Delete Untitled.ipynb

Untitled.ipynb

@@ -1,761 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "404d2feb-8a5c-4a6e-9012-bd88edd0b5bb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "data\t\t  __pycache__\t    run.py\t\t     Untitled.ipynb\n",
-      "JointBERT-master  requirements.txt  tableQA_single_table.py\n"
-     ]
-    }
-   ],
-   "source": [
-    "!ls"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "b729ace0-35b0-4ba3-b44f-b907deabf4fd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "from gradio import *"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "b1c9b471-9078-4413-b8e5-ed21c0f858fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from run import *"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "151526b1-8ae0-4c5f-86e0-de3afd80cd73",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "szse_summary_df = pd.read_csv(os.path.join(main_path ,\"data/df1.csv\"))\n",
-    "tableqa_ = \"数据表问答（编辑数据）\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "9a5982e8-fb75-4a90-bca9-80a1b394e691",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "default_val_dict = {\n",
-    "    tableqa_ :{\n",
-    "            \"tqa_question\": \"EPS大于0且周涨跌大于5的平均市值是多少？\",\n",
-    "            \"tqa_header\": szse_summary_df.columns.tolist(),\n",
-    "            \"tqa_rows\": szse_summary_df.values.tolist(),\n",
-    "            \"tqa_data_path\": os.path.join(main_path ,\"data/df1.csv\"),\n",
-    "            \"tqa_answer\":   {\n",
-    "    \"sql_query\":  \"SELECT AVG(col_4) FROM Mem_Table WHERE col_5 > 0 and col_3 > 5\",\n",
-    "    \"cnt_num\":  2,\n",
-    "    \"conclusion\":  [57.645]\n",
-    "        }\n",
-    "    }\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "10098d86-3b56-4fff-a2fd-1eca344cc114",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "###default_val_dict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "c5eb6ca8-0317-4e63-8407-7f3033164dad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tableqa_layer(post_data):\n",
-    "    question = post_data[\"question\"]\n",
-    "    table_rows = post_data[\"table_rows\"]\n",
-    "    table_header = post_data[\"table_header\"]\n",
-    "    assert all(map(lambda x: type(x) == type(\"\"), [question, table_rows, table_header]))\n",
-    "    table_rows = json.loads(table_rows)\n",
-    "    table_header = json.loads(table_header)\n",
-    "\n",
-    "    assert all(map(lambda x: type(x) == type([]), [table_rows, table_header]))\n",
-    "    if bool(table_rows) and bool(table_header):\n",
-    "        assert len(table_header) == len(table_rows[0])\n",
-    "    df = pd.DataFrame(table_rows, columns = table_header)\n",
-    "    conclusion = single_table_pred(question, df)\n",
-    "    return conclusion"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "dab2d0e0-3e09-465d-9a02-af17d79cc8ea",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_tableqa(*input):\n",
-    "    question, data = input\n",
-    "    header = data.columns.tolist()\n",
-    "    rows = data.values.tolist()\n",
-    "\n",
-    "    rows = list(filter(lambda x: any(map(lambda xx: bool(xx), x)), rows))\n",
-    "\n",
-    "    assert all(map(lambda x: type(x) == type([]), [header, rows]))\n",
-    "    header = json.dumps(header)\n",
-    "    rows = json.dumps(rows)\n",
-    "\n",
-    "    assert all(map(lambda x: type(x) == type(\"\"), [question, header, rows]))\n",
-    "    \n",
-    "    resp = tableqa_layer(\n",
-    "        {\n",
-    "            \"question\": question,\n",
-    "            \"table_header\": header,\n",
-    "            \"table_rows\": rows\n",
-    "        }\n",
-    "    )\n",
-    "    if \"cnt_num\" in resp:\n",
-    "        if hasattr(resp[\"cnt_num\"], \"tolist\"):\n",
-    "            resp[\"cnt_num\"] = resp[\"cnt_num\"].tolist()\n",
-    "    if \"conclusion\" in resp:\n",
-    "        if hasattr(resp[\"conclusion\"], \"tolist\"):\n",
-    "            resp[\"conclusion\"] = resp[\"conclusion\"].tolist()\n",
-    "    '''\n",
-    "    import pickle as pkl\n",
-    "    with open(\"resp.pkl\", \"wb\") as f:\n",
-    "        pkl.dump(resp, f)\n",
-    "    print(resp)\n",
-    "    '''\n",
-    "    resp = json.loads(json.dumps(resp))\n",
-    "    return resp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "62b96be0-d491-42f7-9655-0f65f38e1d75",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "###\n",
-    "###np.asarray(2).tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "b63dc5ae-49af-4fa7-abf0-681fdb37e2e2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "###json.loads(json.dumps(default_val_dict))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "41abba16-830e-4755-a783-92fef780d3e5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "demo = gr.Blocks(css=\".container { max-width: 800px; margin: auto; }\")\n",
-    "\n",
-    "with demo:\n",
-    "    gr.Markdown(\"\")\n",
-    "    with gr.Tabs():\n",
-    "        #### tableqa\n",
-    "        with gr.TabItem(\"数据表问答（TableQA）\"):\n",
-    "\n",
-    "            with gr.Tabs():\n",
-    "                with gr.TabItem(tableqa_):\n",
-    "                    tqa_question = gr.Textbox(\n",
-    "                    default_val_dict[tableqa_][\"tqa_question\"],\n",
-    "                    label = \"问句：(输入)\"\n",
-    "                    )\n",
-    "\n",
-    "                    tqa_data = gr.Dataframe(\n",
-    "                    headers=default_val_dict[tableqa_][\"tqa_header\"],\n",
-    "                    value=default_val_dict[tableqa_][\"tqa_rows\"],\n",
-    "                    row_count = len(default_val_dict[tableqa_][\"tqa_rows\"]) + 1\n",
-    "                    )\n",
-    "\n",
-    "                    tqa_answer = JSON(\n",
-    "                    default_val_dict[tableqa_][\"tqa_answer\"],\n",
-    "                    label = \"问句：（输出）\"\n",
-    "                    )\n",
-    "\n",
-    "                    tqa_button = gr.Button(\"得到答案\")\n",
-    "\n",
-    "                tqa_button.click(run_tableqa, inputs=[\n",
-    "                    tqa_question,\n",
-    "                    tqa_data\n",
-    "                ], outputs=tqa_answer)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "68a25da4-a514-45c4-8d49-264210b14f87",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running on local URL:  http://172.16.56.206:7860\n",
-      "Running on public URL: https://c7bd449621083f2690.gradio.live\n",
-      "\n",
-      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><iframe src=\"https://c7bd449621083f2690.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Building prefix dict from the default dictionary ...\n",
-      "Loading model from cache /tmp/jieba.cache\n",
-      "Loading model cost 0.680 seconds.\n",
-      "Prefix dict has been built successfully.\n"
-     ]
-    }
-   ],
-   "source": [
-    "demo.launch(server_name=\"172.16.56.206\" ,share = True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2bcddf9e-b269-4d74-a4c7-c1ad294e2408",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3d7c4e4f-0abb-43a1-aa40-3fe05aeb8e2e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "35169744-0f5e-48be-b844-31eb35f0ce69",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "66693900-ffeb-473e-b411-465fd33b2ea9",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "e1494b23-b335-441d-a572-928bf99f20d8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pickle as pkl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "aed88b91-f662-4f2a-b62b-c8eb062d145d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(\"resp.pkl\", \"rb\") as f:\n",
-    "    resp = pkl.load(f)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "0329da8a-e16c-42f8-8efb-2bac80902285",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sql_query': 'SELECT AVG(col_4) FROM Mem_Table WHERE col_5 > 0 and col_3 > 5',\n",
-       " 'cnt_num': 2,\n",
-       " 'conclusion': [57.645]}"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "resp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "6241edc4-35da-427b-8fa7-356f7c20a547",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.int64"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(resp[\"cnt_num\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "0fe9930d-fc27-4840-a8dd-ed91d44a922b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if \"cnt_num\" in resp:\n",
-    "    if hasattr(resp[\"cnt_num\"], \"tolist\"):\n",
-    "        resp[\"cnt_num\"] = resp[\"cnt_num\"].tolist()\n",
-    "if \"conclusion\" in resp:\n",
-    "    if hasattr(resp[\"conclusion\"], \"tolist\"):\n",
-    "        resp[\"conclusion\"] = resp[\"conclusion\"].tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "1e23ef44-acef-4aaa-9d36-0ccd97184d92",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'{\"sql_query\": \"SELECT AVG(col_4) FROM Mem_Table WHERE col_5 > 0 and col_3 > 5\", \"cnt_num\": 2, \"conclusion\": [57.645]}'"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "json.dumps(resp)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2523825d-2483-4149-ae16-b2b5f4177359",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "574628ab-e413-4dfe-9d29-133d0a79c80e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "921bd428-a667-429c-8142-6d9aef9c9fb6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "791fee503c3f4aefb4802442d050b1ef",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Downloading:   0%|          | 0.00/110k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from tableQA_single_table import *"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a5890d29-fc99-4bf9-aa61-d55c1989aea9",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0ac20004-cfa9-4259-b324-b2ea4fd9f8e5",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5362b7f7-7d3d-4d2a-b3a3-ead266fad51e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5cc6eb17-f50d-46ac-b516-311c24b62376",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "b89e0bde-662d-48c6-801f-60bedf873846",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "import os\n",
-    "import sys"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "60682115-d2f4-417f-9347-f5feb4261750",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def run_sql_query(s, df):\n",
-    "    conn = sqlite3.connect(\":memory:\")\n",
-    "\n",
-    "    assert isinstance(df, pd.DataFrame)\n",
-    "    question_column = s.question_column\n",
-    "    if question_column is None:\n",
-    "        return {\n",
-    "            \"sql_query\": \"\",\n",
-    "            \"cnt_num\": 0,\n",
-    "            \"conclusion\": []\n",
-    "        }\n",
-    "    total_conds_filtered = s.total_conds_filtered\n",
-    "    agg_pred = s.agg_pred\n",
-    "    conn_pred = s.conn_pred\n",
-    "    sql_format = \"SELECT {} FROM {} {}\"\n",
-    "    header = df.columns.tolist()\n",
-    "    if len(header) > len(set(header)):\n",
-    "        req = []\n",
-    "        have_req = set([])\n",
-    "        idx = 0\n",
-    "        for h in header:\n",
-    "            if h in have_req:\n",
-    "                idx += 1\n",
-    "                req.append(\"{}_{}\".format(h, idx))\n",
-    "            else:\n",
-    "                req.append(h)\n",
-    "            have_req.add(h)\n",
-    "        header = req\n",
-    "    def format_right(val):\n",
-    "        val = str(val)\n",
-    "        is_string = True\n",
-    "        try:\n",
-    "            literal_eval(val)\n",
-    "            is_string = False\n",
-    "        except:\n",
-    "            pass\n",
-    "        if is_string:\n",
-    "            return \"'{}'\".format(val)\n",
-    "        else:\n",
-    "            return val\n",
-    "    #ic(question_column, header)\n",
-    "    assert question_column in header\n",
-    "    assert all(map(lambda t3: t3[0] in header, total_conds_filtered))\n",
-    "    assert len(header) == len(set(header))\n",
-    "    index_header_mapping = dict(enumerate(header))\n",
-    "    header_index_mapping = dict(map(lambda t2: (t2[1], t2[0]) ,index_header_mapping.items()))\n",
-    "    assert len(index_header_mapping) == len(header_index_mapping)\n",
-    "    df_saved = df.copy()\n",
-    "    df_saved.columns = list(map(lambda idx: \"col_{}\".format(idx), range(len(header))))\n",
-    "    df_saved.to_sql(\"Mem_Table\", conn, if_exists = \"replace\", index = False)\n",
-    "    question_column_idx = header.index(question_column)\n",
-    "    sql_question_column = \"col_{}\".format(question_column_idx)\n",
-    "    sql_total_conds_filtered = list(map(lambda t3: (\"col_{}\".format(header.index(t3[0])), t3[1], format_right(t3[2])), total_conds_filtered))\n",
-    "    sql_agg_pred = agg_pred\n",
-    "    if sql_agg_pred.strip():\n",
-    "        sql_agg_pred = \"{}()\".format(sql_agg_pred)\n",
-    "    else:\n",
-    "        sql_agg_pred = \"()\"\n",
-    "    sql_agg_pred = sql_agg_pred.replace(\"()\", \"({})\")\n",
-    "    sql_conn_pred = conn_pred\n",
-    "    if sql_conn_pred.strip():\n",
-    "        pass\n",
-    "    else:\n",
-    "        sql_conn_pred = \"\"\n",
-    "    #sql_where_string = \"\" if not (sql_total_conds_filtered and sql_conn_pred) else \"WHERE {}\".format(\" {} \".format(sql_conn_pred).join(map(lambda t3: \"{} {} {}\".format(t3[0],\"=\" if t3[1] == \"==\" else t3[1], t3[2]), sql_total_conds_filtered)))\n",
-    "    sql_where_string = \"\" if not (sql_total_conds_filtered) else \"WHERE {}\".format(\" {} \".format(sql_conn_pred if sql_conn_pred else \"and\").join(map(lambda t3: \"{} {} {}\".format(t3[0],\"=\" if t3[1] == \"==\" else t3[1], t3[2]), sql_total_conds_filtered)))\n",
-    "    #ic(sql_total_conds_filtered, sql_conn_pred, sql_where_string, s)\n",
-    "    sql_query = sql_format.format(sql_agg_pred.format(sql_question_column), \"Mem_Table\", sql_where_string)\n",
-    "    cnt_sql_query = sql_format.format(\"COUNT(*)\", \"Mem_Table\", sql_where_string).strip()\n",
-    "    #ic(cnt_sql_query)\n",
-    "    cnt_num = pd.read_sql(cnt_sql_query, conn).values.reshape((-1,))[0]\n",
-    "    if cnt_num == 0:\n",
-    "        return {\n",
-    "            \"sql_query\": sql_query,\n",
-    "            \"cnt_num\": 0,\n",
-    "            \"conclusion\": []\n",
-    "        }\n",
-    "    query_conclusion_list = pd.read_sql(sql_query, conn).values.reshape((-1,)).tolist()\n",
-    "    return {\n",
-    "            \"sql_query\": sql_query,\n",
-    "            \"cnt_num\": cnt_num,\n",
-    "            \"conclusion\": query_conclusion_list\n",
-    "        }\n",
-    "\n",
-    "#save_conn = sqlite3.connect(\":memory:\")\n",
-    "def single_table_pred(question, pd_df):\n",
-    "    assert type(question) == type(\"\")\n",
-    "    assert isinstance(pd_df, pd.DataFrame)\n",
-    "    qs_df = pd.DataFrame([[question]], columns = [\"question\"])\n",
-    "\n",
-    "    #print(\"pd_df :\")\n",
-    "    #print(pd_df)\n",
-    "\n",
-    "    tableqa_df = full_before_cat_decomp(pd_df, qs_df, only_req_columns=False)\n",
-    "\n",
-    "    #print(\"tableqa_df :\")\n",
-    "    #print(tableqa_df)\n",
-    "\n",
-    "    assert tableqa_df.shape[0] == 1\n",
-    "    #sql_query_dict = run_sql_query(tableqa_df.iloc[0], pd_df, save_conn)\n",
-    "    sql_query_dict = run_sql_query(tableqa_df.iloc[0], pd_df)\n",
-    "    return sql_query_dict\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "a85ca3ee-3d78-4605-aad1-17e29f557c77",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "szse_summary_df = pd.read_csv(os.path.join(main_path ,\"data/df1.csv\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "090a2750-5c6c-454f-bb0d-d9eabfb00721",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = {\n",
-    "            \"tqa_question\": \"EPS大于0且周涨跌大于5的平均市值是多少？\",\n",
-    "            \"tqa_header\": szse_summary_df.columns.tolist(),\n",
-    "            \"tqa_rows\": szse_summary_df.values.tolist(),\n",
-    "            \"tqa_data_path\": os.path.join(main_path ,\"data/df1.csv\"),\n",
-    "            \"tqa_answer\":   {\n",
-    "    \"sql_query\":  \"SELECT AVG(col_4) FROM Mem_Table WHERE col_5 > 0 and col_3 > 5\",\n",
-    "    \"cnt_num\":  2,\n",
-    "    \"conclusion\":  [57.645]\n",
-    "    }\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "b9bbaf84-53f5-4fac-82f9-3cdb2a6a355a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd_df = pd.DataFrame(data[\"tqa_rows\"], columns = data[\"tqa_header\"])\n",
-    "question = data[\"tqa_question\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "92fd092e-58ec-4951-850d-dc1cba07adea",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sql_query': 'SELECT AVG(col_4) FROM Mem_Table WHERE col_5 > 0 and col_3 > 5',\n",
-       " 'cnt_num': 2,\n",
-       " 'conclusion': [57.645]}"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "single_table_pred(question, pd_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "27cfe48f-49df-4e95-b896-6ad499851920",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "89cddb81-0b82-4f30-b553-60d1c9a9b007",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "892fa614-7b9e-440e-bac1-7b4c68641fbb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = {\n",
-    "        \"question\": \"翔的出生地是什么？\",\n",
-    "        \"table_header\": json.dumps(\n",
-    "            [\"姓名\", \"年龄\", \"出生地\"]\n",
-    "        ),\n",
-    "        \"table_rows\": json.dumps(\n",
-    "            [\n",
-    "                [\"王翔\", 31, \"宁波\"],\n",
-    "                [\"王雨\", 12, \"翔雨宾馆\"]\n",
-    "            ]\n",
-    "        )\n",
-    "    }"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "261de835-fe0b-4c4f-ac0b-70d0f2026f4d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "question = data[\"question\"]\n",
-    "question = \"年龄是王翔的是什么？\"\n",
-    "pd_df = pd.DataFrame(json.loads(data[\"table_rows\"]), columns = json.loads(data[\"table_header\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "fe1cb949-049f-47f1-97cc-37f34a369bc6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sql_query': 'SELECT (col_1) FROM Mem_Table ',\n",
-       " 'cnt_num': 2,\n",
-       " 'conclusion': [31, 12]}"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "single_table_pred(question, pd_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f4f42ab2-6ab7-405a-b4f0-2e925878ed77",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}