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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "initial_id",
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2024-07-30T17:54:34.420100Z",
"start_time": "2024-07-30T17:54:31.587244Z"
}
},
"outputs": [],
"source": [
"import os\n",
"import re\n",
"import json\n",
"import base64\n",
"import tiktoken\n",
"import time\n",
"import pandas as pd\n",
"import openai\n",
"from tqdm.notebook import tqdm\n",
"from openai import OpenAI\n"
]
},
{
"cell_type": "code",
"outputs": [],
"source": [
"MODEL_LIMITS = {\n",
" \"gpt-3.5-turbo-0125\": 16_385,\n",
" \"gpt-4-turbo-2024-04-09\": 128_000,\n",
" \"gpt-4o-2024-05-13\": 128_000\n",
"}\n",
"\n",
"# The cost per token for each model input.\n",
"MODEL_COST_PER_INPUT = {\n",
" \"gpt-3.5-turbo-0125\": 0.0000005,\n",
" \"gpt-4-turbo-2024-04-09\": 0.00001,\n",
" \"gpt-4o-2024-05-13\": 0.000005\n",
"}\n",
"\n",
"# The cost per token for each model output.\n",
"MODEL_COST_PER_OUTPUT = {\n",
" \"gpt-3.5-turbo-0125\": 0.0000015,\n",
" \"gpt-4-turbo-2024-04-09\": 0.00003,\n",
" \"gpt-4o-2024-05-13\": 0.000015\n",
"}\n",
"\n"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-07-30T17:54:34.420796Z",
"start_time": "2024-07-30T17:54:34.416511Z"
}
},
"id": "707f7b0c807c3293",
"execution_count": 2
},
{
"cell_type": "code",
"outputs": [],
"source": [
"# If the question is a multi-choice question and you are unsure which one is correct, you must guess an option. Please don't ask me any questions and give me the answer in the response.\n",
"\n",
"def get_gpt_res(text, image, model):\n",
" if image and model[:7] != \"gpt-3.5\":\n",
" base64_image = encode_image(image)\n",
" image_code = {\n",
" \"type\": \"image_url\",\n",
" \"image_url\": {\n",
" \"url\": f\"data:image/jpeg;base64,{base64_image}\"}\n",
" }\n",
" \n",
" response = client.chat.completions.create(\n",
" model=model,\n",
" messages=[\n",
" {\n",
" \"role\": \"system\",\n",
" \"content\": [\n",
" {\n",
" \"type\": \"text\",\n",
" \"text\": \"You are a data analyst. I will give you a background introduction and data analysis question. You must answer the question. \"\n",
" }\n",
" ]\n",
" },\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": [\n",
" {\n",
" \"type\": \"text\",\n",
" \"text\": text\n",
" },\n",
" image_code\n",
" ]\n",
" }\n",
" ],\n",
" temperature=0,\n",
" max_tokens=2256,\n",
" top_p=1,\n",
" frequency_penalty=0,\n",
" presence_penalty=0\n",
" )\n",
" else:\n",
" response = client.chat.completions.create(\n",
" model=model,\n",
" messages=[\n",
" {\n",
" \"role\": \"system\",\n",
" \"content\": [\n",
" {\n",
" \"type\": \"text\",\n",
" \"text\": \"You are a data analyst. I will give you a background introduction and data analysis question. You must answer the question. \"\n",
" }\n",
" ]\n",
" },\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": [\n",
" {\n",
" \"type\": \"text\",\n",
" \"text\": text\n",
" }\n",
" ]\n",
" }\n",
" ],\n",
" temperature=0,\n",
" # max_tokens=2256,\n",
" top_p=1,\n",
" frequency_penalty=0,\n",
" presence_penalty=0\n",
" )\n",
" return response"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-07-30T17:54:34.429697Z",
"start_time": "2024-07-30T17:54:34.423485Z"
}
},
"id": "13c11c802e901f74",
"execution_count": 3
},
{
"cell_type": "code",
"outputs": [],
"source": [
"samples = []\n",
"with open(\"./data.json\", \"r\") as f:\n",
" for line in f:\n",
" samples.append(eval(line.strip()))\n"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-07-30T17:54:34.449313Z",
"start_time": "2024-07-30T17:54:34.429336Z"
}
},
"id": "959b638b518acaf8",
"execution_count": 4
},
{
"cell_type": "code",
"outputs": [],
"source": [
"def gpt_tokenize(string: str, encoding) -> int:\n",
" \"\"\"Returns the number of tokens in a text string.\"\"\"\n",
" num_tokens = len(encoding.encode(string))\n",
" return num_tokens\n",
"\n",
"def find_jpg_files(directory):\n",
" jpg_files = [file for file in os.listdir(directory) if file.lower().endswith('.jpg') or file.lower().endswith('.png')]\n",
" return jpg_files if jpg_files else None\n",
"\n",
"# Function to encode the image\n",
"def encode_image(image_path):\n",
" with open(image_path, \"rb\") as image_file:\n",
" return base64.b64encode(image_file.read()).decode('utf-8')\n",
"\n",
"\n",
"def find_excel_files(directory):\n",
" jpg_files = [file for file in os.listdir(directory) if (file.lower().endswith('xlsx') or file.lower().endswith('xlsb') or file.lower().endswith('xlsm')) and not \"answer\" in file.lower()]\n",
" return jpg_files if jpg_files else None\n",
"\n",
"def read_excel(file_path):\n",
" # 读取Excel文件中的所有sheet\n",
" xls = pd.ExcelFile(file_path)\n",
" sheets = {}\n",
" for sheet_name in xls.sheet_names:\n",
" sheets[sheet_name] = xls.parse(sheet_name)\n",
" return sheets\n",
"\n",
"def dataframe_to_text(df):\n",
" # 将DataFrame转换为文本\n",
" text = df.to_string(index=False)\n",
" return text\n",
"\n",
"def combine_sheets_text(sheets):\n",
" # 将所有sheet的文本内容组合起来\n",
" combined_text = \"\"\n",
" for sheet_name, df in sheets.items():\n",
" sheet_text = dataframe_to_text(df)\n",
" combined_text += f\"Sheet name: {sheet_name}\\n{sheet_text}\\n\\n\"\n",
" return combined_text\n",
"\n",
"def read_txt(path):\n",
" with open(path, \"r\") as f:\n",
" return f.read()\n",
"\n",
"def truncate_text(text, max_tokens=128000):\n",
" # 计算当前文本的token数\n",
" tokens = text.split()\n",
" if len(tokens) > max_tokens:\n",
" # 截断文本以确保不超过最大token数\n",
" text = ' '.join(tokens[-max_tokens:])\n",
" return text"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-07-30T17:54:34.454285Z",
"start_time": "2024-07-30T17:54:34.444120Z"
}
},
"id": "41837159d54fe6be",
"execution_count": 5
},
{
"cell_type": "code",
"outputs": [
{
"data": {
"text/plain": " 0%| | 0/35 [00:00<?, ?it/s]",
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "f7f9c1f189cf4b09ab7938cc1eccb88b"
}
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total cost: 36.999739999999996\n",
"Total cost: 38.250449999999994\n",
"Total cost: 39.498639999999995\n",
"Total cost: 40.747339999999994\n",
"Total cost: 41.992529999999995\n",
"Total cost: 43.23591999999999\n",
"Total cost: 44.484019999999994\n",
"Total cost: 45.72899999999999\n",
"Total cost: 46.97565999999999\n",
"Total cost: 48.22759999999999\n",
"Total cost: 49.47311999999999\n",
"Total cost: 50.719599999999986\n",
"Total cost: 51.96382999999999\n",
"Total cost: 53.212769999999985\n",
"Total cost: 54.45198999999999\n",
"Total cost: 54.486009999999986\n",
"Total cost: 54.51985999999999\n",
"Total cost: 54.55831999999999\n",
"Total cost: 54.593969999999985\n",
"Total cost: 54.621729999999985\n",
"Total cost: 54.65634999999998\n",
"Total cost: 54.690989999999985\n",
"Total cost: 54.728549999999984\n",
"Total cost: 54.76124999999998\n",
"Total cost: 54.792459999999984\n",
"Total cost: 54.82964999999999\n",
"Total cost: 54.867389999999986\n",
"Total cost: 54.90312999999998\n",
"Total cost: 54.93681999999998\n",
"Total cost: 54.969619999999985\n",
"Total cost: 55.007139999999985\n",
"Total cost: 55.04078999999999\n",
"Total cost: 55.076819999999984\n",
"Total cost: 55.114809999999984\n",
"Total cost: 55.153359999999985\n",
"Total cost: 55.220739999999985\n",
"Total cost: 55.29938999999999\n",
"Total cost: 55.37173999999999\n",
"Total cost: 55.445109999999985\n",
"Total cost: 55.513029999999986\n",
"Total cost: 55.593209999999985\n",
"Total cost: 55.666409999999985\n",
"Total cost: 55.73881999999998\n",
"Total cost: 55.98776999999998\n",
"Total cost: 56.24514999999998\n",
"Total cost: 56.50251999999998\n",
"Total cost: 56.75385999999998\n",
"Total cost: 56.992249999999984\n",
"Total cost: 57.239629999999984\n",
"Total cost: 57.49024999999998\n",
"Total cost: 57.738469999999985\n",
"Total cost: 57.987479999999984\n",
"Total cost: 58.24189999999999\n",
"Total cost: 58.88888999999999\n",
"Total cost: 59.53223999999999\n",
"Total cost: 60.16538999999999\n",
"Total cost: 60.79905999999999\n",
"Total cost: 61.43428999999999\n",
"Total cost: 62.06920999999999\n",
"Total cost: 62.70693999999999\n",
"Total cost: 63.35130999999999\n",
"Total cost: 63.991719999999994\n",
"Total cost: 64.63403\n",
"Total cost: 65.27351\n",
"Total cost: 65.2993\n",
"Total cost: 65.32312\n",
"Total cost: 65.34591\n",
"Total cost: 65.37329\n",
"Total cost: 65.39828\n"
]
},
{
"ename": "RateLimitError",
"evalue": "Error code: 429 - {'error': {'message': 'You exceeded your current quota, please check your plan and billing details. For more information on this error, read the docs: https://platform.openai.com/docs/guides/error-codes/api-errors.', 'type': 'insufficient_quota', 'param': None, 'code': 'insufficient_quota'}}",
"output_type": "error",
"traceback": [
"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[0;31mRateLimitError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[0;32mIn[8], line 57\u001B[0m\n\u001B[1;32m 53\u001B[0m \u001B[38;5;66;03m# print(len(encoding.encode(prompt)))\u001B[39;00m\n\u001B[1;32m 54\u001B[0m \u001B[38;5;66;03m# print(prompt)\u001B[39;00m\n\u001B[1;32m 55\u001B[0m \u001B[38;5;66;03m# text = truncate_text(text, 20000)\u001B[39;00m\n\u001B[1;32m 56\u001B[0m start \u001B[38;5;241m=\u001B[39m time\u001B[38;5;241m.\u001B[39mtime()\n\u001B[0;32m---> 57\u001B[0m response \u001B[38;5;241m=\u001B[39m \u001B[43mget_gpt_res\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcut_text\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mimage\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 58\u001B[0m cost \u001B[38;5;241m=\u001B[39m response\u001B[38;5;241m.\u001B[39musage\u001B[38;5;241m.\u001B[39mcompletion_tokens \u001B[38;5;241m*\u001B[39m MODEL_COST_PER_OUTPUT[model] \u001B[38;5;241m+\u001B[39m response\u001B[38;5;241m.\u001B[39musage\u001B[38;5;241m.\u001B[39mprompt_tokens \u001B[38;5;241m*\u001B[39m MODEL_COST_PER_INPUT[model]\n\u001B[1;32m 60\u001B[0m answers\u001B[38;5;241m.\u001B[39mappend({\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mid\u001B[39m\u001B[38;5;124m\"\u001B[39m: sample[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mid\u001B[39m\u001B[38;5;124m\"\u001B[39m], \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmodel\u001B[39m\u001B[38;5;124m\"\u001B[39m: response\u001B[38;5;241m.\u001B[39mmodel, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124minput\u001B[39m\u001B[38;5;124m\"\u001B[39m: response\u001B[38;5;241m.\u001B[39musage\u001B[38;5;241m.\u001B[39mprompt_tokens,\n\u001B[1;32m 61\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124moutput\u001B[39m\u001B[38;5;124m\"\u001B[39m: response\u001B[38;5;241m.\u001B[39musage\u001B[38;5;241m.\u001B[39mcompletion_tokens, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcost\u001B[39m\u001B[38;5;124m\"\u001B[39m: cost, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtime\u001B[39m\u001B[38;5;124m\"\u001B[39m: time\u001B[38;5;241m.\u001B[39mtime()\u001B[38;5;241m-\u001B[39mstart, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mresponse\u001B[39m\u001B[38;5;124m\"\u001B[39m: response\u001B[38;5;241m.\u001B[39mchoices[\u001B[38;5;241m0\u001B[39m]\u001B[38;5;241m.\u001B[39mmessage\u001B[38;5;241m.\u001B[39mcontent})\n",
"Cell \u001B[0;32mIn[3], line 42\u001B[0m, in \u001B[0;36mget_gpt_res\u001B[0;34m(text, image, model)\u001B[0m\n\u001B[1;32m 12\u001B[0m response \u001B[38;5;241m=\u001B[39m client\u001B[38;5;241m.\u001B[39mchat\u001B[38;5;241m.\u001B[39mcompletions\u001B[38;5;241m.\u001B[39mcreate(\n\u001B[1;32m 13\u001B[0m model\u001B[38;5;241m=\u001B[39mmodel,\n\u001B[1;32m 14\u001B[0m messages\u001B[38;5;241m=\u001B[39m[\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 39\u001B[0m presence_penalty\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0\u001B[39m\n\u001B[1;32m 40\u001B[0m )\n\u001B[1;32m 41\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m---> 42\u001B[0m response \u001B[38;5;241m=\u001B[39m \u001B[43mclient\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mchat\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcompletions\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcreate\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 43\u001B[0m \u001B[43m \u001B[49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 44\u001B[0m \u001B[43m \u001B[49m\u001B[43mmessages\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m[\u001B[49m\n\u001B[1;32m 45\u001B[0m \u001B[43m \u001B[49m\u001B[43m{\u001B[49m\n\u001B[1;32m 46\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mrole\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43msystem\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 47\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mcontent\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43m[\u001B[49m\n\u001B[1;32m 48\u001B[0m \u001B[43m \u001B[49m\u001B[43m{\u001B[49m\n\u001B[1;32m 49\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mtype\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mtext\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 50\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mtext\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mYou are a data analyst. I will give you a background introduction and data analysis question. 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"File \u001B[0;32m/opt/anaconda3/envs/dsbench/lib/python3.8/site-packages/openai/_base_client.py:921\u001B[0m, in \u001B[0;36mSyncAPIClient.request\u001B[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001B[0m\n\u001B[1;32m 912\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mrequest\u001B[39m(\n\u001B[1;32m 913\u001B[0m \u001B[38;5;28mself\u001B[39m,\n\u001B[1;32m 914\u001B[0m cast_to: Type[ResponseT],\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 919\u001B[0m stream_cls: \u001B[38;5;28mtype\u001B[39m[_StreamT] \u001B[38;5;241m|\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[1;32m 920\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m ResponseT \u001B[38;5;241m|\u001B[39m _StreamT:\n\u001B[0;32m--> 921\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_request\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 922\u001B[0m \u001B[43m \u001B[49m\u001B[43mcast_to\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcast_to\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 923\u001B[0m \u001B[43m \u001B[49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43moptions\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 924\u001B[0m \u001B[43m \u001B[49m\u001B[43mstream\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mstream\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 925\u001B[0m \u001B[43m \u001B[49m\u001B[43mstream_cls\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mstream_cls\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 926\u001B[0m \u001B[43m \u001B[49m\u001B[43mremaining_retries\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mremaining_retries\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 927\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m/opt/anaconda3/envs/dsbench/lib/python3.8/site-packages/openai/_base_client.py:1005\u001B[0m, in \u001B[0;36mSyncAPIClient._request\u001B[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001B[0m\n\u001B[1;32m 1003\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m retries \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m0\u001B[39m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_should_retry(err\u001B[38;5;241m.\u001B[39mresponse):\n\u001B[1;32m 1004\u001B[0m err\u001B[38;5;241m.\u001B[39mresponse\u001B[38;5;241m.\u001B[39mclose()\n\u001B[0;32m-> 1005\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_retry_request\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 1006\u001B[0m \u001B[43m \u001B[49m\u001B[43moptions\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1007\u001B[0m \u001B[43m \u001B[49m\u001B[43mcast_to\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1008\u001B[0m \u001B[43m \u001B[49m\u001B[43mretries\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1009\u001B[0m \u001B[43m \u001B[49m\u001B[43merr\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mresponse\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1010\u001B[0m \u001B[43m \u001B[49m\u001B[43mstream\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mstream\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1011\u001B[0m \u001B[43m \u001B[49m\u001B[43mstream_cls\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mstream_cls\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1012\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1014\u001B[0m \u001B[38;5;66;03m# If the response is streamed then we need to explicitly read the response\u001B[39;00m\n\u001B[1;32m 1015\u001B[0m \u001B[38;5;66;03m# to completion before attempting to access the response text.\u001B[39;00m\n\u001B[1;32m 1016\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m err\u001B[38;5;241m.\u001B[39mresponse\u001B[38;5;241m.\u001B[39mis_closed:\n",
"File \u001B[0;32m/opt/anaconda3/envs/dsbench/lib/python3.8/site-packages/openai/_base_client.py:1053\u001B[0m, in \u001B[0;36mSyncAPIClient._retry_request\u001B[0;34m(self, options, cast_to, remaining_retries, response_headers, stream, stream_cls)\u001B[0m\n\u001B[1;32m 1049\u001B[0m \u001B[38;5;66;03m# In a synchronous context we are blocking the entire thread. Up to the library user to run the client in a\u001B[39;00m\n\u001B[1;32m 1050\u001B[0m \u001B[38;5;66;03m# different thread if necessary.\u001B[39;00m\n\u001B[1;32m 1051\u001B[0m time\u001B[38;5;241m.\u001B[39msleep(timeout)\n\u001B[0;32m-> 1053\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_request\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 1054\u001B[0m \u001B[43m \u001B[49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43moptions\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1055\u001B[0m \u001B[43m \u001B[49m\u001B[43mcast_to\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcast_to\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1056\u001B[0m \u001B[43m \u001B[49m\u001B[43mremaining_retries\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mremaining\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1057\u001B[0m \u001B[43m \u001B[49m\u001B[43mstream\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mstream\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1058\u001B[0m \u001B[43m \u001B[49m\u001B[43mstream_cls\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mstream_cls\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1059\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m/opt/anaconda3/envs/dsbench/lib/python3.8/site-packages/openai/_base_client.py:1005\u001B[0m, in \u001B[0;36mSyncAPIClient._request\u001B[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001B[0m\n\u001B[1;32m 1003\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m retries \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m0\u001B[39m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_should_retry(err\u001B[38;5;241m.\u001B[39mresponse):\n\u001B[1;32m 1004\u001B[0m err\u001B[38;5;241m.\u001B[39mresponse\u001B[38;5;241m.\u001B[39mclose()\n\u001B[0;32m-> 1005\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_retry_request\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 1006\u001B[0m \u001B[43m \u001B[49m\u001B[43moptions\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1007\u001B[0m \u001B[43m \u001B[49m\u001B[43mcast_to\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1008\u001B[0m \u001B[43m \u001B[49m\u001B[43mretries\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1009\u001B[0m \u001B[43m \u001B[49m\u001B[43merr\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mresponse\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1010\u001B[0m \u001B[43m \u001B[49m\u001B[43mstream\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mstream\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1011\u001B[0m \u001B[43m \u001B[49m\u001B[43mstream_cls\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mstream_cls\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1012\u001B[0m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1014\u001B[0m \u001B[38;5;66;03m# If the response is streamed then we need to explicitly read the response\u001B[39;00m\n\u001B[1;32m 1015\u001B[0m \u001B[38;5;66;03m# to completion before attempting to access the response text.\u001B[39;00m\n\u001B[1;32m 1016\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m err\u001B[38;5;241m.\u001B[39mresponse\u001B[38;5;241m.\u001B[39mis_closed:\n",
"File \u001B[0;32m/opt/anaconda3/envs/dsbench/lib/python3.8/site-packages/openai/_base_client.py:1053\u001B[0m, in \u001B[0;36mSyncAPIClient._retry_request\u001B[0;34m(self, options, cast_to, remaining_retries, response_headers, stream, stream_cls)\u001B[0m\n\u001B[1;32m 1049\u001B[0m \u001B[38;5;66;03m# In a synchronous context we are blocking the entire thread. Up to the library user to run the client in a\u001B[39;00m\n\u001B[1;32m 1050\u001B[0m \u001B[38;5;66;03m# different thread if necessary.\u001B[39;00m\n\u001B[1;32m 1051\u001B[0m time\u001B[38;5;241m.\u001B[39msleep(timeout)\n\u001B[0;32m-> 1053\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_request\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 1054\u001B[0m \u001B[43m \u001B[49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43moptions\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1055\u001B[0m \u001B[43m \u001B[49m\u001B[43mcast_to\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcast_to\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1056\u001B[0m \u001B[43m \u001B[49m\u001B[43mremaining_retries\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mremaining\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1057\u001B[0m \u001B[43m \u001B[49m\u001B[43mstream\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mstream\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1058\u001B[0m \u001B[43m \u001B[49m\u001B[43mstream_cls\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mstream_cls\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1059\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m/opt/anaconda3/envs/dsbench/lib/python3.8/site-packages/openai/_base_client.py:1020\u001B[0m, in \u001B[0;36mSyncAPIClient._request\u001B[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001B[0m\n\u001B[1;32m 1017\u001B[0m err\u001B[38;5;241m.\u001B[39mresponse\u001B[38;5;241m.\u001B[39mread()\n\u001B[1;32m 1019\u001B[0m log\u001B[38;5;241m.\u001B[39mdebug(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mRe-raising status error\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m-> 1020\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_make_status_error_from_response(err\u001B[38;5;241m.\u001B[39mresponse) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m 1022\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_process_response(\n\u001B[1;32m 1023\u001B[0m cast_to\u001B[38;5;241m=\u001B[39mcast_to,\n\u001B[1;32m 1024\u001B[0m options\u001B[38;5;241m=\u001B[39moptions,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 1027\u001B[0m stream_cls\u001B[38;5;241m=\u001B[39mstream_cls,\n\u001B[1;32m 1028\u001B[0m )\n",
"\u001B[0;31mRateLimitError\u001B[0m: Error code: 429 - {'error': {'message': 'You exceeded your current quota, please check your plan and billing details. For more information on this error, read the docs: https://platform.openai.com/docs/guides/error-codes/api-errors.', 'type': 'insufficient_quota', 'param': None, 'code': 'insufficient_quota'}}"
]
}
],
"source": [
"client = OpenAI(api_key=\"api\")\n",
"\n",
"tokens4generation = 6000\n",
"# model = \"gpt-3.5-turbo-0125\"\n",
"model = \"gpt-4-turbo-2024-04-09\"\n",
"data_path = \"./data/\"\n",
"total_cost = 0\n",
"encoding = tiktoken.encoding_for_model(model)\n",
"for id in tqdm(range(len(samples))):\n",
" # print(sample)\n",
" sample =samples[id]\n",
" if len(sample[\"questions\"]) > 0:\n",
" start = sample[\"questions\"][0]\n",
" end = sample[\"questions\"][-1]\n",
" # print(start)\n",
" # print(end)\n",
" image = find_jpg_files(os.path.join(data_path, sample[\"id\"]))\n",
" if image:\n",
" image = os.path.join(data_path, sample[\"id\"], image[0])\n",
" \n",
" excel_content = \"\"\n",
" excels = find_excel_files(os.path.join(data_path, sample[\"id\"]))\n",
" if excels:\n",
" \n",
" for excel in excels:\n",
" excel_file_path = os.path.join(data_path, sample[\"id\"], excel)\n",
" # print(excel_file_path)\n",
" sheets = read_excel(excel_file_path)\n",
" combined_text = combine_sheets_text(sheets)\n",
" excel_content += f\"The excel file {excel} is: \" + combined_text\n",
"\n",
" introduction = read_txt(os.path.join(data_path, sample[\"id\"], \"introduction.txt\"))\n",
" questions = []\n",
" for question_name in sample[\"questions\"]:\n",
" questions.append(read_txt(os.path.join(data_path, sample[\"id\"], question_name+\".txt\")))\n",
" \n",
" # print(workbooks)\n",
" \n",
" text = \"\"\n",
" if excel_content:\n",
" text += f\"The workbook is detailed as follows. {excel_content} \\n\"\n",
" text += f\"The introduction is detailed as follows. \\n {introduction} \\n\"\n",
" answers = []\n",
" for question in questions:\n",
" prompt = text + f\"The questions are detailed as follows. \\n {question}\"\n",
" \n",
" # print(len(encoding.encode(prompt)))\n",
" cut_text = encoding.decode(encoding.encode(prompt)[tokens4generation-MODEL_LIMITS[model]:])\n",
" # print(len(encoding.encode(prompt)))\n",
" # print(prompt)\n",
" # text = truncate_text(text, 20000)\n",
" start = time.time()\n",
" response = get_gpt_res(cut_text, image, model)\n",
" cost = response.usage.completion_tokens * MODEL_COST_PER_OUTPUT[model] + response.usage.prompt_tokens * MODEL_COST_PER_INPUT[model]\n",
" \n",
" answers.append({\"id\": sample[\"id\"], \"model\": response.model, \"input\": response.usage.prompt_tokens,\n",
" \"output\": response.usage.completion_tokens, \"cost\": cost, \"time\": time.time()-start, \"response\": response.choices[0].message.content})\n",
" total_cost += cost\n",
" print(\"Total cost: \", total_cost)\n",
" # break\n",
" save_path = os.path.join(\"./save_process\", model)\n",
" if not os.path.exists(save_path):\n",
" os.makedirs(save_path)\n",
" with open(os.path.join(save_path, sample['id']+\".json\"), \"w\") as f:\n",
" for answer in answers:\n",
" json.dump(answer, f)\n",
" f.write(\"\\n\")\n"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-07-30T19:57:21.359830Z",
"start_time": "2024-07-30T19:25:26.195520Z"
}
},
"id": "70acf35fbe1b0929",
"execution_count": 8
}
],
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"language": "python",
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"file_extension": ".py",
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|