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| """ | |
| Code here was custom written, but based on ChatGPT-4. | |
| Example prompt: write a python function that uses an api that given an image returns a text summary of it | |
| """ | |
| import json | |
| import os | |
| from concurrent.futures import ThreadPoolExecutor | |
| from typing import Dict, List, Optional | |
| from dotenv import load_dotenv | |
| from google.cloud import vision | |
| from google.oauth2 import service_account | |
| from code_tasks.image_dimensions import get_image_dimensions | |
| load_dotenv() | |
| VISION_CREDENTIALS = service_account.Credentials.from_service_account_info( | |
| json.loads(os.environ["GOOGLE_APPLICATION_KEY"]) | |
| ) | |
| def analyze_images(images) -> List[Dict]: | |
| max = 20 | |
| def get_image_info(image_url: str) -> Optional[Dict]: | |
| client = vision.ImageAnnotatorClient(credentials=VISION_CREDENTIALS) | |
| image = vision.Image() | |
| image.source.image_uri = image_url # type: ignore | |
| try: | |
| response = client.label_detection(image=image) # type: ignore | |
| labels = [label.description for label in response.label_annotations] | |
| dimensions = get_image_dimensions(image_url) | |
| return { | |
| "url": image_url, | |
| "labels": ", ".join(labels), | |
| "dimensions": f"{dimensions[0]}x{dimensions[1]}", | |
| } | |
| except: | |
| return None | |
| with ThreadPoolExecutor() as executor: | |
| images = executor.map(get_image_info, images[:max]) | |
| return [i for i in images if i] | |