Add Supabase data import functionality and environment variable support

- Introduced a new .gitignore file to exclude virtual environment and configuration files.
- Enhanced app.py to load environment variables for Supabase connection and added functions for creating tables and importing data from CSV files.
- Updated README.md to reflect changes in project title and SDK version.
- Added requirements.txt to manage dependencies including Gradio, Supabase, and dotenv.

.gitignore

@@ -0,0 +1,3 @@
+.venv/
+*.un~
+.env

README.md

@@ -1,15 +1,12 @@
 ---
-title: Operation Data Analysis3
-emoji: 💬
-colorFrom: yellow
-colorTo: purple
+title: Operation Data Analysis
+emoji: 🦀
+colorFrom: gray
+colorTo: gray
 sdk: gradio
-sdk_version: 5.42.0
+sdk_version: 5.44.1
 app_file: app.py
 pinned: false
-hf_oauth: true
-hf_oauth_scopes:
-- inference-api
 ---
 
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,70 +1,150 @@
+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 gradio as gr
-from huggingface_hub import InferenceClient
-
-
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
+import pandas as pd
+import math
+
+# ▼ Supabaseクライアントは事前に初期化しておく必要があります
+# from supabase import create_client
+# supabase_client = create_client(SUPABASE_URL, SUPABASE_KEY)
+
+
+def pandas_dtype_to_supabase_type(dtype):
+    """Maps pandas data types to Supabase SQL data types."""
+    if pd.api.types.is_integer_dtype(dtype):
+        return "integer"
+    elif pd.api.types.is_float_dtype(dtype):
+        return "double precision"
+    elif pd.api.types.is_bool_dtype(dtype):
+        return "boolean"
+    elif pd.api.types.is_datetime64_any_dtype(dtype):
+        return "timestamp with time zone"
+    else:
+        return "text"
+
+
+def create_table_if_not_exists(db_name, dataframe):
     """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+    Creates a table in Supabase if it doesn't exist, using RPC `exec_sql`.
     """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
-
-    messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+    columns = []
+    for col_name, dtype in dataframe.dtypes.items():
+        supabase_type = pandas_dtype_to_supabase_type(dtype)
+        columns.append(f'"{col_name}" {supabase_type}')
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+    columns_sql = ", ".join(columns)
+    create_table_sql = f"""
+    CREATE TABLE IF NOT EXISTS public."{db_name}" (
+        id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+        {columns_sql}
+    );
+    """
+    print(f"Executing SQL for table creation:\n{create_table_sql}")
+
+    try:
+        response = supabase_client.rpc("exec_sql", {"sql": create_table_sql}).execute()
+
+        resp_data = getattr(response, "data", None)
+        resp_error = getattr(response, "error", None)
+
+        if resp_error:
+            return False, f"Error creating table: {getattr(resp_error, 'message', str(resp_error))}"
+
+        if resp_data:
+            return True, f"Table '{db_name}' created or already exists. RPC returned data: {resp_data}"
+        else:
+            return True, f"Table '{db_name}' created or already exists. (no data returned)"
 
+    except Exception as e:
+        print(f"Exception during table creation RPC call: {e}")
+        return False, f"Exception during table creation: {e}"
+
+
+def clean_dataframe_for_json(df: pd.DataFrame):
+    """
+    Convert DataFrame to JSON-compliant list of dicts
+    (replace NaN, NaT, inf, -inf with None).
+    """
+    records = df.to_dict(orient="records")
+    cleaned = []
+    for row in records:
+        new_row = {}
+        for k, v in row.items():
+            if v is None:
+                new_row[k] = None
+            elif isinstance(v, float) and (math.isnan(v) or math.isinf(v)):
+                new_row[k] = None
+            else:
+                new_row[k] = v
+        cleaned.append(new_row)
+    return cleaned
+
+
+def import_data_to_supabase(csv_file, db_name):
+    """
+    Imports data from a CSV file to a Supabase table,
+    creating the table if it doesn't exist.
+    """
+    global supabase_client
+    full_message = []
+
+    if not csv_file or not db_name:
+        return "Please upload a CSV file and provide a table name."
+
+    try:
+        # 1. CSV読込
+        df = pd.read_csv(csv_file.name)
+
+        # 2. テーブル作成
+        table_creation_success, table_creation_msg = create_table_if_not_exists(db_name, df)
+        full_message.append(f"Table Creation Status: {table_creation_msg}")
+
+        # 3. JSON対応にクリーニング（NaN, NaT, inf → None）
+        data = clean_dataframe_for_json(df)
+
+        if not data:
+            full_message.append("Data Insertion Status: CSV file empty or not parsed. No data inserted.")
+            return "\n".join(full_message)
+
+        # 4. データ挿入
+        print(f"Attempting to insert {len(data)} rows into table '{db_name}'.")
+        response = supabase_client.table(db_name).insert(data).execute()
+
+        resp_data = getattr(response, "data", None)
+        resp_error = getattr(response, "error", None)
+
+        if resp_error:
+            full_message.append(
+                f"Data Insertion Status: Error inserting data - {getattr(resp_error, 'message', str(resp_error))}"
+            )
+        elif resp_data:
+            full_message.append(f"Data Insertion Status: Successfully inserted {len(resp_data)} rows.")
+        else:
+            full_message.append("Data Insertion Status: Insert executed, but no data returned.")
+
+    except Exception as e:
+        full_message.append(f"An unexpected error occurred during processing: {e}")
+
+    return "\n".join(full_message)
+
+
+# Gradio アプリ
+iface = gr.Interface(
+    fn=import_data_to_supabase,
+    inputs=[
+        gr.File(label="Upload CSV File"),
+        gr.Textbox(label="Supabase Database Table Name")
+    ],
+    outputs="text",
+    title="Supabase Data Importer",
+    description="CSVファイルをアップロードし、データをSupabaseデータベースのテーブルにインポートします。指定されたテーブルが存在しない場合は新しく作成します。"
+)
 
-if __name__ == "__main__":
-    demo.launch()
+iface.launch(mcp_server=True)

requirements.txt

@@ -0,0 +1,4 @@
+gradio[mcp]
+supabase
+numpy
+python-dotenv