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import base64
import copy
import io
import os
import random
import time
import re
import json
import argparse
import yaml
import openai
from openai import AzureOpenAI
from prompts import *
import base64
from mimetypes import guess_type
# from img2table.document import Image
# from img2table.document import PDF
# from img2table.ocr import TesseractOCR
# from img2table.ocr import EasyOCR
from PIL import Image as PILImage
class LLMQueryAPI:
def __init__(self) -> None:
pass
def gpt4_chat_completion(self, query):
with open('config_gpt4.yaml', 'r') as f:
config = yaml.safe_load(f)
API_KEY = config.get('API_KEY')
API_VERSION = config.get('API_VERSION')
API_BASE = config.get('API_BASE')
client = AzureOpenAI(
azure_endpoint= API_BASE,
api_version= API_VERSION,
api_key = API_KEY
)
deployment_name='gpt-4-2024-04-09'
response = client.chat.completions.create(
model=deployment_name,
messages=query)
return response.choices[0].message.content
def gpt35_chat_completion(self, query):
with open('config_gpt35.yaml', 'r') as f:
config = yaml.safe_load(f)
API_KEY = config.get('API_KEY')
API_VERSION = config.get('API_VERSION')
API_BASE = config.get('API_BASE')
response = openai.ChatCompletion.create(
engine='gpt-35-turbo-0613',
messages=query,
request_timeout=60,
api_key = API_KEY,
api_version = API_VERSION,
api_type = "azure",
api_base = API_BASE,
)
return response['choices'][0]['message']
def copilot_chat_completion(self, query):
with open('config_gpt4.yaml', 'r') as f:
config = yaml.safe_load(f)
API_KEY = config.get('API_KEY')
API_VERSION = config.get('API_VERSION')
API_BASE = config.get('API_BASE')
response = openai.ChatCompletion.create(
engine='gpt-4-0613',
messages=query,
request_timeout=60,
api_key = API_KEY,
api_version = API_VERSION,
api_type = "azure",
api_base = API_BASE,
)
return response['choices'][0]['message']
def LLM_chat_query(self, query, llm):
if llm == 'gpt-3.5-turbo':
return self.gpt35_chat_completion(query)
elif llm == "gpt-4":
return self.gpt4_chat_completion(query)
# return self.copilot_chat_completion(query)
def get_llm_response(self, llm, query):
chat_completion = []
chat_completion.append({"role": "system", "content": query})
res = self.LLM_chat_query(chat_completion, llm)
return res
class LLMProxyQueryAPI:
def __init__(self) -> None:
pass
def gpt35_chat_completion(self, query):
client = openai.Client()
response = client.chat.completions.create(
model="gpt-3.5-turbo-16k",
messages=query,
)
return response.choices[0].message.content
def gpt4o_chat_completion(self, query):
client = openai.Client()
response = client.chat.completions.create(
model="gpt-4o",
messages=query,
)
return response.choices[0].message.content
def gpt4_chat_completion(self, query):
client = openai.Client()
response = client.chat.completions.create(
model="gpt-4-1106-preview",
messages=query,
)
return response.choices[0].message.content
def gpt4_vision(self, query, image_path):
print(query)
client = openai.Client()
response = client.chat.completions.create(
model="gpt-4-vision-preview",
messages=[
{
"role": "user",
"content": [
{
"type": "text",
"text": query
},
{
"type": "image_url",
"image_url": {
"url": image_path
}
}
]
}
],
max_tokens=4096,
stream=False
)
return response.choices[0].message.content
def LLM_chat_query(self, llm, query, image_path=None):
if llm == 'gpt-3.5-turbo':
return self.gpt35_chat_completion(query)
elif llm == "gpt-4":
return self.gpt4_chat_completion(query)
elif llm == "gpt-4o":
return self.gpt4o_chat_completion(query)
elif llm == "gpt-4V":
return self.gpt4_vision(query, image_path)
def get_llm_response(self, llm, query, image_path=None):
if llm == "gpt-4V" and image_path:
res = self.LLM_chat_query(llm, query, image_path)
return res
chat_completion = []
chat_completion.append({"role": "system", "content": query})
res = self.LLM_chat_query(llm, chat_completion)
return res
# if __name__ == '__main__':
# llm_query_api = LLMProxyQueryAPI()
# def local_image_to_data_url(image_path):
# mime_type, _ = guess_type(image_path)
# if mime_type is None:
# mime_type = 'application/octet-stream'
# with open(image_path, "rb") as image_file:
# base64_encoded_data = base64.b64encode(image_file.read()).decode('utf-8')
# return f"data:{mime_type};base64,{base64_encoded_data}"
# tesseract = TesseractOCR()
# pdf = PDF(src="temp3.pdf", pages=[0, 0])
# extracted_tables = pdf.extract_tables(ocr=tesseract,
# implicit_rows=True,
# borderless_tables=True,)
# html_table = extracted_tables[0][0].html_repr()
# print(html_table)
# table_image_path = "./temp3.jpeg"
# table_image_data_url = local_image_to_data_url(table_image_path)
# print(table_image_data_url)
# query = table_image_to_html_prompt.replace("{{html_table}}", html_table)
# html_table_refined = llm_query_api.get_llm_response("gpt-4V", query, table_image_data_url)
# print(html_table_refined)
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