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LASTA_DEV01\lasta
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참고/instructor-main/examples/vision/slides.py
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250
참고/instructor-main/examples/vision/slides.py
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import json
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import logging
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import sys
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from typing import Optional
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from dotenv import find_dotenv, load_dotenv
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from openai import OpenAI
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from pydantic import BaseModel, Field
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from rich import print as rprint
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import instructor
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load_dotenv(find_dotenv())
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IMAGE_FILE = "image-file.txt" # file with all the images to be processed
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# Add logger
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logging.basicConfig()
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logger = logging.getLogger("app")
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logger.setLevel("INFO")
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class Competitor(BaseModel):
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name: str
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features: Optional[list[str]]
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# Define models
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class Industry(BaseModel):
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"""
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Represents competitors from a specific industry extracted from an image using AI.
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"""
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name: str = Field(description="The name of the industry")
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competitor_list: list[Competitor] = Field(
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description="A list of competitors for this industry"
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)
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class Competition(BaseModel):
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"""
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Represents competitors extracted from an image using AI.
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This class serves as a structured representation of
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competitors and their qualities.
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"""
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industry_list: list[Industry] = Field(
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description="A list of industries and their competitors"
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)
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# Define clients
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client_image = instructor.from_openai(OpenAI(), mode=instructor.Mode.MD_JSON)
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# Define functions
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def read_images(image_urls: list[str]) -> Competition:
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"""
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Given a list of image URLs, identify the competitors in the images.
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"""
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logger.info(f"Identifying competitors in images... {len(image_urls)} images")
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return client_image.chat.completions.create(
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model="gpt-4-vision-preview",
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response_model=Competition,
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max_tokens=2048,
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temperature=0,
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messages=[
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "Identify competitors and generate key features for each competitor.",
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},
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*[
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{"type": "image_url", "image_url": {"url": url}}
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for url in image_urls
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],
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],
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}
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],
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)
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def process_and_identify_competitors():
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"""
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Main function to process the image list file and identify competitors.
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"""
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logger.info("Starting app...")
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try:
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with open(IMAGE_FILE) as file:
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logger.info(f"Reading images from file: {IMAGE_FILE}")
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image_list = file.read().splitlines()
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logger.info(f"{len(image_list)} images read from file: {IMAGE_FILE}")
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except Exception as e:
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logger.error(f"Error reading images from file: {IMAGE_FILE}")
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logger.error(e)
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sys.exit(1)
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competitors = read_images(image_list)
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rprint(f"[green]{len(competitors.industry_list)} industries identified:[/green]")
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for industry in competitors.industry_list:
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rprint(f"[green]{industry.name}[/green]")
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rprint(f"[blue]Features: {industry.competitor_list}[/blue]")
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logger.info("Writing results to file...")
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with open("results.json", "w") as f:
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json.dump(
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{
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"competitors": competitors.model_dump(),
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},
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f,
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indent=4,
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)
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if __name__ == "__main__":
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process_and_identify_competitors()
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"""
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Example output:
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{
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"competitors": {
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"industry_list": [
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{
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"name": "Accommodation and Hospitality",
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"competitor_list": [
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{
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"name": "craigslist",
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"features": [
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"Transactions Offline",
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"Inexpensive"
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]
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},
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{
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"name": "couchsurfing",
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"features": [
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"Transactions Offline",
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"Inexpensive"
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]
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},
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{
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"name": "BedandBreakfast.com",
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"features": [
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"Transactions Offline",
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"Inexpensive"
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]
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},
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{
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"name": "airbnb",
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"features": [
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"Transactions Online",
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"Inexpensive"
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]
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},
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{
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"name": "HOSTELS.com",
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"features": [
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"Transactions Online",
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"Inexpensive"
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]
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},
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{
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"name": "VRBO",
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"features": [
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"Transactions Offline",
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"Costly"
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]
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},
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{
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"name": "Rentahome",
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"features": [
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"Transactions Online",
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"Costly"
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]
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},
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{
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"name": "Orbitz",
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"features": [
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"Transactions Online",
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"Costly"
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]
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},
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{
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"name": "Hotels.com",
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"features": [
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"Transactions Online",
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"Costly"
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]
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}
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]
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},
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{
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"name": "E-commerce Wine Retailers",
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"competitor_list": [
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{
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"name": "winesimple",
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"features": [
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"Ecommerce Retailers",
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"True Personalized Selections",
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"Brand Name Wine",
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"No Inventory Cost",
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"Target Mass Market"
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]
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},
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{
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"name": "nakedwines.com",
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"features": [
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"Ecommerce Retailers",
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"Target Mass Market"
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]
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},
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{
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"name": "Club W",
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"features": [
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"Ecommerce Retailers",
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"Brand Name Wine",
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"Target Mass Market"
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]
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},
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{
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"name": "Tasting Room",
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"features": [
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"Ecommerce Retailers",
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"True Personalized Selections",
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"Brand Name Wine"
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]
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},
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{
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"name": "hellovino",
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"features": [
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"Ecommerce Retailers",
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"True Personalized Selections",
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"No Inventory Cost",
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"Target Mass Market"
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]
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}
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]
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}
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]
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}
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}
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"""
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