_emote = w * confidence_score + (1-w) * frequency_penalty
-        return probability_emote > np.random.random_sample()
-
-    def story(self,
-              story_till_now="Hello, I'm a language model,",
-              num_generation=4,
-              length=10):
-        # last_length = 0
-
-        for i in range(num_generation):
-            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)
-            # printj.yellow(f'Sentence {i}:')
-            # story_to_print = f'{printj.ColorText.cyan(story_till_now[:last_length])}{printj.ColorText.green(story_till_now[last_length:])}\n'
-            # 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,",
-                 num_generation=4,
-                 length=20, reaction_weight=0.5):
-        stats_df = pd.DataFrame(data=[], columns=[])
-        stats_dict = dict()
-        num_reactions = 0
-        reaction_frequency = 0
-        emotion = self.get_emotion(story_till_now)  # first line emotion
-        story_data = [{
-            'sentence': story_till_now,
-            'turn': 'first',
-            'emotion': emotion['label'],
-            'confidence_score': emotion['score'],
-        }]
-        for i in range(num_generation):
-            # Text generation for User
-            last_length = len(story_till_now)
-            printj.cyan(story_till_now)
-            printj.red.bold_on_white(
-                f'loop: {i}; generate user text; length: {last_length}')
-            genreate_user_sentence = self.generator(story_till_now, max_length=self.get_num_token(
-                story_till_now)+length, num_return_sequences=1)
-            story_till_now = genreate_user_sentence[0]['generated_text']
-            new_sentence_user = story_till_now[last_length:]
-
-            printj.red.bold_on_white(f'loop: {i}; check emotion')
-            # Emotion self.classifier for User
-            emotion_user = self.get_emotion(new_sentence_user)
-            if emotion_user['label'] == 'neutral':
-                show_emotion_user = False
-            else:
-                reaction_frequency = num_reactions/(i+1)
-                show_emotion_user = self.check_show_emotion(
-                    confidence_score=emotion_user['score'], frequency=reaction_frequency, w=reaction_weight)
-            if show_emotion_user:
-                num_reactions += 1
-
-            story_data.append({
-                'sentence': new_sentence_user,
-                'turn': 'user',
-                'emotion': emotion_user['label'],
-                'confidence_score': emotion_user['score'],
-            })
-            stats_dict['sentence_no'] = i
-            stats_dict['turn'] = 'user'
-            stats_dict['sentence'] = new_sentence_user
-            stats_dict['show_emotion'] = show_emotion_user
-            stats_dict['emotion_label'] = emotion_user['label']
-            stats_dict['emotion_score'] = emotion_user['score']
-            stats_dict['num_reactions'] = num_reactions
-            stats_dict['reaction_frequency'] = reaction_frequency
-            stats_dict['reaction_weight'] = reaction_weight
-            stats_df = pd.concat(
-                [stats_df, pd.DataFrame(stats_dict, index=[f'idx_{i}'])])
-            # Text generation for Robot
-            last_length = len(story_till_now)
-            printj.cyan(story_till_now)
-            printj.red.bold_on_white(
-                f'loop: {i}; generate robot text; length: {last_length}')
-            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_robot = story_till_now[last_length:]
-            emotion_robot = self.get_emotion(new_sentence_robot)
-
-            story_data.append({
-                'sentence': new_sentence_robot,
-                'turn': 'robot',
-                'emotion': emotion_robot['label'],
-                'confidence_score': emotion_robot['score'],
-            })
-            stats_dict['sentence_no'] = i
-            stats_dict['turn'] = 'robot'
-            stats_dict['sentence'] = new_sentence_robot
-            stats_dict['show_emotion'] = None
-            stats_dict['emotion_label'] = emotion_robot['label']
-            stats_dict['emotion_score'] = emotion_robot['score']
-            stats_dict['num_reactions'] = None
-            stats_dict['reaction_frequency'] = None
-            stats_dict['reaction_weight'] = None
-            stats_df = pd.concat(
-                [stats_df, pd.DataFrame(stats_dict, index=[f'idx_{i}'])])
-
-        return stats_df, story_till_now, story_data
-
-    def get_stats(self,
-                  story_till_now="Hello, I'm a language model,",
-                  num_generation=4,
-                  length=20, reaction_weight=-1, num_tests=2):
-        use_random_w = reaction_weight == -1
-        # self.stories = []
-        try:
-            num_rows = max(self.stats_df.story_id)+1
-        except Exception:
-            num_rows = 0
-        for story_id in range(num_tests):
-            if use_random_w:
-                # reaction_weight = np.random.random_sample()
-                reaction_weight = np.round(np.random.random_sample(), 1)
-            stats_df0, _story_till_now, story_data = self.auto_ist(
-                story_till_now=story_till_now,
-                num_generation=num_generations,
-                length=length, reaction_weight=reaction_weight)
-            stats_df0.insert(loc=0, column='story_id', value=story_id+num_rows)
-
-            # stats_df0['story_id'] = story_id
-            self.stats_df = pd.concat([self.stats_df, stats_df0])
-            printj.yellow(f'story_id: {story_id}')
-            printj.green(stats_df0)
-            self.stories.append(_story_till_now)
-            self.data.append(story_data)
-        self.stats_df = self.stats_df.reset_index(drop=True)
-        print(self.stats_df)
-
-    def save_stats(self, path='pandas_simple.xlsx'):
-        writer = pd.ExcelWriter(path, engine='xlsxwriter')
-
-        # Convert the dataframe to an XlsxWriter Excel object.
-        self.stats_df.to_excel(writer, sheet_name='IST')
-
-        # Close the Pandas Excel writer and output the Excel file.
-        writer.save()

story_gen_test.py

@@ -1,34 +0,0 @@
-# %%
-import printj
-from story_gen import StoryGenerator
-
-gen = StoryGenerator()
-# # %%
-# story_till_now, emotion = gen.story(story_till_now='Hello, I\'m a language model,', num_generation=3, length=10)
-# printj.purple(story_till_now)
-# printj.yellow(emotion)
-
-
-# %%
-gen.get_stats(story_till_now="For myriad of eons i’ve forgotten who I really was, harvesting the essence of all existence.",
-             length=10, num_generation=3, num_tests=50)
-
-# %%
-gen.save_stats('/home/jitesh/haru/ist/results/a.xlsx')
-
-
-
-
-# %%
-data=gen.stats_df[gen.stats_df.sentence_no==3]
-import seaborn as sns
-sns.set_theme(style="whitegrid")
-# ax = sns.violinplot(x="day", y="total_bill", data=tips)
-ax = sns.violinplot(x="reaction_weight", y="num_reactions", data=data).set_title('Analysing ProbabilityEmote (Max reactions=3)')
-# %%
-
-gen.stats_df[gen.stats_df.sentence_no==3]
-# %%
-import re
-len(re.findall(r'\w+', 'line ive '))
-# %%