Add application file

app.py

@@ -0,0 +1,348 @@
+import datetime 
+import numpy as np
+import pandas as pd
+import re
+import json
+import os
+import glob
+
+import torch
+import torch.nn.functional as F
+from torch.optim import Adam
+from tqdm import tqdm
+from torch import nn
+from transformers import AutoTokenizer
+
+import argparse
+from bs4 import BeautifulSoup
+import requests
+
+def split_essay_to_sentence(origin_essay):
+    origin_essay_sentence = sum([[a.strip() for a in i.split('.')] for i in origin_essay.split('\n')], [])
+    essay_sent = [a for a in origin_essay_sentence if len(a) > 0]
+    return essay_sent
+
+def get_first_extraction(text_sentence):
+    row_dict = {}
+    for row in tqdm(text_sentence):
+        question = 'what is the feeling?'
+        answer = question_answerer(question=question, context=row)
+        row_dict[row] = answer
+    return row_dict
+
+
+class myDataset_for_infer(torch.utils.data.Dataset):
+    def __init__(self, X):
+        self.X = X
+
+    def __len__(self):
+        return len(self.X)
+
+    def __getitem__(self,idx):
+        sentences =  tokenizer(self.X[idx], return_tensors = 'pt', padding = 'max_length', max_length = 96, truncation = True)
+        return sentences
+    
+    
+def infer_data(model, main_feeling_keyword):
+    #ds = myDataset_for_infer()
+    df_infer = myDataset_for_infer(main_feeling_keyword)
+
+    infer_dataloader = torch.utils.data.DataLoader(df_infer, batch_size= 16)
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+    if device == 'cuda':
+        model = model.cuda()
+
+    result_list = []
+    with torch.no_grad():
+        for idx, infer_input in tqdm(enumerate(infer_dataloader)):
+            mask = infer_input['attention_mask'].to(device)
+            input_id = infer_input['input_ids'].squeeze(1).to(device)
+
+            output = model(input_id, mask)
+            result = np.argmax(output.logits, axis=1).numpy()
+            result_list.extend(result)
+    return result_list
+
+
+def get_word_emotion_pair(cls_model, origin_essay_sentence, idx2emo):
+
+    import re
+    def get_noun(sent):
+        return [w for (w, p) in pos_tag(word_tokenize(p_texts[0])) if len(w) > 1 and p in (['NN','N','NP'])]
+    def get_adj(sent):
+        return [w for (w, p) in pos_tag(word_tokenize(p_texts[0])) if len(w) > 1 and p in (['ADJ'])]
+    def get_verb(sent):
+        return [w for (w, p) in pos_tag(word_tokenize(p_texts[0])) if len(w) > 1 and p in (['VERB'])]
+
+    result_list = infer_data(cls_model, origin_essay_sentence)
+    final_result = pd.DataFrame(data = {'text': origin_essay_sentence , 'label' : result_list})
+    final_result['emotion'] = final_result['label'].map(idx2emo)
+    
+    final_result['noun_list'] = final_result['text'].map(get_noun)
+    final_result['adj_list'] = final_result['text'].map(get_adj)
+    final_result['verb_list'] = final_result['text'].map(get_verb)
+    
+    final_result['title'] = 'none'
+    file_made_dt = datetime.datetime.now()
+    file_made_dt_str = datetime.datetime.strftime(file_made_dt, '%Y%m%d_%H%M%d')
+    os.makedirs(f'./result/{nickname}/{file_made_dt_str}/', exist_ok = True)
+    final_result.to_csv(f"./result/{nickname}/{file_made_dt_str}/essay_result.csv", index = False)
+
+    return final_result, file_made_dt_str
+
+    return final_result, file_made_dt_str
+
+
+def get_essay_base_analysis(file_made_dt_str, nickname):
+    essay1 = pd.read_csv(f"./result/{nickname}/{file_made_dt_str}/essay_result.csv")
+    essay1['noun_list_len'] = essay1['noun_list'].apply(lambda x : len(x))
+    essay1['noun_list_uniqlen'] = essay1['noun_list'].apply(lambda x : len(set(x)))
+    essay1['adj_list_len'] = essay1['adj_list'].apply(lambda x : len(x))
+    essay1['adj_list_uniqlen'] = essay1['adj_list'].apply(lambda x : len(set(x)))
+    essay1['vocab_all'] = essay1[['noun_list','adj_list']].apply(lambda x : sum((eval(x[0]),eval(x[1])), []), axis=1)
+    essay1['vocab_cnt'] = essay1['vocab_all'].apply(lambda x : len(x))
+    essay1['vocab_unique_cnt'] = essay1['vocab_all'].apply(lambda x : len(set(x)))
+    essay1['noun_list'] = essay1['noun_list'].apply(lambda x : eval(x))
+    essay1['adj_list'] = essay1['adj_list'].apply(lambda x : eval(x))
+    d = essay1.groupby('title')[['noun_list','adj_list']].sum([]).reset_index()
+    d['noun_cnt'] = d['noun_list'].apply(lambda x : len(set(x)))
+    d['adj_cnt'] = d['adj_list'].apply(lambda x : len(set(x)))
+
+    # 문장 기준 최고 감정
+    essay_summary =essay1.groupby(['title'])['emotion'].value_counts().unstack(level =1)
+
+    emo_vocab_dict = {}
+    for k, v in essay1[['emotion','noun_list']].values:
+      for vocab in v:
+        if (k, 'noun', vocab) not in emo_vocab_dict:
+          emo_vocab_dict[(k, 'noun', vocab)] = 0
+
+        emo_vocab_dict[(k, 'noun', vocab)] += 1
+
+    for k, v in essay1[['emotion','adj_list']].values:
+      for vocab in v:
+        if (k, 'adj', vocab) not in emo_vocab_dict:
+          emo_vocab_dict[(k, 'adj', vocab)] = 0
+
+        emo_vocab_dict[(k, 'adj', vocab)] += 1
+    vocab_emo_cnt_dict = {}
+    for k, v in essay1[['emotion','noun_list']].values:
+      for vocab in v:
+        if (vocab, 'noun') not in vocab_emo_cnt_dict:
+          vocab_emo_cnt_dict[('noun', vocab)] = {}
+        if k not in vocab_emo_cnt_dict[( 'noun', vocab)]:
+          vocab_emo_cnt_dict[( 'noun', vocab)][k] = 0
+
+        vocab_emo_cnt_dict[('noun', vocab)][k] += 1
+
+    for k, v in essay1[['emotion','adj_list']].values:
+      for vocab in v:
+        if ('adj', vocab) not in vocab_emo_cnt_dict:
+          vocab_emo_cnt_dict[( 'adj', vocab)] = {}
+        if k not in vocab_emo_cnt_dict[( 'adj', vocab)]:
+          vocab_emo_cnt_dict[( 'adj', vocab)][k] = 0
+
+        vocab_emo_cnt_dict[('adj', vocab)][k] += 1
+
+    vocab_emo_cnt_df = pd.DataFrame(vocab_emo_cnt_dict).T
+    vocab_emo_cnt_df['total'] = vocab_emo_cnt_df.sum(axis=1)
+    # 단어별 최고 감정 및 감정 개수
+    all_result=vocab_emo_cnt_df.sort_values(by = 'total', ascending = False)
+
+    # 단어별 최고 감정 및 감정 개수 , 형용사 포함 시
+    adj_result=vocab_emo_cnt_df.sort_values(by = 'total', ascending = False)
+
+    # 명사만 사용 시
+    noun_result=vocab_emo_cnt_df[vocab_emo_cnt_df.index.get_level_values(0) == 'noun'].sort_values(by = 'total', ascending = False)
+
+    final_file_name = f"essay_all_vocab_result.csv"
+    adj_file_name = f"essay_adj_vocab_result.csv"
+    noun_file_name = f"essay_noun_vocab_result.csv"
+    
+    os.makedirs(f'./result/{nickname}/{file_made_dt_str}/', exist_ok = True)
+    
+    all_result.to_csv(f"./result/{nickname}/{file_made_dt_str}/essay_all_vocab_result.csv", index = False)
+    adj_result.to_csv(f"./result/{nickname}/{file_made_dt_str}/essay_adj_vocab_result.csv", index = False)
+    noun_result.to_csv(f"./result/{nickname}/{file_made_dt_str}/essay_noun_vocab_result.csv", index = False)
+    
+    return all_result, adj_result, noun_result, essay_summary, file_made_dt_str
+
+
+
+from transformers import AutoModelForSequenceClassification
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+def all_process(origin_essay, nickname):
+    essay_sent =split_essay_to_sentence(origin_essay)
+    idx2emo = {0: 'Anger', 1: 'Sadness', 2: 'Anxiety', 3: 'Hurt', 4: 'Embarrassment', 5: 'Joy'}
+    tokenizer = AutoTokenizer.from_pretrained('seriouspark/xlm-roberta-base-finetuning-sentimental-6label')
+    cls_model = AutoModelForSequenceClassification.from_pretrained('seriouspark/xlm-roberta-base-finetuning-sentimental-6label')
+    
+    final_result, file_name_dt = get_word_emotion_pair(cls_model, essay_sent, idx2emo)
+    all_result, adj_result, noun_result, essay_summary, file_made_dt_str = get_essay_base_analysis(file_name_dt, nickname)
+    
+    summary_result = pd.concat([adj_result, noun_result]).fillna(0).sort_values(by = 'total', ascending = False).fillna(0).reset_index()[:30]
+    with open(f'./result/{nickname}/{file_name_dt}/summary.json','w') as f:
+        json.dump( essay_summary.to_json(),f)
+    with open(f'./result/{nickname}/{file_made_dt_str}/all_result.json','w') as f:
+        json.dump( all_result.to_json(),f)    
+    with open(f'./result/{nickname}/{file_made_dt_str}/adj_result.json','w') as f:
+        json.dump( adj_result.to_json(),f)  
+    with open(f'./result/{nickname}/{file_made_dt_str}/noun_result.json','w') as f:
+        json.dump( noun_result.to_json(),f)  
+    #return essay_summary, summary_result
+    total_cnt = essay_summary.sum(axis=1).values[0]
+    essay_summary_list = sorted(essay_summary.T.to_dict()['none'].items(), key = lambda x: x[1], reverse =True)
+    essay_summary_list_str = ' '.join([f'{row[0]} {int(row[1]*100 / total_cnt)}%' for row in essay_summary_list])
+    summary1 = f"""{nickname}, Your sentiments in your writting are [{essay_summary_list_str}] """
+
+    return summary1
+
+def get_similar_vocab(message):
+    if (len(message) > 0) & (len(re.findall('[A-Za-z]+', message))> 0):
+        vocab = message
+        all_dict_url = f"https://www.dictionary.com/browse/{vocab}"
+        response = requests.get(all_dict_url)
+
+        html_content = response.text
+        # BeautifulSoup로 HTML 파싱
+        soup = BeautifulSoup(html_content, 'html.parser')
+        result = soup.find_all(class_='ESah86zaufmd2_YPdZtq')
+        p_texts = [p.get_text() for p in soup.find_all('p')]
+        whole_vocab = sum([ [word for word , pos in pos_tag(word_tokenize(text)) if pos in ['NN','JJ','NNP','NNS']] for text in p_texts],[])
+
+        similar_words_final = Counter(whole_vocab).most_common(10)
+        return [i[0] for i in similar_words_final]
+    
+    else: 
+        return message
+    
+def get_similar_means(vocab):
+    all_dict_url = f"https://www.dictionary.com/browse/{vocab}"
+    response = requests.get(all_dict_url)
+    html_content = response.text
+    soup = BeautifulSoup(html_content, 'html.parser')
+    result = soup.find_all(class_='ESah86zaufmd2_YPdZtq')
+    p_texts = [p.get_text() for p in soup.find_all('p')]
+    return p_texts[:10]
+
+
+info_dict = {}
+def run_all(message, history):
+    global info_dict
+    if message.find('NICKNAME:')>=0:
+        global nickname 
+        nickname = message.replace('NICKNAME','').replace(':','').strip()
+        #global nickname
+        info_dict[nickname] = {}
+        return f'''Good [{nickname}]!! Let's start!. 
+Give me a vocabulary in your mind.
+\n\n\nwhen you type the vocab, please include \"VOCAB: \" 
+e.g <VOCAB: orange>
+'''
+    try :
+        #print(nickname)
+        if message.find('VOCAB:')>=0:
+            clear_message = message.replace('VOCAB','').replace(':','').strip()
+            info_dict[nickname]['main_word'] = clear_message
+            vocab_mean_list = []
+            similar_words_final = get_similar_vocab(clear_message)
+            print(similar_words_final)
+            similar_words_final_with_main = similar_words_final + [clear_message]
+            if len(similar_words_final_with_main)>0:
+                for w in similar_words_final_with_main:
+                    temp_means = get_similar_means(w)
+                    vocab_mean_list.append(temp_means[:2])
+                fixed_similar_words_final = list(set([i for i in sum(vocab_mean_list, []) if len(i) > 10]))[:10]
+
+
+                word_str = ' \n'.join([str(idx) + ") " + i for idx, i in enumerate(similar_words_final, 1)])
+                sentence_str = ' \n'.join([str(idx) + ") " + i for idx, i in enumerate(fixed_similar_words_final, 1)])
+
+                return f'''Let's start writing with the VOCAB<{clear_message}>! 
+First, how about those similar words?
+{word_str} \n
+The word has these meanings. 
+{sentence_str}\n
+Pick and type one meaning of these list.
+\n\n\n When you type in, please include \"SENT:\", like this.
+\n e.g. <SENT: a globose, reddish-yellow, bitter or sweet, edible citrus fruit. >
+    '''
+            else:
+                return 'Include \"VOCAB:\" please (VOCAB: orange)'
+
+        elif message.find('SENT:')>=0:
+            clear_message = message.replace('SENT','').replace(':','').strip()
+            info_dict[nickname]['selected_sentence'] = clear_message
+            return f'''You've got [{clear_message}]. 
+\n With this sentence, we can make creative short writings
+\n\n\n Include \"SHORT_W: \", please.
+\n e.g <SHORT_W: Whenever I smell the citrus, I always reminise him, first>
+
+            '''
+
+        elif message.find('SHORT_W:')>=0:
+            clear_message = message.replace('SHORT_W','').replace(':','').strip()
+            info_dict[nickname]['short_contents'] = clear_message
+
+            return f'''This is your short sentence <{clear_message}> .
+\n With this sentence, let's step one more thing, please write long sentences more than 500 words.
+\n\n\n When you input, please include\"LONG_W: \" like this.
+\n e.g <LONG_W: He enjoyed wearing blue T-shirts at the gym, but the intense citrus scent he used on his clothes was noticeably excessive  ... >
+            '''
+        elif message.find('LONG_W:')>=0:
+            long_message = message.replace('LONG_W','').replace(':','').strip()
+
+            length_of_lm = len(long_message)
+            if length_of_lm >= 500:
+                info_dict['long_contents'] = long_message
+                os.makedirs(f"./result/{nickname}/", exist_ok = True)
+                with open(f"./result/{nickname}/contents.txt",'w') as f:
+                    f.write(long_message)
+                return f'Your entered text is {length_of_lm} characters. This text is worth analyzing. If you wish to start the analysis, please type "START ANALYSIS"'
+            else :
+                return f'The text you have entered is {length_of_lm} characters. It\'s a bit short for analysis. Could you please provide a bit more sentences'
+
+        elif message.find('START ANALYSIS')>=0:
+            with open(f"./result/{nickname}/contents.txt",'r') as f:
+                    orign_essay = f.read()
+            summary = all_process(orign_essay, nickname)
+            
+            #print(summary)
+            return summary
+        else:
+            return 'Please start from the beginning'
+
+    except:
+        return 'An error has occurred. Restarting from the beginning. Please enter your NICKNAME:'       
+                
+        
+import gradio as gr
+import requests
+history = []
+info_dict = {}
+iface = gr.ChatInterface(
+    fn=run_all,
+    chatbot = gr.Chatbot(),
+    textbox = gr.Textbox(placeholder="Please enter including the chatbot's request prefix.", container = True, scale = 7),
+    title = 'MooGeulMooGeul',
+    description = "Please start by choosing your nickname. Include 'NICKNAME: ' in your response",
+    theme = 'soft',
+    examples = ['NICKNAME: bluebottle',
+                'VOCAB: orange',
+                'SENT: a globose, reddish-yellow, bitter or sweet, edible citrus fruit.',
+                'SHORT_W: Whenever I smell the citrus, I always reminise him, first',
+                '''LONG_W: Whenever I smell citrus, I always think of him. He used to come to the gym wearing a blue T-shirt, often spraying a strong citrus scent.
+                That scent was quite distinctive, letting me know when he was passing by. 
+                I usually arrived to work out between 7:00 and 7:30 AM, and interestingly, he would arrive about 10 minutes after me.
+                On days I came early, he did too; and when I was late, he was also late.
+                The citrus scent from his body was always so intense, as if he had just sprayed it.'''
+               ],
+    cache_examples = False,
+    retry_btn = None,
+    undo_btn = 'Delete Previous',
+    clear_btn = 'Clear',
+                   
+)
+iface.launch(share=True)