app.py

import streamlit as st
from streamlit_folium import st_folium
import pandas as pd
import matplotlib.pyplot as plt
import japanize_matplotlib
import seaborn as sns
import numpy as np

st.set_page_config(page_title="データ分析ダッシュボード", layout="wide")

tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs([" データ分析ダッシュボード", "Folium", "Network graph", "Ribbon graph", "Sankey diagram", "Waterfall Chart"])

with tab1:
    st.title("📊 データ分析ダッシュボード")

    uploaded_file = st.file_uploader("CSVファイルをアップロードしてください", type=["csv"])

    if uploaded_file is not None:
        df = pd.read_csv(uploaded_file)
        st.success("✅ ファイルを読み込みました！")

        st.header("📌 データのプレビュー")
        st.dataframe(df.head())

        st.header("📊 基本統計情報")
        st.write(df.describe())

        st.header("📈 カラムごとの分布表示")
        # 数値型カラムを取得
        numeric_columns = df.select_dtypes(include=np.number).columns
        # 数値型カラムが存在するか確認
        if len(numeric_columns) > 0:
            column = st.selectbox("分布を見たいカラムを選んでください", numeric_columns)
            
            if column:  # 選択されたカラムが存在する場合
                fig, ax = plt.subplots()
                sns.histplot(df[column], kde=True, ax=ax)
                ax.set_title(f"{column} のヒストグラム")
                st.pyplot(fig)

            fig, ax = plt.subplots()
            sns.histplot(df[column], kde=True, ax=ax)
            ax.set_title(f"{column} のヒストグラム")
            st.pyplot(fig)

            numeric_df = df.select_dtypes(include=["number"])
            fig_corr, ax_corr = plt.subplots()
            sns.heatmap(numeric_df.corr(), annot=True, cmap="coolwarm", fmt=".2f", ax=ax_corr)
            st.pyplot(fig_corr)

            st.header("📊 ボックスプロット")
            box_col = st.selectbox("ボックスプロットの対象カラムを選択", df.select_dtypes(include=np.number).columns, key="box")
            fig_box, ax_box = plt.subplots()
            sns.boxplot(x=df[box_col], ax=ax_box)
            ax_box.set_title(f"{box_col} のボックスプロット")
            st.pyplot(fig_box)

            st.header("📈 折れ線グラフ")
            if df.select_dtypes(include=np.number).shape[1] >= 2:
                line_x = st.selectbox("X軸に使うカラムを選択", df.select_dtypes(include=np.number).columns, key="line_x")
                line_y = st.selectbox("Y軸に使うカラムを選択", df.select_dtypes(include=np.number).columns, key="line_y")

                fig_line, ax_line = plt.subplots()
                sns.lineplot(x=df[line_x], y=df[line_y], ax=ax_line)
                ax_line.set_title(f"{line_x} vs {line_y} 折れ線グラフ")
                st.pyplot(fig_line)

            st.header("📊 カテゴリカルデータの棒グラフ")
            cat_columns = df.select_dtypes(include='object').columns
            if len(cat_columns) > 0:
                cat_col = st.selectbox("カテゴリカルカラムを選択", cat_columns)
                fig_bar, ax_bar = plt.subplots()
                df[cat_col].value_counts().plot(kind='bar', ax=ax_bar)
                ax_bar.set_title(f"{cat_col} の頻度棒グラフ")
                st.pyplot(fig_bar)
        else:
            st.warning("数値型のカラムがありません。")

    else:
        st.info("左側のサイドバーからCSVファイルをアップロードしてください。")

with tab2:
    st.header("📊 住所・座標の可視化")
    import folium
    from streamlit_folium import st_folium
    import pandas as pd
    import requests 

    # uploaded_file2 = st.file_uploader("住所CSVファイルをアップロードしてください", type=["csv"])

    # if uploaded_file2 is not None:
    df = pd.read_csv("./address.csv")

    name_list = df["name"]
    address_list = df["address"]

    def get_lat_long_from_address(address):
        """住所をAPIで検索し、緯度と経度を取得する"""
        endpoint = "https://msearch.gsi.go.jp/address-search/AddressSearch"
        response = requests.get(endpoint, params={"q": address})

        if response.status_code == 200:
            data = response.json()
            if len(data) > 0:
                lat = data[0]["geometry"]["coordinates"][1]
                lon = data[0]["geometry"]["coordinates"][0]
                return lat, lon
        return None, None

    staff_lat_lon = [get_lat_long_from_address(addr) for addr in address_list]
    
    data = pd.DataFrame({
        "クリニック名": name_list,
        "クリニック住所": address_list,
        "クリニック_緯度": [lat for lat, lon in staff_lat_lon],
        "クリニック_経度": [lon for lat, lon in staff_lat_lon]
    })


    col1, col2 = st.columns([3, 2])

    # 地図の生成
    m = folium.Map(location=[37.5, 140.5], zoom_start=7)
    
    # マーカーの追加 (NaNチェック 추가)
    for i in range(len(data["クリニック名"])):
        lat = data["クリニック_緯度"][i]
        lon = data["クリニック_経度"][i]
        
        if pd.isna(lat) or pd.isna(lon):
            continue
        
        folium.Marker(
            location=(lat, lon),
            popup=data["クリニック名"][i],
            icon=folium.Icon(color="blue"),
        ).add_to(m)


    with col1:
        st.title("📍 クリニックの位置情報")
        map_data = st_folium(m, width=1200, height=800)


    with col2:
        st.markdown("<br>", unsafe_allow_html=True) 
        st.markdown("<br>", unsafe_allow_html=True) 
        st.markdown("<br>", unsafe_allow_html=True) 
        st.markdown("<br>", unsafe_allow_html=True) 
        st.subheader("📌 選択されたクリニック情報")
        
        if map_data and map_data["last_object_clicked"]:
            clicked_lat = map_data["last_object_clicked"]["lat"]
            clicked_lon = map_data["last_object_clicked"]["lng"]

            for i in range(len(data["クリニック名"])):
                if (round(data["クリニック_緯度"][i], 6) == round(clicked_lat, 6)) and (round(data["クリニック_経度"][i], 6) == round(clicked_lon, 6)):
                    st.write(f"**🏥 クリニック名:** {data['クリニック名'][i]}")
                    st.write(f"**📍 住所:** {data['クリニック住所'][i]}")
                    break
        else:
            st.info("📌 マーカーをクリックしてください。")

with tab3:
    st.header("📊 ネットワークグラフ")
    from pyvis.network import Network
    import streamlit.components.v1 as components
    import pandas as pd
    import numpy as np

    np.random.seed(42)
    nodes = [f"Node_{i}" for i in range(1, 21)]
    edges = []

    for _ in range(50):
        src = np.random.choice(nodes)
        dst = np.random.choice(nodes)
        if src != dst:
            weight = np.random.randint(1, 10)
            edges.append((src, dst, weight))

    df = pd.DataFrame(edges, columns=["Source", "Target", "Weight"])

    net = Network(height="800px", width="100%", bgcolor="#1e1e1e", font_color="white")
    
    net.force_atlas_2based(gravity=-500, central_gravity=0.03, spring_length=120, spring_strength=0.1)

    for node in nodes:
        net.add_node(node, label=node, title=f"{node} - Custom Tooltip", color="#29b6f6", font={'size': 20})

    for src, dst, weight in edges:
        net.add_edge(src, dst, value=weight, title=f"Weight: {weight}", width=weight)

    net.show_buttons(filter_=["physics", "nodes", "edges"])
    net.toggle_physics(True)
    net.set_edge_smooth('dynamic')

    net.save_graph('network.html')
    HtmlFile = open("network.html", 'r', encoding='utf-8')
    source_code = HtmlFile.read()
    components.html(source_code, height=850, scrolling=True)
    
with tab4:
    import plotly.graph_objects as go
    import pandas as pd
    import streamlit as st

    st.header("📊 リボングラフ")

    data = {
        "categories": ["Botox", "Filler", "Laser Treatment", "Skincare", "Hair Transplant", "Others"],
        "q1_2021": [1500, 2500, 1000, 2000, 1800, 1600],
        "q2_2021": [1800, 2300, 1400, 1900, 1600, 1700],
        "q3_2021": [2200, 2400, 1600, 1800, 2000, 1500],
        "q4_2021": [2600, 2100, 1800, 1700, 2200, 1400],
        "q1_2022": [2400, 2500, 1900, 2100, 2300, 1200],
        "q2_2022": [2700, 2600, 1700, 2300, 2100, 1000],
        "q3_2022": [2800, 2800, 2000, 2400, 2500, 900],
        "q4_2022": [2900, 2900, 2100, 2400, 2700, 1100],
        "q1_2023": [3100, 3000, 2200, 2500, 2900, 1200],
        "q2_2023": [3300, 3100, 2400, 2600, 3000, 1300],
        "q3_2023": [3400, 3200, 2500, 2700, 3200, 1400],
        "q4_2023": [3500, 3300, 2600, 2800, 3300, 1500],
    }
    df = pd.DataFrame(data)

    final = pd.DataFrame(df["categories"])
    quarters = [
        "q1_2021", "q2_2021", "q3_2021", "q4_2021", 
        "q1_2022", "q2_2022", "q3_2022", "q4_2022", 
        "q1_2023", "q2_2023", "q3_2023", "q4_2023"
    ]

    for i, col in enumerate(quarters):
        ch1 = df.loc[:, ["categories", col]]
        ch1.sort_values(col, inplace=True)
        ch1[f"y{i}_upper"] = ch1[col].cumsum()
        ch1[f"y{i}_lower"] = ch1[f"y{i}_upper"].shift(1).fillna(0)
        ch1[f"y{i}"] = ch1.apply(lambda x: (x[f"y{i}_upper"] + x[f"y{i}_lower"]) / 2, axis=1)
        final = final.merge(
            ch1[["categories", f"y{i}_upper", f"y{i}_lower", f"y{i}"]], on="categories"
        )

    colors = {
        "Botox": "#72c6e8",
        "Filler": "#E41A37",
        "Laser Treatment": "#5c606d",
        "Skincare": "#12618F",
        "Hair Transplant": "#d9871b",
        "Others": "rgba(0,0,0,0.3)",
    }

    st.sidebar.header("🔍 施術フィルター")
    selected_categories = st.sidebar.multiselect(
        "表示する施術を選んでください:",
        options=df["categories"],
        default=df["categories"]
    )

    fig = go.Figure()
    x = list(range(1, len(quarters) + 1))
    x_rev = x[::-1]

    for category in selected_categories:
        ch1 = final.query("categories == @category")
        upper_col = [col for col in ch1.columns if "upper" in col]
        lower_col = [col for col in ch1.columns if "lower" in col]
        upper_data = ch1[upper_col].values.tolist()[0]
        lower_data = ch1[lower_col].values.tolist()[0]

        smooth_upper = pd.Series(upper_data).rolling(window=2, min_periods=1).mean().tolist()
        smooth_lower = pd.Series(lower_data).rolling(window=2, min_periods=1).mean().tolist()

        fig.add_trace(
            go.Scatter(
                x=x + x_rev,
                y=smooth_upper + smooth_lower[::-1],
                fill="toself",
                fillcolor=colors[category],
                opacity=0.7,
                line_color="rgba(0,0,0,0.2)",
                hoverinfo="none",
                showlegend=True,
                name=category
            )
        )

    fig.update_layout(
        xaxis_title="Quarter",
        yaxis_title="累積データ (人数)",
        xaxis=dict(
            tickmode="array",
            tickvals=x,
            ticktext=quarters
        ),
        plot_bgcolor="#f9f9f9",
        hovermode="x unified",
        height=600
    )

    st.title("各施術の累積データのリボングラフ")
    st.plotly_chart(fig, use_container_width=True)
    
with tab5:
    import matplotlib.pyplot as plt
    import plotly.graph_objects as go


    nodes = [
        "カウンセリング", "二重まぶた手術", "鼻整形", "顎矯正", "脂肪吸引",
        "フィラー施術", "ボトックス", "胸拡大", "リフティング", "ヘア移植",
        "回復管理", "追加ケア", "再施術"
    ]

    links = {
        'source': [
            0, 0, 0, 0, 0, 0, 0, 0, 0, 
            1, 2, 3, 4, 5, 6, 7, 8, 9,  
            2, 4, 6,                    
            4, 7                       
        ],
        'target': [
            1, 2, 3, 4, 5, 6, 7, 8, 9,  
            10, 10, 10, 10, 10, 10, 10, 10, 10,  
            11, 11, 11,  
            12, 12      
        ],
        'value': [
            1200, 950, 800, 700, 1800, 2200, 500, 670, 430,  
            1100, 900, 600, 700, 1700, 2100, 400, 600, 300,   
            300, 200, 100,                                   
            150, 100                                          
        ]
    }

    fig = go.Figure(go.Sankey(
        arrangement="snap",
        node=dict(
            pad=20,                   
            thickness=30,           
            line=dict(color="black", width=0.5),
            label=nodes,
            color=[
                '#a6cee3', '#1f78b4', '#b2df8a', '#33a02c', '#fb9a99',
                '#e31a1c', '#fdbf6f', '#ff7f00', '#cab2d6', '#6a3d9a',
                '#ffff99', '#b15928', '#ffed6f'
            ]
        ),
        link=dict(
            source=links['source'],
            target=links['target'],
            value=links['value'],
            color='rgba(44, 160, 44, 0.4)'
        )
    ))

    fig.update_layout(
        title_text="美容整形の施術プロセス Sankeyダイアグラム",
        font=dict(size=14),  
        height=600     
    )

    st.title("美容整形業界の主要な施術ルート（件数ベース）")
    st.plotly_chart(fig)
    
with tab6:
    import plotly.graph_objects as go

    import plotly.graph_objects as go
    import streamlit as st

    # 🔹 データ定義
    x = [
        "2023年度 売上",
        "ボトックスの売上増加",
        "フィラー施術の売上減少",
        "レーザー治療の売上増加",
        "スキンケア商品の売上減少",
        "ヘア移植の売上増加",
        "その他の施術の影響",
        "2024年度 予測売上",
    ]
    y = [32000, 4500, -3000, 6000, -2000, 3500, 800, 42800]
    measure = [
        "absolute",
        "relative",
        "relative",
        "relative",
        "relative",
        "relative",
        "relative",
        "total",
    ]

    fig = go.Figure(
        go.Waterfall(
            x=x,
            y=y,
            measure=measure,
            text=[f"{val:,}万円" for val in y],
            textposition="outside",
            decreasing={"marker": {"color": "#F44336"}},
            increasing={"marker": {"color": "#4CAF50"}},
            totals={"marker": {"color": "#2196F3"}},
            connector={"line": {"color": "grey", "width": 2}},
            constraintext="none"
        )
    )

    fig.update_layout(
        title={
            "text": "📈 売上変動分析",
            "x": 0.5,
            "xanchor": "center",
            "yanchor": "top",
            "font": {"size": 24}
        },
        yaxis=dict(
            title="売上 (万円)",
            tickfont=dict(size=18)
        ),
        xaxis=dict(
            tickfont=dict(size=16),
            tickangle=-30
        ),
        height=800,  
        waterfallgap=0.3,
        font=dict(size=18),
        plot_bgcolor="rgba(0,0,0,0)",
        margin=dict(l=40, r=40, t=60, b=80)  
    )


    st.title("📊 売上変動分析")
    st.plotly_chart(fig, use_container_width=True)