Upload 2 files

app (28).py

@@ -0,0 +1,82 @@
+import requests
+from bs4 import BeautifulSoup
+import pandas as pd
+import gradio as gr
+
+def fetch_kosdaq_data():
+    # 네이버 증권 코스닥 URL
+    url = "https://finance.naver.com/sise/sise_rise.naver?sosok=1"
+    
+    try:
+        # 웹 페이지 요청
+        response = requests.get(url)
+        response.raise_for_status()
+        soup = BeautifulSoup(response.content, "html.parser")
+
+        # 테이블 데이터 추출
+        table = soup.find("table", class_="type_2")
+        rows = table.find_all("tr")
+
+        data = []
+        for row in rows:
+            columns = row.find_all("td")
+            if len(columns) >= 12:  # 12개 열이 있는 행만 처리
+                try:
+                    # 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)
+
+                    data.append([
+                        rank, name, current_price, diff, change_rate, 
+                        volume, buy_price, sell_price, buy_total, 
+                        sell_total, per, roe
+                    ])
+                except Exception as e:
+                    print(f"Error parsing row: {e}")
+                    continue
+
+        # 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
+
+    except Exception as e:
+        print(f"Error occurred: {e}")
+        return None
+
+def display_data():
+    df = fetch_kosdaq_data()
+    if df is not None and not df.empty:
+        return df
+    else:
+        return "Failed to fetch data or no data available. Please check the logs."
+
+# 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=["Rank", "Name", "Current Price", "Difference", "Change Rate",
+                                             "Volume", "Buy Price", "Sell Price", "Buy Total",
+                                             "Sell Total", "PER", "ROE"])  # 명시적 열 이름 지정
+
+        fetch_button.click(fn=fetch_kosdaq_data, inputs=[], outputs=output_table)
+
+    return demo
+
+demo = gradio_interface()
+
+if __name__ == "__main__":
+    demo.launch()

requirements (9).txt

@@ -0,0 +1,4 @@
+beautifulsoup4==4.12.2
+pandas==1.5.3
+requests==2.31.0
+gradio==3.41.0