src/streamlit_app.py

import streamlit as st
import requests
from bs4 import BeautifulSoup
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from datetime import datetime
import time

st.set_page_config(page_title="Market Price Monitor", layout="wide")

# ==================== WEB SCRAPING FUNCTIONS ====================

def scrape_coinmarketcap():
    """Scrape cryptocurrency prices từ CoinMarketCap"""
    url = "https://coinmarketcap.com/"
    headers = {"User-Agent": "Mozilla/5.0"}
    
    try:
        response = requests.get(url, headers=headers, timeout=10)
        if response.status_code != 200:
            return pd.DataFrame(), "Error fetching data"
        
        soup = BeautifulSoup(response.content, "html.parser")
        rows = soup.select("tbody tr")[:15]
        
        data = []
        for row in rows:
            cols = row.find_all("td")
            if len(cols) >= 7:
                name = cols[2].find("p", class_=True).text.strip() if cols[2].find("p") else "N/A"
                symbol = cols[2].find("p", class_="coin-item-symbol").text.strip() if cols[2].find("p", class_="coin-item-symbol") else "N/A"
                price = cols[3].text.strip() if len(cols) > 3 else "N/A"
                change_24h = cols[4].text.strip() if len(cols) > 4 else "N/A"
                market_cap = cols[6].text.strip() if len(cols) > 6 else "N/A"
                
                data.append({
                    "Name": name,
                    "Symbol": symbol,
                    "Price": price,
                    "24h Change": change_24h,
                    "Market Cap": market_cap
                })
        
        df = pd.DataFrame(data)
        return df, None
    except Exception as e:
        return pd.DataFrame(), str(e)

def scrape_product_prices(product_urls):
    """Scrape giá sản phẩm từ nhiều website (tùy chỉnh)"""
    data = []
    
    for url in product_urls:
        try:
            headers = {"User-Agent": "Mozilla/5.0"}
            response = requests.get(url, headers=headers, timeout=10)
            soup = BeautifulSoup(response.content, "html.parser")
            
            # Tùy chỉnh selector theo website (ví dụ: Amazon, Shopee)
            title = soup.find("span", class_="a-size-medium") or soup.find("h1")
            price = soup.find("span", class_="a-price-whole") or soup.find("div", class_="price")
            
            data.append({
                "URL": url,
                "Product": title.text.strip() if title else "Unknown",
                "Price": price.text.strip() if price else "N/A",
                "Scraped At": datetime.now().strftime("%Y-%m-%d %H:%M")
            })
        except Exception as e:
            data.append({
                "URL": url,
                "Product": "Error",
                "Price": f"Error: {str(e)}",
                "Scraped At": datetime.now().strftime("%Y-%m-%d %H:%M")
            })
    
    return pd.DataFrame(data)

# ==================== STREAMLIT DASHBOARD ====================

st.title("📊 Market Price Monitor Dashboard")
st.markdown("Theo dõi giá thị trường thời gian thực - Web Scraping tự động")

# Sidebar
st.sidebar.header("⚙️ Cài đặt")

# Chọn loại thị trường
market_type = st.sidebar.radio(
    "Chọn thị trường:",
    ["Cryptocurrency", "Sản phẩm E-commerce", "Cả hai"]
)

auto_refresh = st.sidebar.checkbox("Tự động làm mới (30s)", value=False)
refresh_interval = st.sidebar.slider("Tần suất (giây)", 10, 120, 30)

# ==================== CRYPTOCURRENCY SECTION ====================
if market_type in ["Cryptocurrency", "Cả hai"]:
    st.header("🪙 Cryptocurrency Prices")
    
    col1, col2, col3 = st.columns(3)
    
    if market_type == "Cryptocurrency":
        df_crypto, error = scrape_coinmarketcap()
        
        if error:
            st.error(f"❌ Lỗi: {error}")
        else:
            # Metrics
            with col1:
                st.metric("Total Cryptos", len(df_crypto))
            with col2:
                avg_price = df_crypto["Price"].astype(str).str.replace(r"[^\d.]", "", regex=True).mean()
                st.metric("Avg Price", f"${avg_price:.2f}" if avg_price else "N/A")
            with col3:
                top_gainer = df_crypto.loc[df_crypto["24h Change"].str.contains("+", na=False)].head(1)
                if not top_gainer.empty:
                    st.metric("Top Gainer", f"{top_gainer['Name'].values[0]} ({top_gainer['24h Change'].values[0]})")
                else:
                    st.metric("Top Gainer", "N/A")
            
            # Data table
            st.subheader("📋 Dữ liệu chi tiết")
            st.dataframe(
                df_crypto,
                use_container_width=True,
                hide_index=True
            )
            
            # Charts
            col1, col2 = st.columns(2)
            
            with col1:
                st.subheader("📈 Top 10 by Price")
                df_crypto_clean = df_crypto.copy()
                df_crypto_clean["Price"].replace({r"[^\d.]": ""}, regex=True, inplace=True)
                df_crypto_clean["Price"] = pd.to_numeric(df_crypto_clean["Price"], errors="coerce")
                df_top10 = df_crypto_clean.nlargest(10, "Price")
                
                fig_bar = px.bar(df_top10, x="Symbol", y="Price", color="Name",
                                title="Top 10 Crypto Prices",
                                labels={"Price": "Price (USD)"})
                st.plotly_chart(fig_bar, use_container_width=True)
            
            with col2:
                st.subheader("🥧 Market Cap Distribution")
                fig_pie = px.pie(df_crypto.head(10), names="Name", values="Market Cap",
                               title="Top 10 Market Cap")
                st.plotly_chart(fig_pie, use_container_width=True)
            
            # 24h Change chart
            st.subheader("📊 24h Change (%)")
            df_crypto_clean["24h Change"].replace({r"[^\d.-]": ""}, regex=True, inplace=True)
            df_crypto_clean["24h Change"] = pd.to_numeric(df_crypto_clean["24h Change"], errors="coerce")
            
            fig_change = px.bar(df_crypto_clean.head(15), x="Name", y="24h Change",
                              color="24h Change",
                              color_continuous_scale="RdYlGn",
                              title="24h Price Change (%)")
            st.plotly_chart(fig_change, use_container_width=True)

# ==================== E-COMMERCE SECTION ====================
if market_type in ["Sản phẩm E-commerce", "Cả hai"]:
    st.header("🛒 E-commerce Product Prices")
    
    # Input URLs
    st.subheader("🔗 Thêm URL sản phẩm")
    url_input = st.text_area(
        "Nhập URLs (mỗi dòng 1 URL):",
        placeholder="https://amazon.com/product1\nhttps://shopee.vn/product2",
        height=150
    )
    
    if st.button("🔍 Scrape Prices", type="primary"):
        if url_input.strip():
            urls = [url.strip() for url in url_input.split("\n") if url.strip()]
            with st.spinner("Đang scrape dữ liệu..."):
                df_products = scrape_product_prices(urls)
            
            st.subheader("📋 Kết quả")
            st.dataframe(df_products, use_container_width=True, hide_index=True)
            
            # Download CSV
            csv = df_products.to_csv(index=False).encode("utf-8")
            st.download_button(
                "📥 Download CSV",
                csv,
                "market_prices.csv",
                "text/csv"
            )
        else:
            st.warning("⚠️ Vui lòng nhập ít nhất 1 URL")

# ==================== FOOTER ====================
st.markdown("---")
st.markdown(
    f"""
    <div style='text-align: center; color: gray;'>
        🔄 Cập nhật: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")} | 
        Data scraped từ CoinMarketCap & E-commerce sites
    </div>
    """,
    unsafe_allow_html=True
)

# Auto-refresh
if auto_refresh:
    time.sleep(refresh_interval)
    st.rerun()