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from functools import lru_cache |
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from typing import Optional |
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from pydantic_settings import BaseSettings |
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EXTRACT_INFO_SYSTEM = "You are an expert in structured data extraction. You will be given an image or a set of images of a product and should extract its properties into the given structure." |
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EXTRACT_INFO_HUMAN = ( |
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"""Output properties of the main product (or {product_taxonomy}) shown in the images. You should use the following product data to assist you, if available: |
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{product_data} |
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If an attribute appears in both the image and the product data, use the value from the product data.""" |
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).replace(" ", "") |
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FOLLOW_SCHEMA_SYSTEM = "You are an expert in structured data extraction. You will be given an dictionary of attributes of a product and should output the its properties into the given structure." |
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FOLLOW_SCHEMA_HUMAN = """Convert following attributes to structured schema. Keep all the keys and number of values. Only replace the values themselves. : |
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{json_info}""" |
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GET_PERCENTAGE_SYSTEM = "You are a fashion assistant. You have to assign percentages of cerntainty to each attribute of a product based on the image and product data. You will be given an image or a set of images of a product and set of attributes and should output the percentages of certainty into the given structure." |
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GET_PERCENTAGE_HUMAN = """I have a schema for extracting attributes from images of products. It's pasted below. |
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For each of the allowed values, in each of the attributes, assign a percentage of certainty that the product fits with that value. |
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I have attached images of the product. |
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You should use the following product data to assist you, if available: |
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{product_data} |
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If an attribute appears in both the image and the product data, use the value from the product data. |
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""" |
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REEVALUATE_SYSTEM = "You are a fashion assistant. You have to reevaluate the attributes of a product based on the image and product data. You will be given an image or a set of images of a product and set of attributes and should output the reevaluated attributes into the given structure." |
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REEVALUATE_HUMAN = """Reevaluate the following attributes of the main product (or {product_taxonomy}) shown in the images. Here are the attributes to reevaluate: |
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{product_data} |
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If an attribute has type of string, do not need to reevaluate the values, just the attribute itself. If an attribute has type of list[string], reevaluate the top three values. |
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""" |
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class Prompts(BaseSettings): |
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EXTRACT_INFO_SYSTEM_MESSAGE: str = EXTRACT_INFO_SYSTEM |
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EXTRACT_INFO_HUMAN_MESSAGE: str = EXTRACT_INFO_HUMAN |
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FOLLOW_SCHEMA_SYSTEM_MESSAGE: str = FOLLOW_SCHEMA_SYSTEM |
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FOLLOW_SCHEMA_HUMAN_MESSAGE: str = FOLLOW_SCHEMA_HUMAN |
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GET_PERCENTAGE_SYSTEM_MESSAGE: str = GET_PERCENTAGE_SYSTEM |
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GET_PERCENTAGE_HUMAN_MESSAGE: str = GET_PERCENTAGE_HUMAN |
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REEVALUATE_SYSTEM_MESSAGE: str = REEVALUATE_SYSTEM |
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REEVALUATE_HUMAN_MESSAGE: str = REEVALUATE_HUMAN |
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@lru_cache |
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def get_prompts() -> Prompts: |
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""" |
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Create and cache a Prompts instance. |
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Returns the same instance for subsequent calls. |
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""" |
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prompts = Prompts() |
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return prompts |
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