adds simple story generator

app.py

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+import streamlit as st
+
+from transformers import pipeline, set_seed
+
+import time, sys
+
+from transformers import pipeline, set_seed
+import printj
+start = time.time()
+generator = pipeline('text-generation', model='gpt2')
+classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
+process_time = time.time()-start
+print(f'Process Time: {process_time}')
+# set_seed(42)
+# sys.exit()
+
+def story(story_till_now, num_generation, length):
+    last_length = 0
+
+    # story_till_now = "Hello, I'm a language model,"
+    for  i in range(num_generation):
+        # start = time.time()
+        results = generator(story_till_now, max_length=30+length*i, num_return_sequences=1)
+        # process_time = time.time()-start
+        # print(f'Process Time: {process_time}, avg. time: {process_time/num_return_sequences}')
+        story_till_now = results[0]['generated_text']
+        new_sentence = story_till_now[last_length:]
+        emotion = classifier(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}')
+        last_length = len(story_till_now)
+    return story_till_now
+story_till_now=st.text_input(label='First Sentence', value='Hello, I\'m a language model,', placeholder="Start writing your story...")
+
+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)
+story_till_now=story(story_till_now, num_generation, length)
+st.text(story_till_now)

requirements.txt

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+transformers==4.16.2
+printj==0.1.0
+torch==1.10.2
+torchvision==0.11.3