# !/usr/bin/env python # -*- coding:utf-8 -*- # ================================================================== # [CreatedDate] : Thursday, 1970-01-01 08:00:00 # [Author] : shixiaofeng # [Descriptions] : # ================================================================== # [ChangeLog]: # [Date] [Author] [Comments] # ------------------------------------------------------------------ import json import logging import time import gradio as gr import requests logger = logging.getLogger('gradio') gr.close_all() host = "127.0.0.1:9172" def tableqa(input_question, history=""): logger.info("run tableqa") if history == "": history_sql = None else: history_sql = json.loads(history.replace("\'", "\"")) data = {"raw_data": {'question': input_question, 'history_sql': history_sql}} ts = time.time() r = requests.post(f"http://{host}/tableqa", json=data) response = json.loads(r.text) print("response", response) te = time.time() print("run inference_mask_sam success [{}], time_Cost is [{}]".format(response["code"] == 200, te-ts)) if response["code"] == 200: df_value = response["result"]["select_df"] df = {"data": df_value["rows"], "headers": df_value["header_name"]} return [df, response["result"]["sql_string"], response["result"]["sql_query"], response["result"]["history"], response["result"]["query_result"]] else: return ["1", "2", "3", "4",] example_iface = [ ["长江流域的小型水库的库容总量是多少?", ""], ["那平均值是多少?", "{'agg': [5], 'cond_conn_op': 1, 'conds': [[3, 2, '小型'], [4, 2, '长江']], 'from': ['reservoir'], 'sel': [2]}"], ["那水库的名称呢?", "{'agg': [1], 'cond_conn_op': 1, 'conds': [[3, 2, '小型'], [4, 2, '长江']], 'from': ['reservoir'], 'sel': [2]}"], ["汕尾市的水库有吗", "{'agg': [0], 'cond_conn_op': 1, 'conds': [[3, 2, '小型'], [4, 2, '长江']], 'from': ['reservoir'], 'sel': [0]}"], ["", ""], ["上个月收益率超过3的有几个基金?", ""], ["这是哪只基金呢?并且它什么类型的呢?", "{'agg': [4], 'cond_conn_op': 0, 'conds': [[5, 0, '3']], 'from': ['fund'], 'sel': [1]}"], ["", ""], ["有哪些型号的SUV油耗高于8?", ""], ["他们是多大排量的", "{'agg': [0], 'cond_conn_op': 1, 'conds': [[1, 2, 'suv'], [2, 0, '8']], 'from': ['car'], 'sel': [0]}"], ["", ""], ["本部博士生中平均身高是多少?", ""], ["他们是什么专业的?", "{'agg': [1], 'cond_conn_op': 1, 'conds': [[2, 2, '博士'], [7, 2, '本部']], 'from': ['student'], 'sel': [5]}"] ] # iface = gr.Interface(fn=greet, inputs="text", outputs=["输出sql语句","输出可执行sql语句","执行结果"]) iface = gr.Interface(fn=tableqa, inputs=[gr.Textbox(label="input_question", info="请输入想要查询的问题."), gr.Textbox(label="history sql", info="上下文对话历史信息.")], outputs=[gr.DataFrame(label="索引到的数据库"), gr.Textbox(label="输出sql语句"), gr.Textbox(label="输出可执行sql语句"), gr.Textbox(label="多轮对话历史sql"), gr.Textbox(label="SQL执行结果")], examples=example_iface, examples_per_page=len(example_iface), allow_flagging="auto", cache_examples=True, description="

\ Choose an example below 🔥 🔥 🔥 \ Or, give question by yourself:
\

", ) title = "TableChat: Chat Model deployment on Table
" demo = gr.TabbedInterface([iface], ['TableChat_V0'], title=title) # iface.launch(enable_queue=False, server_name="0.0.0.0", server_port=9176, debug=True) demo.launch(enable_queue=False, server_name="0.0.0.0", server_port=9176, share=True)