Refactor Gradio interface to display ammonia status

- Replaced the run_troubleshooting function with a new display_message function to show a specific alert regarding ammonia levels.
- Updated the Gradio interface to use Blocks for a more structured layout, including a Markdown header and a non-interactive textbox for status display.

app.py

@@ -1,148 +1,10 @@
-from dotenv import load_dotenv
-load_dotenv()
-
-import os
-# 環境変数から取得
-SUPABASE_URL = os.environ.get('SUPABASE_URL')
-
-SUPABASE_KEY = os.environ.get('SUPABASE_KEY')
-
-import supabase
-table_threshold = "Threshold_data"
-table_sensor = "Sensor_data"
-table_troubleshooting = "Troubleshooting_collection"
-
-# クライアントの初期化
-supabase_client = supabase.create_client(SUPABASE_URL, SUPABASE_KEY)
-
-
-# データ取得
-threshold_data = supabase_client.table(table_threshold).select("*").execute()
-sensor_data = supabase_client.table(table_sensor).select("*").execute()
-troubleshooting_data = supabase_client.table(table_troubleshooting).select("*").execute()
-
-import pandas as pd
-threshold_df = pd.DataFrame(threshold_data.data)
-sensor_df = pd.DataFrame(sensor_data.data)
-troubleshooting_df = pd.DataFrame(troubleshooting_data.data)
-
-def check_thresholds(sensor_df, threshold_df):
-    alerts = []
-
-    # '下限'と'上限'カラムを数値型に変換。変換できない値はNaNとする。
-    threshold_df['下限'] = pd.to_numeric(threshold_df['下限'], errors='coerce')
-    threshold_df['上限'] = pd.to_numeric(threshold_df['上限'], errors='coerce')
-
-    for _, row in threshold_df.iterrows():
-        metric = row["指標名"]
-        min_val = row["下限"]
-        max_val = row["上限"]
-        data_no = row["No."] # Get the 'No.' from threshold_df
-
-        # センサーデータに指標が存在しない場合はスキップ
-        if metric not in sensor_df.columns:
-            continue
-
-        # センサーデータの該当カラムを数値型に変換。変換できない値はNaNとする。
-        sensor_metric_data = pd.to_numeric(sensor_df[metric], errors='coerce')
-
-
-        for i, value in enumerate(sensor_metric_data):
-            # Assuming 'datetime' is the timestamp column in sensor_df
-            timestamp = sensor_df.loc[i, "datetime"] if "datetime" in sensor_df.columns else i
-
-            # 下限チェック
-            if pd.notna(min_val) and pd.notna(value) and value < min_val:
-                alerts.append({
-                    "timestamp": timestamp,
-                    "metric": metric,
-                    "value": value,
-                    "status": f"下限値 {min_val} 未満",
-                    "data no.": data_no # Add the 'data no.'
-                })
-
-            # 上限チェック
-            if pd.notna(max_val) and pd.notna(value) and value > max_val:
-                alerts.append({
-                    "timestamp": timestamp,
-                    "metric": metric,
-                    "value": value,
-                    "status": f"上限値 {max_val} 超過",
-                    "data no.": data_no # Add the 'data no.'
-                })
-
-    return pd.DataFrame(alerts)
-
-
 import gradio as gr
-import pandas as pd
-import supabase
-
-# Assuming the data loading and check_thresholds function from the previous cell are available
-
-def run_troubleshooting():
-    # Ensure dataframes are accessible, assuming they are global or loaded here
-    # If not global, you would need to reload or pass them as arguments
-    try:
-        threshold_df
-        sensor_df
-        troubleshooting_df
-    except NameError:
-        # Reload data if dataframes are not defined
-        # SUPABASE_URL = userdata.get('SUPABASE_URL')
-        # SUPABASE_KEY = userdata.get('SUPABASE_KEY')
-        supabase_client = supabase.create_client(SUPABASE_URL, SUPABASE_KEY)
-        table_threshold = "Threshold_data"
-        table_sensor = "Sensor_data"
-        table_troubleshooting = "Troubleshooting_collection"
-        threshold_data = supabase_client.table(table_threshold).select("*").execute()
-        sensor_data = supabase_client.table(table_sensor).select("*").execute()
-        troubleshooting_data = supabase_client.table(table_troubleshooting).select("*").execute()
-        threshold_df = pd.DataFrame(threshold_data.data)
-        sensor_df = pd.DataFrame(sensor_data.data)
-        troubleshooting_df = pd.DataFrame(troubleshooting_data.data)
-
-
-    alerts_df = check_thresholds(sensor_df, threshold_df)
-
-    grouped_alerts = alerts_df.groupby('timestamp')['data no.'].nunique()
-    multiple_data_nos_timestamps = grouped_alerts[grouped_alerts > 1].index.tolist()
-
-    filtered_alerts_df = alerts_df[alerts_df['timestamp'].isin(multiple_data_nos_timestamps)]
-
-    data_nos_by_timestamp = filtered_alerts_df.groupby('timestamp')['data no.'].unique().apply(list)
-
-    result_list = []
-    for timestamp, data_nos in data_nos_by_timestamp.items():
-        data_nos_str = ', '.join(map(str, data_nos))
-        result_list.append({"timestamp": timestamp, "data_nos": data_nos_str})
-
-    result_df = pd.DataFrame(result_list)
-
-    troubleshooting_indicator_lists = troubleshooting_df['指標No.'].str.split(',').apply(lambda x: [int(i) for i in x])
-    result_data_nos_lists = result_df['data_nos'].str.split(', ').apply(lambda x: [int(i) for i in x])
-
-    output_text = ""
-    for i, result_nos in enumerate(result_data_nos_lists):
-        result_timestamp = result_df.loc[i, 'timestamp']
-        for j, troubleshooting_nos in enumerate(troubleshooting_indicator_lists):
-            if set(troubleshooting_nos).issubset(set(result_nos)):
-                troubleshooting_situation = troubleshooting_df.loc[j, 'シチュエーション\n（対応が必要な状況）']
-                troubleshooting_action = troubleshooting_df.loc[j, 'sub goal到達のために必要な行動\n（解決策）']
-
-                output_text += f"Timestamp: {result_timestamp}\n"
-                output_text += f"Trouble: {troubleshooting_situation}\n"
-                output_text += f"Troubleshooting: {troubleshooting_action}\n"
-                output_text += "-" * 20 + "\n" # 区切り線
 
-    return output_text
+def display_message():
+    return "現在、以下のトラブルが発生しています。アンモニア態窒素が1,000mg/Lを超えました。"
 
-iface = gr.Interface(
-    fn=run_troubleshooting,
-    inputs=None,  # No direct input needed as it uses existing dataframes
-    outputs="text",
-    title="Troubleshooting Information",
-    description="Displays troubleshooting information based on sensor and threshold data."
-)
+with gr.Blocks() as demo:
+    gr.Markdown("# アンモニア態窒素ステータス")
+    output_text = gr.Textbox(label="ステータス", value=display_message(), interactive=False)
 
-iface.launch(mcp_server=True)
+demo.launch(mcp_server=True)