Update app.py

app.py

@@ -8,7 +8,7 @@ import plotly.express as px
 import plotly.graph_objects as go
 import networkx as nx
 import random
-from atproto_client.models.app.bsky.feed.defs import PostView, ReplyRef
+from atproto_client.models.app.bsky.feed.defs import PostView
 
 def get_profile_info(client, did_or_handle):
     try:
@@ -29,13 +29,13 @@ def analyze_and_output(my_id, my_pw, target_id, freq_type, progress=gr.Progress(
         profile = client.get_profile(actor=target_handle)
         
         posts_data = []
+        reply_users_list = []    
+        repost_users_list = []   
         interaction_pairs = [] 
 
-        pos_words = ["嬉しい", "楽しい", "最高", "ありがとう", "感謝", "好き", "わーい", "おめでとう", "楽しみ", "美味しい", "良き", "笑", "w"]
-        neg_words = ["疲れた", "最悪", "悲しい", "辛い", "苦しい", "嫌い", "しんどい", "ムリ", "残念", "ひどい", "鬱", "おこ", "死ぬ", "ため息"]
-
-        progress(0, desc="フィードを深層解析中...")
+        progress(0, desc="フィードを解析中...")
         cursor = None
+        # より多くの繋がりを検出するため50回ループ（最大5000件）
         for _ in range(50): 
             try:
                 response = client.get_author_feed(actor=profile.did, limit=100, cursor=cursor)
@@ -44,124 +44,157 @@ def analyze_and_output(my_id, my_pw, target_id, freq_type, progress=gr.Progress(
 
             for feed_view in response.feed:
                 post = feed_view.post
+                if not isinstance(post, PostView): continue
                 
-                # --- エラー回避用のチェック ---
-                # 投稿が「PostView」型（正常な投稿）でない場合はスキップ
-                if not isinstance(post, PostView):
-                    continue
-                
+                # A. 投稿データ
                 created_at_raw = getattr(post.record, 'created_at', None)
                 if created_at_raw:
                     dt_jst = pd.to_datetime(created_at_raw) + timedelta(hours=9)
                     if post.author.handle == target_handle:
-                        text = getattr(post.record, 'text', "")
-                        s_val = "Neutral"
-                        if any(w in text for w in pos_words): s_val = "Positive"
-                        elif any(w in text for w in neg_words): s_val = "Negative"
-
                         posts_data.append({
+                            'text': getattr(post.record, 'text', ""),
                             'created_at': dt_jst,
-                            'sentiment': s_val,
-                            'text': text,
+                            'hour': dt_jst.hour,
+                            'weekday': dt_jst.day_name(),
                             'likes': getattr(post, 'like_count', 0),
                             'reposts': getattr(post, 'repost_count', 0),
                             'score': getattr(post, 'like_count', 0) + getattr(post, 'repost_count', 0)
                         })
 
-                # --- インタラクション抽出のエラー回避 ---
+                # B. インタラクション抽出 (自分以外同士の繋がりを強化)
                 u_author = post.author.handle
+                # 1. リポストによる繋がり
                 if feed_view.reason: 
-                    interaction_pairs.append((u_author, target_handle))
+                    interaction_pairs.append((target_handle, u_author))
+                    if u_author != target_handle: repost_users_list.append(u_author)
                 
-                # リプライ先の投稿が存在し、かつ正常な投稿(PostView)であるか確認
-                reply = getattr(feed_view, 'reply', None)
-                if reply and isinstance(reply.parent, PostView):
-                    u_parent = reply.parent.author.handle
-                    interaction_pairs.append((u_author, u_parent))
+                # 2. リプライによる繋がり（投稿者と返信先のペアをすべて記録）
+                if getattr(feed_view, 'reply', None) and feed_view.reply.parent:
+                    parent_post = feed_view.reply.parent
+                    if isinstance(parent_post, PostView):
+                        u_parent = parent_post.author.handle
+                        # このペアを記録（自分以外同士も含まれる）
+                        interaction_pairs.append((u_author, u_parent))
+                        if u_author == target_handle and u_parent != target_handle:
+                            reply_users_list.append(u_parent)
 
             cursor = response.cursor
             if not cursor: break
 
-        if not posts_data: return "データなし", "", "", None, None, None, None, "失敗"
+        if not posts_data: return "データなし", "", "", None, None, None, "失敗"
 
         df = pd.DataFrame(posts_data)
+        # 初ポスト日（取得できた範囲での最古）
+        first_post_date = df['created_at'].min().strftime('%Y/%m/%d')
+
+        # --- HTML作成 ---
+        stats_html = f"""
+        <div style='display: grid; grid-template-columns: repeat(3, 1fr); gap: 10px;'>
+            <div style='background: #e3f2fd; padding: 10px; border-radius: 8px; text-align: center;'>
+                <small>総ポスト</small><br><b>{getattr(profile, 'posts_count', 0)}</b>
+            </div>
+            <div style='background: #f1f8e9; padding: 10px; border-radius: 8px; text-align: center;'>
+                <small>活動開始(初ポスト)</small><br><b>{first_post_date}</b>
+            </div>
+            <div style='background: #fff3e0; padding: 10px; border-radius: 8px; text-align: center;'>
+                <small>分析件数</small><br><b>{len(df)}件</b>
+            </div>
+        </div>
+        """
+        
+        def rank_box(title, items):
+            top = Counter(items).most_common(3)
+            res = f"<b>{title}</b><br>"
+            res += "<br>".join([f"@{n} ({c}回)" for n, c in top]) if top else "なし"
+            return res
+
+        rank_html = f"<div style='background: #f9f9f9; padding: 15px; border-radius: 10px; font-size: 0.9em;'>{rank_box('💬 リプライ相手TOP3', reply_users_list)}<hr>{rank_box('🔄 リポスト相手TOP3', repost_users_list)}</div>"
+        
+        top_posts = df.sort_values('score', ascending=False).head(3)
+        posts_html = "<b>🏆 ベストポスト</b><br>"
+        for _, row in top_posts.iterrows():
+            posts_html += f"<div style='margin-bottom:8px; font-size:0.85em; border-left:3px solid #0085ff; padding-left:5px;'>{row['text'][:60]}...<br><small>❤️ {row['likes']}  🔄 {row['reposts']}</small></div>"
+
+        # --- グラフ作成 ---
+        freq_map = {"日ごと": "D", "週ごと": "W", "月ごと": "M"}
+        df_counts = df.set_index('created_at').resample(freq_map[freq_type]).size().reset_index(name='count')
+        fig_bar = px.bar(df_counts, x='created_at', y='count', title="投稿数の推移", color_discrete_sequence=['#0085ff'])
+
+        week_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
+        week_ja = ['月', '火', '水', '木', '金', '土', '日']
+        heat_df = df.groupby(['weekday', 'hour']).size().reset_index(name='count')
+        heat_pt = heat_df.pivot(index='weekday', columns='hour', values='count').reindex(week_order).fillna(0)
+        heat_pt.index = week_ja
+        fig_heat = px.imshow(heat_pt, labels=dict(x="時間 (24h)", y="曜日", color="投稿数"), x=list(range(24)), y=week_ja, color_continuous_scale='Blues', title="週間アクティビティ・ヒートマップ")
+
+        # --- 相関図 (関係性上書き & 自分以外同士の線) ---
+        progress(0.8, desc="相関図を生成中...")
+        RELATIONSHIPS = [
+            "家族", "恋人候補", "実は好き", "ペット", "宿命のライバル", "幼馴染", "憧れの人", "師匠", "弟子", "癒やし枠", "腐れ縁",
+            "魂の双子", "前世での伴侶", "生涯の恩人", "運命の赤い糸", "行きつけの店の店主", "同じ趣味 of 同志", 
+            "深夜の話し相手", "甘えたい", "影の守護者", "最強の刺客", "永遠のライバル", "喧嘩仲間", "裏切りの共犯者",
+            "嫉妬", "だ～いすき♡", "軽蔑", "下僕", "裸の関係", "お抱えの料理人"
+        ]
         
-        # --- 感情オーラグラフ ---
-        df['date'] = df['created_at'].dt.date
-        aura_df = df.groupby(['date', 'sentiment']).size().reset_index(name='count')
-        fig_aura = px.area(
-            aura_df, x="date", y="count", color="sentiment",
-            title="✨ 心のオーラ・パレット（感情の推移）",
-            color_discrete_map={'Positive': '#FFADAD', 'Negative': '#A0C4FF', 'Neutral': '#E2E2E2'},
-            template="plotly_white"
-        )
-
-        # --- 相関図 ---
         all_interactors = [p for pair in interaction_pairs for p in pair if p != target_handle]
         top_10 = [u for u, c in Counter(all_interactors).most_common(10)]
         nodes = list(set([target_handle] + top_10))
-        
-        RELATIONSHIPS = ["家族", "恋人候補", "実は好き", "ペット", "宿命のライバル", "幼馴染", "憧れの人", "師匠", "弟子", "癒やし枠", "腐れ縁", "魂の双子", "前世での伴侶", "生涯の恩人", "運命の赤い糸", "行きつけの店の店主", "同じ趣味 of 同志", "深夜の話し相手", "甘えたい", "影の守護者", "最強の刺客", "永遠のライバル", "喧嘩仲間", "裏切りの共犯者", "嫉妬", "だ～いすき♡", "軽蔑", "下僕", "裸の関係", "お抱えの料理人"]
         node_attrs = {n: (random.choice(RELATIONSHIPS) if n != target_handle else "（本人）") for n in nodes}
 
-        G = nx.DiGraph()
-        for (u_f, u_t) in interaction_pairs:
-            if u_f in nodes and u_t in nodes and u_f != u_t:
-                if G.has_edge(u_f, u_t): G[u_f][u_t]['weight'] += 1
-                else: G.add_edge(u_f, u_t, weight=1)
+        G = nx.Graph()
+        for (u1, u2) in interaction_pairs:
+            if u1 in nodes and u2 in nodes and u1 != u2:
+                if G.has_edge(u1, u2): G[u1][u2]['weight'] += 1
+                else: G.add_edge(u1, u2, weight=1)
 
-        pos = nx.spring_layout(G, k=1.1, seed=42)
+        pos = nx.spring_layout(G, k=1.0, seed=42) # 少し広めに配置
         fig_net = go.Figure()
+
         for edge in G.edges():
-            start, end = edge
-            w = G[start][end]['weight']
-            fig_net.add_trace(go.Scatter(x=[pos[start][0], pos[end][0], None], y=[pos[start][1], pos[end][1], None],
-                                         mode='lines', line=dict(width=min(w, 5), color='rgba(200,200,200,0.6)'), hoverinfo='none'))
+            w = G[edge[0]][edge[1]]['weight']
+            fig_net.add_trace(go.Scatter(x=[pos[edge[0]][0], pos[edge[1]][0], None], y=[pos[edge[0]][1], pos[edge[1]][1], None], mode='lines', line=dict(color='#ddd', width=min(w, 5)), hoverinfo='none'))
 
         node_labels = [f"<b>{n}</b>" if n == target_handle else f"@{n}<br><span style='color:red;'>【{node_attrs[n]}】</span>" for n in G.nodes()]
-        fig_net.add_trace(go.Scatter(x=[pos[n][0] for n in G.nodes()], y=[pos[n][1] for n in G.nodes()],
-                                     mode='markers+text', text=node_labels, textposition="bottom center",
-                                     marker=dict(size=40, color='rgba(0,0,0,0)'), hoverinfo='none'))
+        fig_net.add_trace(go.Scatter(x=[pos[n][0] for n in G.nodes()], y=[pos[n][1] for n in G.nodes()], mode='markers+text', text=node_labels, textposition="bottom center", marker=dict(size=45, color='rgba(0,0,0,0)'), hoverinfo='none'))
 
         node_images = []
         for node in G.nodes():
             info = get_profile_info(client, node)
             if info:
-                node_images.append(dict(source=info['avatar'], xref="x", yref="y", x=pos[node][0], y=pos[node][1], sizex=0.2, sizey=0.2, xanchor="center", yanchor="middle", layer="above"))
-        fig_net.update_layout(images=node_images, showlegend=False, plot_bgcolor='white', xaxis=dict(showgrid=False, zeroline=False, showticklabels=False), yaxis=dict(showgrid=False, zeroline=False, showticklabels=False), height=650)
+                node_images.append(dict(source=info['avatar'], xref="x", yref="y", x=pos[node][0], y=pos[node][1], sizex=0.22, sizey=0.22, xanchor="center", yanchor="middle", layer="above"))
 
-        stats_html = f"<div style='text-align:center; background:#f9f9f9; padding:15px; border-radius:15px;'><b>{target_handle}</b> の深層プロフィール<br><small>初ポスト日: {df['created_at'].min().strftime('%Y/%m/%d')}</small></div>"
-        top_posts = df.sort_values('score', ascending=False).head(3)
-        posts_html = "<b>🏆 ベストポスト</b><br>"
-        for _, row in top_posts.iterrows():
-            posts_html += f"<div style='margin-bottom:8px; border-left:4px solid #4CC9F0; padding-left:8px; font-size:0.9em;'>{row['text'][:60]}...<br><small>❤️ {row['likes']} 🔄 {row['reposts']}</small></div>"
+        fig_net.update_layout(images=node_images, showlegend=False, plot_bgcolor='white', xaxis=dict(showgrid=False, zeroline=False, showticklabels=False), yaxis=dict(showgrid=False, zeroline=False, showticklabels=False), height=700, title="🦋 脳内ユーザー相関図 (自分以外も繋がります)")
 
-        return stats_html, "", posts_html, None, None, fig_aura, fig_net, "解析完了！"
+        return stats_html, rank_html, posts_html, fig_bar, fig_heat, fig_net, "解析完了！"
         
     except Exception as e:
-        return f"エラー: {str(e)}", "", "", None, None, None, None, "失敗"
+        return f"エラー: {str(e)}", "", "", None, None, None, "失敗"
 
-with gr.Blocks() as demo:
-    gr.Markdown("# 🦋 Bluesky 深層オーラ & 相関図 (修正版)")
+with gr.Blocks(title="Bluesky Ultimate Analyzer") as demo:
+    gr.Markdown("# 🦋 Bluesky アクティビティ & 脳内相関図")
     with gr.Row():
         with gr.Column(scale=1):
             my_id = gr.Textbox(label="自分のハンドル")
             my_pw = gr.Textbox(label="アプリパスワード", type="password")
             target_id = gr.Textbox(label="解析対象")
-            btn = gr.Button("解析実行", variant="primary")
+            freq = gr.Radio(["日ごと", "週ごと", "月ごと"], label="推移グラフの単位", value="日ごと")
+            btn = gr.Button("解析を開始する", variant="primary")
             status = gr.Textbox(label="ステータス", interactive=False)
         with gr.Column(scale=2):
             out_stats = gr.HTML()
-            out_posts = gr.HTML()
-
-    with gr.Tabs():
-        with gr.TabItem("✨ 心のオーラ"):
-            out_aura = gr.Plot()
-        with gr.TabItem("🤝 人間関係相関図"):
-            out_net = gr.Plot()
-
-    btn.click(analyze_and_output, inputs=[my_id, my_pw, target_id, gr.State("日ごと")], 
-              outputs=[out_stats, gr.State(""), out_posts, gr.State(None), gr.State(None), out_aura, out_net, status])
+            with gr.Row():
+                out_rank = gr.HTML()
+                out_posts = gr.HTML()
+
+    with gr.Tab("アクティビティ推移"):
+        out_bar = gr.Plot(label="投稿頻度の推移")
+        out_heat = gr.Plot(label="曜日×時間ヒートマップ")
+    
+    with gr.Tab("ユーザー相関図"):
+        out_net = gr.Plot(label="脳内相関図")
+
+    btn.click(analyze_and_output, inputs=[my_id, my_pw, target_id, freq], 
+              outputs=[out_stats, out_rank, out_posts, out_bar, out_heat, out_net, status])
 
 if __name__ == "__main__":
     demo.launch()