Create app.py

app.py

@@ -0,0 +1,245 @@
+!pip -q install gradio
+
+import gradio as gr
+from transformers import pipeline, GPT2Tokenizer, AutoModelForSequenceClassification, AutoTokenizer
+from IPython.display import clear_output
+import joblib, torch
+
+############################################################################################
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+generator_name_0 = 'MasterAlex69/gpt2_edline'
+generator_name_1 = 'MasterAlex69/gpt2_edline_gan'
+
+generator_tokenizer_0 = GPT2Tokenizer.from_pretrained(generator_name_0)
+generator_tokenizer_1 = GPT2Tokenizer.from_pretrained(generator_name_1)
+
+generator_tokenizer_0.pad_token_id = generator_tokenizer_0.eos_token_id
+generator_tokenizer_1.pad_token_id = generator_tokenizer_1.eos_token_id
+
+generator_pipeline_0 = pipeline('text-generation', model = generator_name_0, tokenizer = generator_tokenizer_0, device = device)
+generator_pipeline_1 = pipeline('text-generation', model = generator_name_1, tokenizer = generator_tokenizer_1, device = device)
+
+generator_pkl_name_0 = 'generator_pkl_0.pkl'
+generator_pkl_name_1 = 'generator_pkl_1.pkl'
+
+joblib.dump(generator_pipeline_0, generator_pkl_name_0)
+joblib.dump(generator_pipeline_1, generator_pkl_name_1)
+
+generator_pipeline_0 = joblib.load('/content/' + generator_pkl_name_0)
+generator_pipeline_1 = joblib.load('/content/' + generator_pkl_name_1)
+
+############################################################################################
+
+discriminator_name_0 = 'MasterAlex69/bert_edline'
+discriminator_name_1 = 'MasterAlex69/bert_edline_gan'
+
+discriminator_0 = AutoModelForSequenceClassification.from_pretrained(discriminator_name_0, ).to(device)
+discriminator_1 = AutoModelForSequenceClassification.from_pretrained(discriminator_name_1).to(device)
+
+discriminator_tokenizer_0 = AutoTokenizer.from_pretrained(discriminator_name_0)
+discriminator_tokenizer_1 = AutoTokenizer.from_pretrained(discriminator_name_1)
+
+discriminator_pkl_name_0 = 'discriminator_pkl_0.pkl'
+discriminator_pkl_name_1 = 'discriminator_pkl_1.pkl'
+
+joblib.dump(discriminator_0, discriminator_pkl_name_0)
+joblib.dump(discriminator_1, discriminator_pkl_name_1)
+
+discriminator_0 = joblib.load('/content/' + discriminator_pkl_name_0)
+discriminator_1 = joblib.load('/content/' + discriminator_pkl_name_1)
+
+discriminator_pkl_tokenizer_name_0 = 'discriminator_tokenizer_pkl_0.pkl'
+discriminator_pkl_tokenizer_name_1 = 'discriminator_tokenizer_pkl_1.pkl'
+
+joblib.dump(discriminator_tokenizer_0, discriminator_pkl_tokenizer_name_0)
+joblib.dump(discriminator_tokenizer_1, discriminator_pkl_tokenizer_name_1)
+
+discriminator_tokenizer_0 = joblib.load('/content/' + discriminator_pkl_tokenizer_name_0)
+discriminator_tokenizer_1 = joblib.load('/content/' + discriminator_pkl_tokenizer_name_1)
+
+############################################################################################
+def generate_text_0():
+  return generator_pipeline_0("Строка состоит из символов", max_length = 225, truncation = False)[0]['generated_text']
+
+def generate_text_1():
+  return generator_pipeline_1("Строка состоит из символов", max_length = 225, truncation = False)[0]['generated_text']
+
+def discriminate_text_0(text):
+
+  inputs = discriminator_tokenizer_0(text
+                                   , return_tensors  = "pt"
+                                   , padding         = True
+                                   , truncation      = True).to(device)
+
+  result = discriminator_0(**inputs).logits[:, -1]
+  return torch.round(torch.sigmoid(result)).long().tolist()[0]
+
+def discriminate_text_1(text):
+
+  inputs = discriminator_tokenizer_1(text
+                                   , return_tensors  = "pt"
+                                   , padding         = True
+                                   , truncation      = True).to(device)
+
+  result = discriminator_1(**inputs).logits[:, -1]
+  return torch.round(torch.sigmoid(result)).long().tolist()[0]
+
+def d_test_0(count):
+
+  if count == "": count = 0
+  count = int(count)
+  if count == 0: return 'Введите количество итераций...'
+  if count > 256: return 'Максимальное количество итераций: 256.'
+  result = generator_pipeline_1(['Строка состоит из символов'] * count, max_length = 225, batch_size = count)
+  texts = [item['generated_text'] for sublist in result for item in sublist]
+  results = [discriminate_text_0(text) for text in texts]
+
+  i = 0
+  m = 0
+  for result in results:
+
+    real_result = 0
+    if get_correct_answer(texts[i]).find('(не корректно)') == -1: real_result = 1
+    if result == real_result: m += 1
+    i += 1
+
+  return str(round(m / count * 100, 2)) + '%'
+
+def d_test_1(count):
+
+  if count == "": count = 0
+  count = int(count)
+  if count == 0: return 'Введите количество итераций...'
+  if count > 256: return 'Максимальное количество итераций: 256.'
+  result = generator_pipeline_1(['Строка состоит из символов'] * count, max_length = 225, batch_size = count)
+  texts = [item['generated_text'] for sublist in result for item in sublist]
+  results = [discriminate_text_1(text) for text in texts]
+
+  i = 0
+  m = 0
+  for result in results:
+
+    real_result = 0
+    if get_correct_answer(texts[i]).find('(не корректно)') == -1: real_result = 1
+    if result == real_result: m += 1
+    i += 1
+
+  return str(round(m / count * 100, 2)) + '%'
+
+
+def test(count):
+
+  if count == "": count = 0
+
+  right = 0
+  count = int(count)
+  if count == 0: return 'Введите количество итераций...'
+  if count > 256: return 'Максимальное количество итераций: 256.'
+
+  result = generator_pipeline_1(['Строка состоит из символов'] * count, max_length = 225, batch_size = count)
+  texts = [item['generated_text'] for sublist in result for item in sublist]
+
+  for text in texts:
+    if get_correct_answer(text).find('не корректно') == -1: right += 1
+
+  return str(round(right / count * 100, 2)) + '%'
+
+def test_base(count):
+
+  if count == "": count = 0
+
+  right = 0
+  count = int(count)
+  if count == 0: return 'Введите количество итераций...'
+  if count > 256: return 'Максимальное количество итераций: 256.'
+
+  result = generator_pipeline_0(['Строка состоит из символов'] * count, max_length = 225, batch_size = count)
+  texts = [item['generated_text'] for sublist in result for item in sublist]
+
+  for text in texts:
+    if get_correct_answer(text).find('не корректно') == -1: right += 1
+
+  return str(round(right / count * 100, 2)) + '%'
+
+def get_correct_answer(t):
+
+  if len(t) == 0: return 'Введите задание...'
+
+  start_index   = t.find("(")
+  end_index     = t.find(")", start_index)
+  a = t[start_index + 8: end_index]
+
+  start_index   = t.find("д символов ")
+  end_index     = t.find(".", start_index)
+  c = t[start_index + 11 : end_index]
+
+  start_index   = t.find("а: ")
+  end_index     = t.find(".", start_index)
+  t = t[start_index + 3: end_index]
+
+  t = t.replace(c, '*')
+
+  max_length      = 0
+  current_length  = 0
+
+  for char in t:
+    if char == '*':
+
+      current_length += 1
+      if current_length > max_length: max_length = current_length
+
+    else: current_length = 0
+
+  return str(max_length) + (' (корректно)' if str(max_length) == a else ' (не корректно)')
+
+############################################################################################
+with gr.Blocks(theme = gr.themes.Monochrome()) as iface:
+
+  with gr.Row():
+    with gr.Column():
+      button_gen_0 = gr.Button("Сгенерировать задание (ДО)")
+      button_gen_0_output_text = gr.Textbox(label = "Результат генерации", interactive = False)
+      button_gen_0.click(fn = generate_text_0, outputs = button_gen_0_output_text)
+
+    with gr.Column():
+      button_gen_1 = gr.Button("Сгенерировать задание (ПОСЛЕ)")
+      button_gen_1_output_text = gr.Textbox(label="Результат генерации", interactive = False)
+      button_gen_1.click(fn = generate_text_1, outputs = button_gen_1_output_text)
+
+  with gr.Row():
+    with gr.Column():
+      button_test = gr.Button("Провести испытание (ДО) генератор")
+      test_input_text = gr.Textbox(label = "Количество итераций")
+      test_output_text = gr.Textbox(label = "Корректных заданий")
+      button_test.click(fn = test_base, inputs = test_input_text, outputs = test_output_text)
+
+    with gr.Column():
+      button_test_ = gr.Button("Провести испытание (ПОСЛЕ) генератор")
+      test_input_text_ = gr.Textbox(label = "Количество итераций")
+      test_output_text_ = gr.Textbox(label = "Корректных заданий")
+      button_test_.click(fn = test, inputs = test_input_text_, outputs = test_output_text_)
+
+  with gr.Row():
+    with gr.Column():
+      button_get_correct_answer = gr.Button("Получить правильный ответ")
+      get_correct_answer_input_text = gr.Textbox(label = "Задание")
+      get_correct_answer_output_text = gr.Textbox(label = "Ответ")
+      button_get_correct_answer.click(fn = get_correct_answer, inputs = get_correct_answer_input_text, outputs = get_correct_answer_output_text)
+
+  with gr.Row():
+    with gr.Column():
+      bn_test_d_0 = gr.Button("Провести испытание (ДО) дискриминатор")
+      bn_test_d_0_text_input = gr.Textbox(label = "Количество итераций")
+      bn_test_d_0_text_output = gr.Textbox(label = "Совпадений")
+      bn_test_d_0.click(fn = d_test_0, inputs = bn_test_d_0_text_input, outputs = bn_test_d_0_text_output)
+
+    with gr.Column():
+      bn_test_d_1 = gr.Button("Провести испытание (ПОСЛЕ) дискриминатор")
+      bn_test_d_1_text_input = gr.Textbox(label = "Количество итераций")
+      bn_test_d_1_text_output = gr.Textbox(label = "Совпадений")
+      bn_test_d_1.click(fn = d_test_1, inputs = bn_test_d_1_text_input, outputs = bn_test_d_1_text_output)
+
+clear_output()
+iface.launch(share = True, debug = False)