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app.py

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+# %%
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+from nltk.tokenize import sent_tokenize
+from konlpy.tag import Kkma
+import gc
+
+import nltk
+nltk.download('punkt')
+
+# from PyKakao import KoGPT
+# kogpt_api = KoGPT(service_key = "")
+import openai
+openai.api_key = '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'
+gpt2_tokenizer = AutoTokenizer.from_pretrained('gpt2')
+
+import os
+# if not(os.environ['JAVA_HOME']):
+#     os.environ['JAVA_HOME'] = r'C:\Program Files\Java\jdk-17\bin\server'
+
+#en2ko = 'alphahg/m2m100_418M-finetuned-en-to-ko-4770260'#'alphahg/mbart-large-50-finetuned-en-to-ko-8603428-finetuned-en-to-ko-9914408'
+en2ko = 'alphahg/mbart-large-50-finetuned-en-to-ko-8603428-finetuned-en-to-ko-9914408'
+ko2en = 'alphahg/opus-mt-ko-en-finetuned-ko-to-en-2780616'
+ensum = 'allenai/led-large-16384-arxiv'
+kosum = 'alphahg/pko-t5-small-finetuned-paper-4564652' #'lcw99/t5-base-korean-text-summary'
+
+kkma = Kkma()
+#en_pipe = pipeline('translation', model=en2ko, tokenizer=en2ko, src_lang = "en", tgt_lang = "ko", device_map="auto")
+en2ko_model = AutoModelForSeq2SeqLM.from_pretrained(en2ko)
+
+en_pipe = pipeline('translation', model=en2ko_model, tokenizer=en2ko, src_lang = "en_XX", tgt_lang = "ko_KR", device="cuda:0")
+ko_pipe = pipeline('translation', model=ko2en, tokenizer=ko2en, device="cuda:0")
+style_pipe = pipeline('translation', model=en2ko_model, tokenizer=en2ko, src_lang = "ko_KR", tgt_lang = "ko_KR", device="cuda:0")
+
+en_sum = pipeline('summarization', model=ensum, tokenizer=ensum, device="cuda:1")
+ko_sum = pipeline('summarization', model=kosum, tokenizer=kosum, device="cuda:1")
+
+def len_tokens(text, pipe):
+    return len(pipe.tokenizer(text)['input_ids'])
+
+def split_sent(sentences, pipe, max_len=256):
+    if not sentences:
+        return []
+
+    paragraphs = []
+    example = sentences[0]
+    for i in range(1, len(sentences)):
+        if len_tokens(example + ' ' + sentences[i], pipe) > max_len:
+            paragraphs.append(example)
+            example = sentences[i]
+        else:
+            example += ' ' + sentences[i]
+    
+    paragraphs.append(example)
+
+    return paragraphs
+
+# chatbot = Chatbot({
+#   #"session_token": "eyJhbGciOiJkaXIiLCJlbmMiOiJBMjU2R0NNIn0..hV_ujfbYLwBgI-g6.zQW0evUrpYfli2cujTFp1ie5PhthUZayoSY2Chb1Eb8Ow3t6l2-NUwGJcYyxVKQS0aITN3-ph-KzPysnu7dCF9KrC-22DZzs1zMFm3PHEkjb4jD69qndcFEGGH8y4SejfYwvdj4wfKmVnGo3XTNZ31qPSDA2PBoaOMpxWABSqWULMJbS-_Y--wd0YhsqMFlkCQpXyfxSf9yxXlPYt0HR_NgupoBXP-WVYbODSUYFVqa3IsScbSPS-mUY0YrAb8AJUkvej7HiSCN9onyThgTtZgZklwqpB4FesJaC6R3nSXSg5cKDDupGVBUQlPRiemacCV6tXnSC-bCtN9a-l9RRqtX_FJNP8T7Kb75ktuedKKrXXTmk3x7hz_RhYhZ4wXFkbqXexZQXTfQoI2vKlLN73EHBlJOqsDLnOP7zT4Vr2RpBbk1HK5D_uHh5x1X3aBHslHfEQpjjZKiMs0to9DwKzSNHXlNmCeGjT9ZzKVsWYCiseO20IlKxQ63Q_nIbi-6y8e6LWw9O82ESKkkRe8kN-CzGxakKJegmHKQGRPZu9ZEIqwWYqlnahVyWRFOtjfMNN3ncGbQGi54VMyfSqmSvPXecaxsVNzl00gHvmCFBFJBDXM2GTsvEzQsJi1MLyopXtSuiU1anL_kC1eMvew61vd3TtC97ZwlQLaWc6dT14p5NdJpN9ihFpgtxMP1rcQhNTc5fo9BBoKrO4yGOuy0wixJs6ORVdY2o3c653X8PFmrso1XaV3KpBaSmcvszIhL1anJTA8SpnPNRmksEFONcX8AfpQ-4WbckCS737TZYCDulVtvukyVAbtq9cEQP5kXXAGOWKg5lX4nRFynM83f8P-XGIg-XUGE99NRIOCBo28cr04fWFOaJOyHf9eP6Rx5zjNv1qwp4FxhVVP9jlmSTfu97CZSR91L-k8V6jVgbj8F6YUZ6iiu51kaOAqf5de4EUncSFyGLuJfCGTJTPSYYl1lnR6bSfTVHKwP28YzzcU2myMM5B0ZXDwydD900TYXZOCxxLPUbu5-G3roR2KZnuWLXFOiafAvDx-LHYUHSWQZ9ouWcDaQBNsXmfTZtIWHQ8aTZwlNEnN4-uFdlk2Lm35qp1v-8Fp_3aXGQ3CrTy-ryMV0rUPTSMCEA8gVA_mD40zV6Wcb4asc3zsYAuomQ3Iu4iB5wyWGxUIJVzl1C9QaPpAx7vp5u7w-0_rtocVVXFRTZ8aSxNS3QAd62TbVyToIOrsvp4kOWDcqhNp5QBAsJtES9pbO9fiy_SJS83SFMliSFd-jhXfKu0kUYIUb9yaN5QC6eEpgJ7KzhwTcNDtoqyBKMyVTSdUXA9P2Yv2e4r-BVnxlW0RxknQdesK-wZrwuAZt_bnLaHSqFzyWz5AE7pukTBQ2QdVoity_tVURzhTcINh6rvPywm2IVl1gC3FjfhQVfTvHWFtUzNqLN4yUfI0Tc1mGHQlYuxZ_yux2B8HeYb_cyb1rR_mwDiOs3PKOnhfNRdXqXf6RWr7KdjNc0k-CMm13DAYQggmFCmEZW20FiwalKqVq3nFTFDhfp5mxtr0sLCVxGA3eTqC6_i2TAVGqDLjxzfz5WiK7J3FAN2_kmEZLBVXHabwa9kKyCzcgCx6FrxaFidskO6t4dWu3wok95hXMae0Z4ZFs7HVNisM1pkRm7LE3XdvnslKHAJkPr57HsFdlQITJRSx3Tg0EN1LUt80hKx8VGXPv7zBeXP5lni2ixpglMQmiKLiszowGoqu2oJPwougueu5Bj4BLhmoqK8DCtdxl3MYAyxLWWStXQqcEJQw7koYmPNwr4BzI9cQVk81LbPwrXBbJfR7G14e5qV0lULfuU5qVfNt7DU6FbwXmzv6qFI-jOClLzSTKpFzp51wQQ5fh2REs6CPJlL-kiyomJPXcqSeezDCLLwWjI_vIyODFkzt91l-dmFriu3HMkMC1v29AJlfPA_avSiJzJDEI7rb6AbEyT6piqp1TYlWMkI_rJCsCZXIb10Rjd2y9sR-Dz3_FZzRvJUA7BfRlP7Bf04HnYsyMRoJilbuyQ5fB0B2L2nxjYY2zoHJ_x6HTS6tcrAijOO4FSSQngWD9iTKCm6pjW3aZjFyXyjmP82S3VnhEyON390aIL7j9Y0wGnHzOkn54OfyxxGeo2mFAIv9kthL_Fi8d9G_rvvQOBUM2a7kjF5-n8wby0YDujoRl0ETg379HyMVf7F2BHWQ8nAbICRxWZ7EzPLwzrVjPiQVPZklkrVYgEmGxZDrgEG8IeNi7FMgGruaQ1tENczRMXzaApK8k6-FXKhfFIV7dN95tP4k6tnnxRFoMAUWcXwQCzRH8YhID36TAUFdBQ-c52MTogPo1Rki1N49j_e7Mph1OABQX2Fw9-CukT6reQkp3nGrwi0IKnKoyGhHOBHK3kzQwINfjOBbNpOjP-6MX_9kiRTstN2GLte8w0QJQVl84o8ACTjV8N4rhI6xyLKIvqoyZ6jNO3SYs8fEutmZO8-qB0iksIiQHupxQgcmgbAyM.KxAbJWqMvwm0PYtq7MuR6A"
+
+# }, conversation_id=None, parent_id=None) # You can start a custom conversation
+# %%
+def translate(text, lang, gpt_fix=False):
+    from_en = False if lang == '한영' else True
+    sentences = sent_tokenize(text) if from_en else kkma.sentences(text)
+    #print(sentences)
+    if not sentences:
+        return ''
+
+    paragraphs = split_sent(sentences, en_pipe, max_len=180) if from_en else split_sent(sentences, ko_pipe)
+    #print(paragraphs)
+    
+    ret = []
+    for text in paragraphs:
+        result = en_pipe(text) if from_en else ko_pipe(text)
+        ret.append(result[0]['translation_text'])
+
+    translated = ' '.join(ret)
+    gc.collect()
+
+    if gpt_fix:
+        if lang == '한영':
+            prompt = 'Improve given formal article without adding:'
+        elif lang == '영한':
+            prompt = "추가적인 내용없이 주어진 글을 개선해:"
+
+        def fix_sent(sent):
+            number_of_tokens = len(gpt2_tokenizer(sent)['input_ids'])
+
+            response = openai.Completion.create(
+                model="text-davinci-003",
+                prompt=prompt+'\n'+sent,
+                temperature=0,
+                max_tokens=number_of_tokens+128,
+                top_p=1.0,
+                frequency_penalty=0.0,
+                presence_penalty=0.0
+            )
+
+            return response.choices[0].text.strip()
+
+        # def fix_sent(sent):
+        #     generated = kogpt_api.generate(prompt+'\n'+sent, max_tokens=256)
+        #     return generated['generations'][0]['text']
+        
+        translated = fix_sent(translated)
+
+    return translated
+
+#%%
+def translate_with_sum(text, lang, gpt_fix=False):
+    from_en = False if lang == '한영' else True
+
+    if lang == '영한':
+        summary = en_sum(text, max_length=int(len_tokens(text, en_sum)/2)+32)
+        text = summary[0]['summary_text']
+
+    sentences = sent_tokenize(text) if from_en else kkma.sentences(text)
+    #print(sentences)
+    if not sentences:
+        return ''
+
+    paragraphs = split_sent(sentences, en_pipe if from_en else ko_pipe)
+    #print(paragraphs)
+
+    ret = []
+    for text in paragraphs:
+        result = en_pipe(text) if from_en else ko_pipe(text)
+        ret.append(result[0]['translation_text'])
+
+    summarized = ' '.join(ret)
+    if lang == '한영':
+        summary = en_sum(summarized, max_length=int(len_tokens(summarized, en_sum)/2)+32)
+        return summary[0]['summary_text']
+
+    gc.collect()
+    return summarized
+
+def summarize(text, lang):
+    if lang == 'Korean':
+        summarizer = ko_sum
+    elif lang == 'English':
+        summarizer = en_sum
+
+    summary = summarizer(text, max_length=int(len_tokens(text, summarizer) * 0.7))[0]['summary_text']
+    return summary
+
+def translate_styleonly(text):
+    sentences = kkma.sentences(text)
+    paragraphs = split_sent(sentences, style_pipe, max_len=180)
+    #print(paragraphs)
+
+    ret = []
+    for text in paragraphs:
+        result = style_pipe(text)
+        ret.append(result[0]['translation_text'])
+
+    translated = ' '.join(ret)
+    gc.collect()
+
+    return translated
+
+# %%
+interface1 = gr.Interface(fn=translate, inputs=["text", gr.Radio(["영한", "한영"], value='영한'), 'checkbox'], outputs="text", batch=True, max_batch_size=8)
+interface2 = gr.Interface(fn=translate_with_sum, inputs=["text", gr.Radio(["영한", "한영"], value='영한')], outputs="text", batch=True, max_batch_size=8)
+parallel_interface = gr.Parallel(interface1, interface2)
+
+summarize_interface = gr.Interface(fn=summarize, inputs=["text", gr.Radio(["Korean", "English"], value='Korean')], outputs="text", batch=True, max_batch_size=8)
+style_interface = gr.Interface(fn=translate_styleonly, inputs=["text"], outputs="text", batch=True, max_batch_size=8)
+#%%
+demo = gr.TabbedInterface([parallel_interface, summarize_interface, style_interface], ['번역 및 요약', '요약', '스타일 번역'], css="footer {visibility: hidden}") # '요약'
+demo.queue()
+demo.launch(share=True) # Share the demo
+# %%

requirements.txt

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+nltk
+konlpy
+openai