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import csv |
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import openai |
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import os |
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import random |
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import re |
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random.seed(1929) |
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client = openai.OpenAI(api_key=os.getenv('OPENAI_API_KEY')) |
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model = "o4-mini" |
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def natural_enum(n: int) -> str: |
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nums = [str(i) for i in range(1, n + 1)] |
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if n == 1: |
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return nums[0] |
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return ", ".join(nums[:-1]) + f", or {nums[-1]}" |
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def shuffle_with_index_tracking(images, best_index=0, second_index=1): |
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copy = [i for i in images] |
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random.shuffle(copy) |
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return copy, copy.index(images[0]), copy.index(images[1]) |
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def ask_openai_best_outfit(post, images): |
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image_messages = [ |
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{ |
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"type": "input_image", |
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"image_url": url |
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} for url in images |
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] |
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prompt = ( |
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"You're a fashion-savvy assistant. Here are images of different outfits. " |
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f"{post['title']} {post['selftext']} " |
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f"Respond only with the number of the best outfit: {natural_enum(len(images))}." |
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) |
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try: |
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response = client.responses.create( |
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model=model, |
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input=[ |
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{"role": "user", "content": [ |
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{"type": "input_text", "text": prompt}, |
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*image_messages |
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]} |
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], |
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) |
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except openai.BadRequestError as e: |
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raise Exception("There is an issue possibly retrieving images for the post. It might have been deleted?") |
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text = response.output[1].content[0].text |
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match = re.search(r"\d+", text) |
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if match is not None: |
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return int(match[0]) |
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raise ValueError(f"Invalid response: {text}") |
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posts = csv.DictReader(open('./dataset.csv', 'r')) |
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score = 0.0 |
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items = 0 |
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for post in posts: |
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items += 1 |
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print(post['title']) |
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shuffled_images, best_after_shuffle, second_after_shuffle = shuffle_with_index_tracking(post["images"].split(",")) |
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predicted_index = ask_openai_best_outfit(post, shuffled_images) - 1 |
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if predicted_index == best_after_shuffle: |
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score += 1.0 |
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elif predicted_index == second_after_shuffle: |
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score += float(post["secondChoice"]) / float(post["firstChoiceVotes"]) |
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print(f"Total score: {score}/{items}") |
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