import os import openai openai_api = os.getenv("OPENAI_API","") if openai_api: openai.api_base = openai_api class ImageToText: def __init__(self, gpt_version="gpt-3.5-turbo"): self.template = self.initialize_template() 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 of each object. 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') openai.api_key = os.getenv("OPENAI_API_KEY", "") 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