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import requests
from bs4 import BeautifulSoup
import pandas as pd
import gradio as gr
def scrape_kosdaq():
url = "https://finance.naver.com/sise/sise_rise.naver?sosok=1"
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.82 Safari/537.36"
}
try:
# Request the webpage
response = requests.get(url, headers=headers)
response.raise_for_status()
print("[INFO] Page fetched successfully.")
# Parse the HTML
soup = BeautifulSoup(response.content, "html.parser")
# Locate the table
table = soup.find("table", class_="type_2")
rows = table.find_all("tr")[2:] # Skip the header rows
data = []
# Extract data row by row
for row in rows:
cols = row.find_all("td")
if len(cols) < 12: # Skip blank or irrelevant rows
continue
entry = {
"Rank": cols[0].get_text(strip=True),
"Name": cols[1].get_text(strip=True),
"Price": cols[2].get_text(strip=True),
"Change": cols[3].get_text(strip=True),
"Change_Rate": cols[4].get_text(strip=True),
"Volume": cols[5].get_text(strip=True),
"Buy_Price": cols[6].get_text(strip=True),
"Sell_Price": cols[7].get_text(strip=True),
"Total_Buy_Quantity": cols[8].get_text(strip=True),
"Total_Sell_Quantity": cols[9].get_text(strip=True),
"PER": cols[10].get_text(strip=True),
"ROE": cols[11].get_text(strip=True),
}
data.append(entry)
print(f"[DEBUG] Extracted {len(data)} rows.")
return pd.DataFrame(data)
except requests.exceptions.RequestException as e:
print(f"[ERROR] Failed to fetch page: {e}")
return pd.DataFrame()
def display_data():
df = scrape_kosdaq()
if df.empty:
return "Failed to fetch data or no data available."
return df
# Gradio Interface
def gradio_interface():
with gr.Blocks() as demo:
gr.Markdown("### Naver Kosdaq Stock Scraper")
output = gr.Dataframe()
fetch_button = gr.Button("Fetch Data")
fetch_button.click(display_data, inputs=[], outputs=output)
return demo
if __name__ == "__main__":
gradio_interface().launch()
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