Update dashboard.py

dashboard.py

@@ -1,21 +1,78 @@
 from __future__ import annotations
 import gradio as gr
 import plotly.express as px
-from data import read_events, aggregate
+import pandas as pd
+from data import read_events, aggregate, append_events
 from bandit import EmpiricalBayesHierarchicalThompson
 from causal import fit_uplift_binary
 
 BANDIT = EmpiricalBayesHierarchicalThompson(min_explore=0.05, margin=0.0, n_draws=10000)
 
+REQUIRED_COLS = [
+    "date","medium","creative","is_control",
+    "impressions","clicks","conversions","cost","features_json"
+]
+
+# ---- helpers ----
+def _ok(msg: str):
+    return gr.Markdown.update(value=f"✅ {msg}")
+def _warn(msg: str):
+    return gr.Markdown.update(value=f"⚠️ {msg}")
+def _err(msg: str):
+    return gr.Markdown.update(value=f"❌ {msg}")
+
+# ---- data ops ----
 def ui_refresh_tables():
     df = read_events()
     agg = aggregate()
-    return df, agg
+    return df, agg, _ok("Refreshed.")
+
+def ui_load_sample():
+    rows = [
+        {"date":"2025-09-01","medium":"FB","creative":"A1","is_control":1,"impressions":1000,"clicks":25,"conversions":3,"cost":480,"features_json":"{}"},
+        {"date":"2025-09-01","medium":"FB","creative":"B1","is_control":0,"impressions":850,"clicks":24,"conversions":2,"cost":395,"features_json":"{}"},
+        {"date":"2025-09-01","medium":"FB","creative":"C1","is_control":0,"impressions":900,"clicks":21,"conversions":2,"cost":420,"features_json":"{}"},
+        {"date":"2025-09-01","medium":"FB","creative":"D1","is_control":0,"impressions":820,"clicks":20,"conversions":2,"cost":380,"features_json":"{}"},
+        {"date":"2025-09-01","medium":"FB","creative":"E1","is_control":0,"impressions":960,"clicks":31,"conversions":3,"cost":490,"features_json":"{}"},
+        {"date":"2025-09-01","medium":"GDN","creative":"A2","is_control":1,"impressions":1100,"clicks":25,"conversions":2,"cost":545,"features_json":"{}"},
+        {"date":"2025-09-01","medium":"GDN","creative":"B2","is_control":0,"impressions":990,"clicks":27,"conversions":3,"cost":514,"features_json":"{}"},
+        {"date":"2025-09-01","medium":"GDN","creative":"C2","is_control":0,"impressions":860,"clicks":19,"conversions":2,"cost":450,"features_json":"{}"},
+        {"date":"2025-09-01","medium":"GDN","creative":"D2","is_control":0,"impressions":1045,"clicks":33,"conversions":4,"cost":570,"features_json":"{}"},
+        {"date":"2025-09-01","medium":"GDN","creative":"E2","is_control":0,"impressions":905,"clicks":20,"conversions":2,"cost":462,"features_json":"{}"}
+    ]
+    append_events(pd.DataFrame(rows))
+    df, agg = read_events(), aggregate()
+    return df, agg, _ok("Sample data loaded.")
+
+def ui_upload_csv(file: gr.File):
+    if file is None:
+        return gr.update(), gr.update(), _warn("CSVファイルを選択してください。")
+    try:
+        df = pd.read_csv(file.name)
+    except Exception as e:
+        return gr.update(), gr.update(), _err(f"CSV読み込みに失敗: {e}")
+
+    missing = [c for c in REQUIRED_COLS if c not in df.columns]
+    if missing:
+        return gr.update(), gr.update(), _err(f"必須列が不足: {missing}")
+
+    # 型のサニタイズ（軽く）
+    for c in ["is_control","impressions","clicks","conversions"]:
+        df[c] = df[c].fillna(0).astype(int)
+    if "cost" in df.columns:
+        df["cost"] = df["cost"].fillna(0.0).astype(float)
+    if "features_json" in df.columns:
+        df["features_json"] = df["features_json"].fillna("{}").astype(str)
+
+    append_events(df)
+    df2, agg2 = read_events(), aggregate()
+    return df2, agg2, _ok(f"取り込み完了（{len(df)}行）。")
 
+# ---- bandit / causal ----
 def ui_recommend():
     agg = aggregate()
     if agg.empty:
-        return {"message": "No data yet. POST /api/ingest first."}
+        return {"message": "No data yet. Dataタブで 'Load Sample Data' を押すか、CSVを取り込んでください。"}
     return BANDIT.recommend(agg)
 
 def ui_plot_posteriors(medium: str):
@@ -36,14 +93,26 @@ def ui_fit_uplift():
         return {"message": "No data"}
     return fit_uplift_binary(agg)
 
+# ---- UI ----
 def build_ui():
     with gr.Blocks(title="AdCopy MAB Optimizer Pro") as demo:
         gr.Markdown("# AdCopy MAB Optimizer Pro — Hierarchical TS + Uplift")
+
         with gr.Tab("Data"):
-            btn = gr.Button("Refresh")
+            status = gr.Markdown()  # メッセージ表示
+            with gr.Row():
+                btn_refresh = gr.Button("Refresh")
+                btn_seed = gr.Button("Load Sample Data")
+            with gr.Row():
+                file = gr.File(label="Upload CSV (columns: " + ", ".join(REQUIRED_COLS) + ")", file_types=[".csv"])
+                btn_upload = gr.Button("Import CSV")
             grid = gr.Dataframe(headers=["ts","date","medium","creative","is_control","impressions","clicks","conversions","cost","features_json"], wrap=True)
             grid_agg = gr.Dataframe()
-            btn.click(ui_refresh_tables, outputs=[grid, grid_agg])
+
+            btn_refresh.click(ui_refresh_tables, outputs=[grid, grid_agg, status])
+            btn_seed.click(ui_load_sample, outputs=[grid, grid_agg, status])
+            btn_upload.click(ui_upload_csv, inputs=[file], outputs=[grid, grid_agg, status])
+
         with gr.Tab("Bandit"):
             bbtn = gr.Button("Suggest Allocation (TS)")
             jout = gr.JSON()
@@ -53,8 +122,10 @@ def build_ui():
                 plot = gr.Plot(visible=False)
                 msg = gr.Markdown()
             gr.Button("Plot CTR by Creative").click(ui_plot_posteriors, inputs=[medium], outputs=[plot, msg])
+
         with gr.Tab("Uplift (Causal)"):
             cbtn = gr.Button("Fit Uplift Model")
             cout = gr.JSON()
             cbtn.click(ui_fit_uplift, outputs=cout)
+
     return demo