Update app.py

app.py

@@ -2,76 +2,75 @@ import requests
 from bs4 import BeautifulSoup
 import pandas as pd
 import gradio as gr
-import logging
 
-# Set up logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-logger = logging.getLogger()
-
-def scrape_kosdaq():
-    url = "https://finance.naver.com/sise/sise_quant.naver?sosok=1"
-    headers = {
-        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
-    }
-
-    logger.info("Sending request to URL: %s", url)
-    response = requests.get(url, headers=headers)
+def fetch_kosdaq_data():
+    # 네이버 증권 코스닥 URL
+    url = "https://finance.naver.com/sise/sise_rise.naver?sosok=1"
     
-    if response.status_code != 200:
-        logger.error("Failed to fetch data. Status code: %d", response.status_code)
-        return f"Error: Unable to fetch data (status code: {response.status_code})"
+    try:
+        # 웹 페이지 요청
+        response = requests.get(url)
+        response.raise_for_status()
+        soup = BeautifulSoup(response.content, "html.parser")
 
-    soup = BeautifulSoup(response.content, 'html.parser')
+        # 테이블 데이터 추출
+        table = soup.find("table", class_="type_2")
+        rows = table.find_all("tr")
 
-    table = soup.find("table", {"class": "type_2"})
-    if not table:
-        logger.error("Failed to find the data table in the HTML.")
-        return "Error: Unable to find the data table."
+        data = []
+        for row in rows:
+            columns = row.find_all("td")
+            if len(columns) > 0:
+                # 데이터 파싱
+                rank = columns[0].get_text(strip=True)
+                name = columns[1].get_text(strip=True)
+                current_price = columns[2].get_text(strip=True)
+                diff = columns[3].get_text(strip=True)
+                change_rate = columns[4].get_text(strip=True)
+                volume = columns[5].get_text(strip=True)
+                buy_price = columns[6].get_text(strip=True)
+                sell_price = columns[7].get_text(strip=True)
+                buy_total = columns[8].get_text(strip=True)
+                sell_total = columns[9].get_text(strip=True)
+                per = columns[10].get_text(strip=True)
+                roe = columns[11].get_text(strip=True)
 
-    rows = table.find_all("tr")
-    data = []
-    
-    for row in rows:
-        cols = row.find_all("td")
-        cols = [col.text.strip() for col in cols]
-        if len(cols) > 1:
-            data.append(cols)
+                data.append([
+                    rank, name, current_price, diff, change_rate, 
+                    volume, buy_price, sell_price, buy_total, 
+                    sell_total, per, roe
+                ])
 
-    if not data:
-        logger.error("No data found in the table.")
-        return "Error: No data found in the table."
+        # DataFrame 생성
+        columns = ["Rank", "Name", "Current Price", "Difference", "Change Rate", 
+                   "Volume", "Buy Price", "Sell Price", "Buy Total", 
+                   "Sell Total", "PER", "ROE"]
+        df = pd.DataFrame(data, columns=columns)
+        return df
 
-    # Define column names based on the structure
-    columns = [
-        "N", "종목명", "현재가", "전일비", "등락률", "거래량", "거래대금", "매수호가",
-        "매도호가", "시가총액", "PER", "ROE"
-    ]
+    except Exception as e:
+        print(f"Error occurred: {e}")
+        return None
 
-    df = pd.DataFrame(data, columns=columns[:len(data[0])])
-    
-    logger.info("Successfully scraped data with %d rows.", len(df))
-    return df
+def display_data():
+    df = fetch_kosdaq_data()
+    if df is not None:
+        return df
+    else:
+        return "Failed to fetch data. Please check the logs."
 
-def interface_function():
-    try:
-        df = scrape_kosdaq()
-        if isinstance(df, str):  # If error message
-            return df
+# Gradio 인터페이스 설정
+def gradio_interface():
+    with gr.Blocks() as demo:
+        gr.Markdown("# 네이버 증권 코스닥 데이터 스크래핑")
+        fetch_button = gr.Button("데이터 가져오기")
+        output_table = gr.Dataframe(headers="auto")
 
-        logger.info("Displaying scraped data to user.")
-        return df.to_csv(index=False)
-    except Exception as e:
-        logger.exception("An error occurred during scraping.")
-        return f"An error occurred: {str(e)}"
+        fetch_button.click(fn=fetch_kosdaq_data, inputs=[], outputs=output_table)
+
+    return demo
 
-def display_interface():
-    gr.Interface(
-        fn=interface_function,
-        inputs=[],
-        outputs="text",
-        title="네이버 증권 코스닥 스크래퍼",
-        description="네이버 증권에서 코스닥 데이터를 스크래핑하여 CSV 형식으로 반환합니다."
-    ).launch()
+demo = gradio_interface()
 
 if __name__ == "__main__":
-    display_interface()
+    demo.launch()