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import os
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import base64
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import time
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import json
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from mimetypes import guess_type
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from openai import AzureOpenAI
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from azure.identity import DefaultAzureCredential, get_bearer_token_provider
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END_POINT = '<endpoint>'
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MODEL_NAME = 'gpt-4o_2024-08-06'
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API_VER = '2024-02-01'
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def local_image_to_data_url(image_path):
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mime_type, _ = guess_type(image_path)
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if mime_type is None:
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mime_type = 'application/octet-stream'
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with open(image_path, "rb") as image_file:
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base64_encoded_data = base64.b64encode(image_file.read()).decode('utf-8')
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return f"data:{mime_type};base64,{base64_encoded_data}"
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def ask_gpt_on_figure(data, _, __):
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endpoint = END_POINT
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token_provider = get_bearer_token_provider(
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DefaultAzureCredential(),
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"https://cognitiveservices.azure.com/.default"
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)
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deployment_name = MODEL_NAME
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client = AzureOpenAI(
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azure_ad_token_provider=token_provider,
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azure_endpoint=endpoint,
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api_version=API_VER
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)
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description = data['description']
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data_scale = 10
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measurement = 'if the figure corresponds to the given description'
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prompt = 'The following is a text description of a 3D CAD figure and an image of a CAD instance. ' +\
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f'Measure {measurement}, and give a score in the scale of {data_scale}. Do not comment on issues such as texture, smoothness and colors' +\
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f'\n description: {description}\n'
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image_path = data['figure_path']
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response = client.chat.completions.create(
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model=deployment_name,
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messages=[
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{'role': 'system', 'content': 'You are a helpful assistant'},
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{'role': 'user', 'content': [
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{'type': 'text', 'text': prompt},
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{'type': 'image_url', 'image_url': {'url': local_image_to_data_url(image_path)}},
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]}
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]
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)
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time.sleep(3)
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return(response.choices[0].message.content)
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def ask_gpt(data, _, __):
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endpoint = END_POINT
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token_provider = get_bearer_token_provider(
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DefaultAzureCredential(),
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"https://cognitiveservices.azure.com/.default"
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)
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deployment_name = MODEL_NAME
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client = AzureOpenAI(
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azure_ad_token_provider=token_provider,
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azure_endpoint=endpoint,
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api_version=API_VER
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)
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description = data['gpt_label']
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prompt = 'The following is an evaluation of an CAD object.' +\
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f'\n evaluation: {description}\n' +\
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'Extract the integer score of the evaluation. The score is between 0 to 10. Return the number only.'
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response = client.chat.completions.create(
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model=deployment_name,
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messages=[
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{'role': 'system', 'content': 'You are a helpful assistant'},
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{'role': 'user', 'content': [
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{'type': 'text', 'text': prompt},
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]}
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]
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)
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time.sleep(3)
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return(response.choices[0].message.content) |
