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feat: 增加base64传入动作指令

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feat: 换用 deepseek 大模型
pref: 修改应急避险2停车条件
This commit is contained in:
bmy
2024-08-04 10:04:27 +08:00
parent 55f5b13d8c
commit f3bb720bed
7 changed files with 278 additions and 130 deletions
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16
app.py
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@@ -10,7 +10,8 @@ import os
import time
import subprocess
import signal
import importlib
import base64
import json
from main_upper import main_func
server_command = [
{"path": "/home/evan/Workplace/project_capture/build/", "script": "./capture"},
@@ -51,7 +52,7 @@ logger.add(handler, format="{time:MM-DD HH:mm:ss} {message}", level="DEBUG")
fileOptions_path = '/home/evan/Workplace/project_main'
fileOptions_list = ['cfg_args.toml','cfg_main.toml', 'cfg_subtask.toml']
cfg_args_path = os.path.join(fileOptions_path, 'cfg_args.toml')
cfg_move_area_path = os.path.join(fileOptions_path, 'cfg_move_area.json')
@app.route('/')
def index():
@@ -64,6 +65,17 @@ def run():
config_args['lane_mode']['mode_index'] = int(mode_index)
with open(cfg_args_path, 'w') as config_file:
toml.dump(config_args, config_file)
try:
action_base64 = request.args.get('action')
decoded_bytes = base64.b64decode(action_base64)
decoded_str = decoded_bytes.decode('utf-8')
json_data = json.loads(decoded_str)
with open(cfg_move_area_path, 'w') as json_file:
json.dump(json_data, json_file)
except:
# 当该字段没有传入参数时 清空配置文件 该任务按照正常流程去做
with open(cfg_move_area_path, 'w') as json_file:
pass
return render_template('index2.html')
# @app.route('/csdn')

2
cfg_main.toml
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@@ -15,5 +15,5 @@ PutHanoi1_counts = 7
PutHanoi2_counts = 2
PutHanoi3_counts = 2
MoveArea1_counts = 6
MoveArea2_counts = 1700
MoveArea2_counts = 10
KickAss_counts = 10

0
cfg_move_area.json Normal file
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165
subtask.py
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@@ -3,6 +3,7 @@ from loguru import logger
from utils import label_filter
from utils import tlabel
from utils import LLM
from utils import LLM_deepseek
from utils import CountRecord
import utils
import toml
@@ -12,10 +13,13 @@ import variable as var
import action as act
import re
import math
import json
import json5
# import threading
# import ctypes
cfg = None
cfg_args = None
cfg_move_area = None
by_cmd = None
filter = None
llm_bot = None
@@ -48,9 +52,15 @@ def import_obj(_by_cmd, skip_queue):
global cfg
global cfg_args
global cfg_move_area
global global_skip_queue
cfg = toml.load('/home/evan/Workplace/project_main/cfg_subtask.toml') # 加载任务配置
cfg_args = toml.load('/home/evan/Workplace/project_main/cfg_args.toml')
try:
with open('/home/evan/Workplace/project_main/cfg_move_area.json', 'r') as f:
cfg_move_area = json.load(f)
except:
cfg_move_area = None
by_cmd = _by_cmd
global_skip_queue = skip_queue
@@ -68,7 +78,7 @@ def import_obj(_by_cmd, skip_queue):
filter = label_filter(socket)
if cfg['move_area']['llm_enable']:
llm_bot = LLM()
llm_bot = LLM_deepseek()
def car_stop():
for _ in range(3):
by_cmd.send_speed_x(0)
@@ -467,7 +477,7 @@ class get_block1():
time.sleep(0.1)
by_cmd.send_position_axis_x(1, 100)
time.sleep(1)
by_cmd.send_position_axis_z(30, 90)
by_cmd.send_position_axis_z(30, 100)
time.sleep(0.5)
by_cmd.send_angle_claw(63)
time.sleep(0.5)
@@ -572,7 +582,7 @@ class put_block():
by_cmd.send_angle_claw(85)
# by_cmd.send_angle_storage(0)
time.sleep(1)
by_cmd.send_position_axis_z(30,90)
by_cmd.send_position_axis_z(30,100)
time.sleep(1)
by_cmd.send_angle_claw(25)
by_cmd.send_distance_x(-10, 110)
@@ -703,13 +713,13 @@ class up_tower():
by_cmd.send_distance_x(-10, 120)
time.sleep(1)
# 上古參數
by_cmd.send_distance_y(-10, 80)
by_cmd.send_distance_y(-10, 50) # 80
# 6_9 模型參數
# by_cmd.send_distance_y(-10, 40)
# 7_12_3 模型參數
# by_cmd.send_distance_y(-10, 50)
time.sleep(2)
car_stop()
# time.sleep(2)
# car_stop()
# FIXME 如果下發 distance 後直接 car_stop，則 distance 執行時間僅由指令間處理延時決定
# time.sleep(3)
# by_cmd.send_speed_y(-10)
@@ -759,21 +769,21 @@ class get_rball():
# 靠近塔
by_cmd.send_angle_scoop(20)
# 上古參數
by_cmd.send_distance_y(-15, 70) # 50
by_cmd.send_distance_y(-15, 50) # 50 # 70
# 6_9 參數
# by_cmd.send_distance_y(-15, 35)
# time.sleep(2)
# 7_12_3 參數
# by_cmd.send_distance_y(-15, 45)
time.sleep(2)
car_stop()
# time.sleep(2)
# car_stop()
calibrate_new(tlabel.RBALL,offset = 44, run = True)
time.sleep(1)
logger.info("抓红球")
# by_cmd.send_angle_scoop(12)
time.sleep(0.5)
by_cmd.send_position_axis_z(30, 200)
time.sleep(2.5)
time.sleep(3)
by_cmd.send_angle_scoop(12)
time.sleep(0.5)
by_cmd.send_angle_scoop(7)
@@ -1255,33 +1265,36 @@ class move_area1():
# filter_w = (148, 560)
# filter_h = (165, 390)
if cfg_move_area == None:
counts = 0
while True:
ocr_socket.send_string("")
resp = ocr_socket.recv_pyobj()
var.llm_text = ''
counts += 1
if resp.get('code') == 0:
for item in resp.get('content'):
counts = 0
while True:
ocr_socket.send_string("")
resp = ocr_socket.recv_pyobj()
var.llm_text = ''
counts += 1
if resp.get('code') == 0:
for item in resp.get('content'):
if item['probability']['average'] < 0.80:
continue
# box = item['location']
# center_x = box['left'] + box['width'] / 2
# center_y = box['top'] + box['height'] / 2
# if center_x < filter_w[0] or center_x > filter_w[1] \
# or center_y < filter_h[0] or center_y > filter_h[1]:
# continue
var.llm_text += item['words']
break
if counts >= 2:
if item['probability']['average'] < 0.80:
continue
# box = item['location']
# center_x = box['left'] + box['width'] / 2
# center_y = box['top'] + box['height'] / 2
# if center_x < filter_w[0] or center_x > filter_w[1] \
# or center_y < filter_h[0] or center_y > filter_h[1]:
# continue
var.llm_text += item['words']
break
if counts >= 2:
var.skip_llm_task_flag = True
return
logger.error(var.llm_text)
if len(var.llm_text) < 3:
var.skip_llm_task_flag = True
return
logger.error(var.llm_text)
if len(var.llm_text) < 3:
var.skip_llm_task_flag = True
return
else:
# 不需要文字识别 直接使用传入的参数执行 action
pass
var.task_speed = 9 # 12
@@ -1325,17 +1338,18 @@ class move_area2():
pass
def init(self):
logger.info("应急避险第二阶段初始化")
self.offset = 15
self.offset = 60
self.delta_x = 0
self.delta_y = 0
self.delta_omage = 0
def find(self):
if var.skip_llm_task_flag:
if var.skip_llm_task_flag and cfg_move_area == None:
return 5000
ret, box = filter.get(tlabel.SHELTER)
if ret:
error = (box[0][2] + box[0][0] - 320) / 2 + self.offset
if abs(error) < 20:
# 增加了一个宽度过滤
if abs(error) < 30 and abs(box[0][2] - box[0][0]) > 180:
return 5000
return False
def add_item(self, item):
@@ -1472,41 +1486,60 @@ class move_area2():
logger.info(f"回正后最终位置： ({self.abs_y:.2f}, {self.abs_x:.2f}), 角度： {math.degrees(self.abs_w % (2 * math.pi))}")
def exec(self):
var.task_speed = 0
if var.skip_llm_task_flag:
logger.error("ocr 识别出错 直接跳过改任务")
if var.skip_llm_task_flag and cfg_move_area == None:
logger.error("ocr 识别出错 直接跳过该任务")
return
logger.info("开始寻找停车区域")
car_stop()
calibrate_new(tlabel.SHELTER, offset = 15, run = True)
calibrate_new(tlabel.SHELTER, offset = 30, run = True)
time.sleep(0.5)
# 调用大模型 然后执行动作
try:
resp = llm_bot.get_command_json(var.llm_text)
logger.info(resp)
except:
logger.error("大模型超时，跳过任务")
return
try:
# FIXME 当前仍然可能存在文心一言分段返回和掺杂解释的问题，不确定当前条件足以过滤
resp_commands = eval(re.findall("```json(.*?)```", resp, re.S)[0])
logger.info(resp_commands)
if len(resp_commands) == 0:
if cfg_move_area == None:
# 调用大模型 然后执行动作
try:
resp = llm_bot.get_command_json(var.llm_text)
logger.info(f"llm 返回原数据 {resp}")
except:
logger.error("大模型 llm_bot 超时，跳过任务")
return
action_list = resp_commands
# 进入停车区域
by_cmd.send_distance_y(10, 450)
time.sleep((450 * 5 / 1000) + 0.5)
for action in action_list:
self.add_item(action)
time.sleep(0.1)
time.sleep(0.5)
self.reset()
except:
logger.warning("任务解析失败并退出，文心一言真是废物")
pass
try:
json_text = re.findall("```json(.*?)```", resp, re.S)
if len(json_text) == 0:
# 返回的内容不带'''json
resp_commands = eval(resp)
else:
resp_commands = json5.loads(json_text[0])
logger.info(resp_commands)
if len(resp_commands) == 0:
return
action_list = resp_commands
# 进入停车区域
by_cmd.send_distance_y(10, 450)
time.sleep((450 * 5 / 1000) + 0.5)
for action in action_list:
self.add_item(action)
time.sleep(0.1)
time.sleep(0.5)
self.reset()
except:
logger.warning("任务解析失败并退出，文心一言真是废物")
pass
else:
# 无需调用大模型 直接开始执行传入的参数
try:
by_cmd.send_distance_y(10, 450)
time.sleep((450 * 5 / 1000) + 0.5)
for action in cfg_move_area:
self.add_item(action)
time.sleep(0.1)
time.sleep(0.5)
self.reset()
except:
pass
pass
def nexec(self):
logger.warning("正在跳過大模型任務")
time.sleep(2)

8
test/test_filter.py
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@@ -16,7 +16,7 @@ socket.connect("tcp://localhost:6667")
logger.info("subtask yolo client init")
filter = label_filter(socket)
filter.switch_camera(2)
filter.switch_camera(1)
# find_counts = 0
@@ -25,10 +25,12 @@ filter.switch_camera(2)
# label = tlabel.TPLATFORM
while True:
time.sleep(0.2)
ret, box = filter.get(tlabel.BASE)
ret, box = filter.get(tlabel.SHELTER)
if ret:
error = (box[0][2] + box[0][0] - 320) / 2
logger.error(error)
if abs(error) < 30 and abs(box[0][2] - box[0][0]) > 180:
# height = box[0][3] - box[0][1]
logger.error(111)
# label = tlabel.HOSPITAL
# ret, box = filter.get(label)

8
test/test_ocr.py
View File

@@ -17,13 +17,7 @@ while True:
if resp.get('code') == 0:
text = ''
for item in resp.get('content'):
if item['probability']['average'] < 0.90:
continue
box = item['location']
center_x = box['left'] + box['width'] / 2
center_y = box['top'] + box['height'] / 2
if center_x < filter_w[0] or center_x > filter_w[1] \
or center_y < filter_h[0] or center_y > filter_h[1]:
if item['probability']['average'] < 0.80:
continue
text += item['words']
print(text)

205
utils.py
View File

@@ -2,6 +2,7 @@
from enum import Enum
import numpy as np
import erniebot
from openai import OpenAI
from simple_pid import PID
from loguru import logger
import threading
@@ -372,15 +373,72 @@ class label_filter:
error = (boxes[center_x_index][4] + boxes[center_x_index][2] - self.img_size[0]) / 2
return (True, error)
return (False, 0)
class LLM_deepseek:
def __init__(self):
self.client = OpenAI(api_key="sk-c2e1073883304143981a9750b97c3518", base_url="https://api.deepseek.com")
self.prompt = '''
你是一个机器人动作规划者，请把我的话翻译成机器人动作规划并生成对应的 JSON 结果。请注意，只能使用以下指定的动作，不能创造新的动作：
允许的动作及其对应格式如下：
[{'properties': {'index': {'title': 'Index', 'type': 'integer'}, 'action': {'title': 'Action', 'type': 'string'}, 'time': {'title': 'Time', 'type': 'number'}}, 'required': ['index', 'action', 'time'], 'title': 'Action', 'type': 'object'}]
我不允许你自我创造出新的 action,action 字段仅仅包括以下内容：
go_right 向右移动
go_left 向左移动
go_front 向前移动
go_back 向后移动
go_left_rotate 向左旋转
go_right_rotate 向右旋转
beep_seconds 蜂鸣器鸣叫的时间
beep_counts 蜂鸣器鸣叫的次数
light_seconds 灯光发光的时间
light_counts 灯光闪烁的次数
beep_light_counts 灯光和蜂鸣器一起闪烁的次数
go_sleep 什么都不做
我的话和你的回复示例如下：
我的话：向左移 0.1m, 向左转弯 85 度
你的回复：[{"index":0,"action":"go_left","time":0.1},{"index":1,"action":"go_left_rotate","time":85}]
我的话：向右移 0.2m, 向前 0.1m
你的回复：[{"index":0,"action":"go_right","time":0.2},{"index":1,"action":"go_front","time":0.1}]
我的话：向右转 90 度，向右移 0.1m
你的回复：[{"index":0,"action":"go_right_rotate","time":90},{"index":1,"action":"go_right","time":0.1}]
我的话：原地左转 38 度
你的回复：[{"index":0,"action":"go_left_rotate","time":38}]
我的话：蜂鸣器发声 5 秒
你的回复：[{"index":0,"action":"beep_seconds","time":5}]
我的话：发光或者照亮 5 秒
你的回复：[{"index":0,"action":"light_seconds","time":5}]
我的话：向右走 30cm，照亮 2s
你的回复：[{"index":0,"action":"go_right","time":0.3},{"index":1,"action":"light_seconds","time":2}]
我的话：向左移 0.2m, 向后 0.1m
你的回复：[{"index":0,"action":"go_left","time":0.2},{"index":1,"action":"go_back","time":0.1}]
我的话：鸣叫 3 声
你的回复：[{"index":0,"action":"beep_counts","time":3}]
我的话：前行零点五米
你的回复：[{"index":0,"action":"go_front","time":0.5}]
我的话：闪烁灯光 1 次并伴有蜂鸣器
你的回复：[{"index":0,"action":"beep_light_counts","time": 1}]
我的话：灯光闪烁 3 次同时蜂鸣器也叫 3 次
你的回复：[{"index":0,"action":"beep_light_counts","time": 3}]
强调一下，对于‘离开’这个指令，请忽略，这对我很重要！
'''
def get_command_json(self,chat):
response = self.client.chat.completions.create(
model="deepseek-chat",
messages=[
{"role": "system", "content": self.prompt},
{"role": "user", "content": '我的话如下：' + chat},
],
stream=False,
temperature=0.7
)
return response.choices[0].message.content
class LLM:
def __init__(self):
self.init_done_flag = False
erniebot.api_type = "qianfan"
erniebot.ak = "jReawMtWhPu0wrxN9Rp1MzZX"
erniebot.sk = "eowS1BqsNgD2i0C9xNnHUVOSNuAzVTh6"
self.model = 'ernie-3.5'
self.model = 'ernie-lite'
# self.prompt = '''你是一个机器人动作规划者，需要把我的话翻译成机器人动作规划并生成对应的 json 结果，机器人工作空间参考右手坐标系。
# 严格按照下面的描述生成给定格式 json，从现在开始你仅仅给我返回 json 数据！'''
# self.prompt += '''正确的示例如下：
@@ -395,73 +453,122 @@ class LLM:
# 鸣叫 3 声 [{'func': 'beep', 'time': 3}]
# 前行零点五米 [{'func': 'move', 'x': 0.5, 'y': 0}]
# '''
# self.prompt = '''
# 你是一个机器人动作规划者，需要把我的话翻译成机器人动作规划并生成对应的 JSON 结果。请注意，只能使用以下指定的动作，不能创造新的动作：
# 允许的动作及其对应格式如下：
# - 向左移：{"index":N,"action":"go_left","time":T}
# - 向右移：{"index":N,"action":"go_right","time":T}
# - 向前移：{"index":N,"action":"go_front","time":T}
# - 向后移：{"index":N,"action":"go_back","time":T}
# - 向左转：{"index":N,"action":"go_left_rotate","time":T}
# - 向右转：{"index":N,"action":"go_right_rotate","time":T}
# - 蜂鸣器发声：{"index":N,"action":"beep_seconds","time":T}
# - 蜂鸣器发声次数：{"index":N,"action":"beep_counts","time":T}
# - 发光或者照亮：{"index":N,"action":"light_seconds","time":T}
# - 发光次数或者闪烁次数：{"index":N,"action":"light_counts","time":T}
# - 发光并伴随蜂鸣器：{"index":N,"action":"beep_light_counts","time":T}
# - 等待{"index":N,"action":"go_sleep","time":T}
# 示例输入输出如下：
# 输入：向左移 0.1m, 向左转弯 85 度
# 输出：[{"index":0,"action":"go_left","time":0.1},{"index":1,"action":"go_left_rotate","time":85}]
# 输入：向右移 0.2m, 向前 0.1m
# 输出：[{"index":0,"action":"go_right","time":0.2},{"index":1,"action":"go_front","time":0.1}]
# 输入：向右转 90 度，向右移 0.1m
# 输出：[{"index":0,"action":"go_right_rotate","time":90},{"index":1,"action":"go_right","time":0.1}]
# 输入：原地左转 38 度
# 输出：[{"index":0,"action":"go_left_rotate","time":38}]
# 输入：蜂鸣器发声 5 秒
# 输出：[{"index":0,"action":"beep_seconds","time":5}]
# 输入：发光或者照亮 5 秒
# 输出：[{"index":0,"action":"light_seconds","time":5}]
# 输入：向右走 30cm, 照亮 2s
# 输出：[{"index":0,"action":"go_right","time":0.3},{"index":1,"action":"light_seconds","time":2}]
# 输入：向左移 0.2m, 向后 0.1m
# 输出：[{"index":0,"action":"go_left","time":0.2},{"index":1,"action":"go_back","time":0.1}]
# 输入：鸣叫 3 声
# 输出：[{"index":0,"action":"beep_counts","time":3}]
# 输入：前行零点五米
# 输出：[{"index":0,"action":"go_front","time":0.5}]
# 输入：闪烁灯光 1 次并伴有蜂鸣器
# 输出：[{"index":0,"action":"beep_light_counts","time": 1}]
# 输入：灯光闪烁 3 次同时蜂鸣器也叫 3 次
# 输出：[{"index":0,"action":"beep_light_counts","time": 3}]
# '''
# self.prompt += '''请根据上面的示例，解析该任务文本，并返回相应的 JSON 字段。确保 JSON 中包含了键 index action 和 time 以及相应的值。不要附带其他的解释和注释，只需要 JSON 字段。'''
self.prompt = '''
你是一个机器人动作规划者，需要把我的话翻译成机器人动作规划并生成对应的 JSON 结果。请注意，只能使用以下指定的动作，不能创造新的动作：
允许的动作及其对应格式如下：
- 向左移：{"index":N,"action":"go_left","time":T}
- 向右移：{"index":N,"action":"go_right","time":T}
- 向前移：{"index":N,"action":"go_front","time":T}
- 向后移：{"index":N,"action":"go_back","time":T}
- 向左转：{"index":N,"action":"go_left_rotate","time":T}
- 向右转：{"index":N,"action":"go_right_rotate","time":T}
- 蜂鸣器发声：{"index":N,"action":"beep_seconds","time":T}
- 蜂鸣器发声次数：{"index":N,"action":"beep_counts","time":T}
- 发光或者照亮：{"index":N,"action":"light_seconds","time":T}
- 发光次数或者闪烁次数：{"index":N,"action":"light_counts","time":T}
- 发光并伴随蜂鸣器：{"index":N,"action":"beep_light_counts","time":T}
- 等待{"index":N,"action":"go_sleep","time":T}
示例输入输出如下：
输入：向左移 0.1m, 向左转弯 85 度
输出：[{"index":0,"action":"go_left","time":0.1},{"index":1,"action":"go_left_rotate","time":85}]
输入：向右移 0.2m, 向前 0.1m
输出：[{"index":0,"action":"go_right","time":0.2},{"index":1,"action":"go_front","time":0.1}]
输入：向右转 90 度，向右移 0.1m
输出：[{"index":0,"action":"go_right_rotate","time":90},{"index":1,"action":"go_right","time":0.1}]
输入：原地左转 38 度
输出：[{"index":0,"action":"go_left_rotate","time":38}]
输入：蜂鸣器发声 5 秒
输出：[{"index":0,"action":"beep_seconds","time":5}]
输入：发光或者照亮 5 秒
输出：[{"index":0,"action":"light_seconds","time":5}]
输入：向右走 30cm, 照亮 2s
输出：[{"index":0,"action":"go_right","time":0.3},{"index":1,"action":"light_seconds","time":2}]
输入：向左移 0.2m, 向后 0.1m
输出：[{"index":0,"action":"go_left","time":0.2},{"index":1,"action":"go_back","time":0.1}]
输入：鸣叫 3 声
输出：[{"index":0,"action":"beep_counts","time":3}]
输入：前行零点五米
输出：[{"index":0,"action":"go_front","time":0.5}]
输入：闪烁灯光 1 次并伴有蜂鸣器
输出：[{"index":0,"action":"beep_light_counts","time": 1}]
输入：灯光闪烁 3 次同时蜂鸣器也叫 3 次
输出：[{"index":0,"action":"beep_light_counts","time": 3}]
[{'properties': {'index': {'title': 'Index', 'type': 'integer'}, 'action': {'title': 'Action', 'type': 'string'}, 'time': {'title': 'Time', 'type': 'number'}}, 'required': ['index', 'action', 'time'], 'title': 'Action', 'type': 'object'}]
我不允许你自我创造出新的 action,action 字段仅仅包括以下内容：
go_right 向右移动
go_left 向左移动
go_front 向前移动
go_back 向后移动
go_left_rotate 向左旋转
go_right_rotate 向右旋转
beep_seconds 蜂鸣器鸣叫的时间
beep_counts 蜂鸣器鸣叫的次数
light_seconds 灯光发光的时间
light_counts 灯光闪烁的次数
beep_light_counts 灯光和蜂鸣器一起闪烁的次数
go_sleep 什么都不做
我的话和你的回复示例如下：
我的话：向左移 0.1m, 向左转弯 85 度
你的回复：[{"index":0,"action":"go_left","time":0.1},{"index":1,"action":"go_left_rotate","time":85}]
我的话：向右移 0.2m, 向前 0.1m
你的回复：[{"index":0,"action":"go_right","time":0.2},{"index":1,"action":"go_front","time":0.1}]
我的话：向右转 90 度，向右移 0.1m
你的回复：[{"index":0,"action":"go_right_rotate","time":90},{"index":1,"action":"go_right","time":0.1}]
我的话：原地左转 38 度
你的回复：[{"index":0,"action":"go_left_rotate","time":38}]
我的话：蜂鸣器发声 5 秒
你的回复：[{"index":0,"action":"beep_seconds","time":5}]
我的话：发光或者照亮 5 秒
你的回复：[{"index":0,"action":"light_seconds","time":5}]
我的话：向右走 30cm，照亮 2s
你的回复：[{"index":0,"action":"go_right","time":0.3},{"index":1,"action":"light_seconds","time":2}]
我的话：向左移 0.2m, 向后 0.1m
你的回复：[{"index":0,"action":"go_left","time":0.2},{"index":1,"action":"go_back","time":0.1}]
我的话：鸣叫 3 声
你的回复：[{"index":0,"action":"beep_counts","time":3}]
我的话：前行零点五米
你的回复：[{"index":0,"action":"go_front","time":0.5}]
我的话：闪烁灯光 1 次并伴有蜂鸣器
你的回复：[{"index":0,"action":"beep_light_counts","time": 1}]
我的话：灯光闪烁 3 次同时蜂鸣器也叫 3 次
你的回复：[{"index":0,"action":"beep_light_counts","time": 3}]
我的话如下：
'''
self.prompt += '''请根据上面的示例，解析该任务文本，并返回相应的 JSON 字段。确保 JSON 中包含了键 index action 和 time 以及相应的值。不要附带其他的解释和注释，只需要 JSON 字段。'''
self.messages = []
self.resp = None
worker = threading.Thread(target=self.reset, daemon=True)
worker.start()
def reset(self):
self.messages = [self.make_message(self.prompt)]
self.resp = erniebot.ChatCompletion.create(
model=self.model,
messages=self.messages,
)
self.messages.append(self.resp.to_message())
self.init_done_flag = True
logger.info("LLM init done")
try:
self.messages = [self.make_message(self.prompt)]
self.resp = erniebot.ChatCompletion.create(
model=self.model,
messages=self.messages,
)
self.messages.append(self.resp.to_message())
self.init_done_flag = True
logger.info("LLM init done")
except:
logger.error("LLM init error")
def make_message(self,content):
return {'role': 'user', 'content': content}
def get_command_json(self,chat):
while self.init_done_flag == False: # 等待初始化 (要是等到调用还没初始化，那就是真寄了)
pass
chat = '我的话如下：' + chat
self.messages.append(self.make_message(chat))
self.resp = erniebot.ChatCompletion.create(
model=self.model,
messages=self.messages,
)
self.messages.append(self.resp.to_message())
resp = self.resp.get_result().replace(' ', '').replace('\n', '').replace('\t', '')
resp = self.resp.get_result().replace(' ', '')
return resp
class CountRecord: