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import openai | |
class ImageToText: | |
def __init__(self, api_key, gpt_version="gpt-3.5-turbo"): | |
self.template = self.initialize_template() | |
openai.api_key = api_key | |
self.gpt_version = gpt_version | |
def initialize_template(self): | |
prompt_prefix_1 = """Generate only an informative and nature paragraph based on the given information(a,b,c,d):\n""" | |
prompt_prefix_2 = """\n a. Image Resolution: """ | |
prompt_prefix_3 = """\n b. Image Caption: """ | |
prompt_prefix_4 = """\n c. Dense Caption: """ | |
prompt_prefix_5 = """\n d. Region Semantic: """ | |
prompt_suffix = """\n There are some rules: | |
Show object, color and position. | |
Use nouns rather than coordinates to show position information of each object. | |
No more than 7 sentences. | |
Only use one paragraph. | |
Describe position detailedly. | |
Do not appear number. | |
""" | |
template = f"{prompt_prefix_1}{prompt_prefix_2}{{width}}X{{height}}{prompt_prefix_3}{{caption}}{prompt_prefix_4}{{dense_caption}}{prompt_prefix_5}{{region_semantic}}{prompt_suffix}" | |
return template | |
def paragraph_summary_with_gpt(self, caption, dense_caption, region_semantic, width, height): | |
question = self.template.format(width=width, height=height, caption=caption, dense_caption=dense_caption, region_semantic=region_semantic) | |
print('\033[1;35m' + '*' * 100 + '\033[0m') | |
print('\nStep4, Paragraph Summary with GPT-3:') | |
print('\033[1;34m' + "Question:".ljust(10) + '\033[1;36m' + question + '\033[0m') | |
completion = openai.ChatCompletion.create( | |
model=self.gpt_version, | |
messages = [ | |
{"role": "user", "content" : question}] | |
) | |
print('\033[1;34m' + "ChatGPT Response:".ljust(18) + '\033[1;32m' + completion['choices'][0]['message']['content'] + '\033[0m') | |
print('\033[1;35m' + '*' * 100 + '\033[0m') | |
return completion['choices'][0]['message']['content'] | |
def paragraph_summary_with_gpt_debug(self, caption, dense_caption, width, height): | |
question = self.template.format(width=width, height=height, caption=caption, dense_caption=dense_caption) | |
print("paragraph_summary_with_gpt_debug:") | |
return question | |