update model and precondition logic

app.py

@@ -1,15 +1,100 @@
 import gradio as gr
+from newspaper import Article
+from newspaper import Config
+
+from transformers import pipeline
+import requests
+from bs4 import BeautifulSoup
+import re
+
+from bs4 import BeautifulSoup as bs
+import requests
+
+from transformers import PreTrainedTokenizerFast, BartForConditionalGeneration
+#  Load Model and Tokenize
+def get_summary(input_text):
+    tokenizer = PreTrainedTokenizerFast.from_pretrained("ainize/kobart-news")
+    summary_model = BartForConditionalGeneration.from_pretrained("ainize/kobart-news")
+    input_ids = tokenizer.encode(input_text, return_tensors="pt")
+    summary_text_ids = summary_model.generate(
+        input_ids=input_ids,
+        bos_token_id=summary_model.config.bos_token_id,
+        eos_token_id=summary_model.config.eos_token_id,
+        length_penalty=2.0,
+        max_length=142,
+        min_length=56,
+        num_beams=4,
+    )
+    return tokenizer.decode(summary_text_ids[0], skip_special_tokens=True)
+
+
+
+USER_AGENT = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:78.0) Gecko/20100101 Firefox/78.0'
+config = Config()
+config.browser_user_agent = USER_AGENT
+config.request_timeout = 10
+
+class news_collector:
+    def __init__(self):
+        self.examples = []
+
+    def get_new_parser(self, url):
+        article = Article(url, language='ko')
+        article.download()
+        article.parse()
+        return article
+
+    def get_news_links(self, page=''):
+        url = "https://news.daum.net/breakingnews/economic"
+        response = requests.get(url)
+        html_text = response.text
+
+        soup = bs(response.text, 'html.parser')
+        news_titles = soup.select("a.link_txt")
+        links = [item.attrs['href'] for item in news_titles ]
+        https_links = [item for item in links if item.startswith('https') == True]
+        https_links
+        return https_links[:2]
+
+
+    def update_news_examples(self):
+        news_links = self.get_news_links()
+        for news_url in news_links:
+            article = self.get_new_parser(news_url)
+            self.examples.append(get_summary(article.text))
+
+
+
+title = "균형잡힌 뉴스 읽기 (Balanced News Reading)"
+
 
-def greet(name):
-    return "Hello " + name + "!!"
 
 with gr.Blocks() as demo:
+    news = news_collector()
+
     gr.Markdown(
     """
     # 균형잡힌 뉴스 읽기 (Balanced News Reading)
+
+    긍정적인 기사와 부정적인 기사의 균형을 보며 뉴스를 읽을 수 있습니다. 데모를 실행하면 데모 실행 날짜의 Daum뉴스를 `Example`에 가져옵니다.
+    모델에서 사용할 수 있는 길이보다 긴 기사가 있기 때문에 기사내용을 요약한 후 요역된 내용을 `Example`에 추가합니다.
+
+    뉴스기사를 선택하고 `Submit`버튼을 누르면 기사의 감정평가를 확인할 수 있습니다.
     """)
-    iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-    # iface.launch()
+    news.update_news_examples()
+
+    gr.load("models/gabrielyang/finance_news_classifier-KR_v7",
+            inputs = gr.Textbox( placeholder="뉴스 기사 내용을 입력하세요." ),
+            examples=news.examples)
+
+    # gr.Examples(
+    #     examples=[
+    #         ["images/demo1.jpg"],
+    #         ["images/demo2.jpg"],
+    #         ["images/demo4.jpg"],
+    #     ],
+
+
 
 
 if __name__ == "__main__":