| import base64 | |
| import pandas as pd | |
| from openai import OpenAI | |
| def get_questions(file_path): | |
| df = pd.read_json(file_path, lines=True) | |
| result=[] | |
| for index, row in df.iterrows(): | |
| result.append([row["Level"], row["Question"], row["file_name"], row["Final answer"]]) | |
| return result | |
| def get_img_b64(file_path): | |
| with open(file_path, "rb") as file: | |
| return base64.b64encode(file.read()).decode("utf-8") | |
| def get_final_answer(model, question, answer): | |
| prompt_template = """ | |
| You are an expert question answering assistant. Given a question and an initial answer, your task is to provide the final answer. | |
| Your final answer must be a number and/or string OR as few words as possible OR a comma-separated list of numbers and/or strings. | |
| If you are asked for a number, don't use comma to write your number neither use units such as $ or % unless specified otherwise. | |
| If you are asked for a string, don't use articles, neither abbreviations, and write the digits in plain text unless specified otherwise. | |
| If you are asked for a comma-separated list, apply the above rules depending of whether the element to be put in the list is a number or a string. | |
| If the final answer is a single word, start with an uppercase character. | |
| If the final answer is a comma-separated list of numbers, use a space character after each comma. | |
| If the final answer is a comma-separated list of strings, start with a lowercase character. | |
| **Question:** """ + question + """ | |
| **Initial answer:** """ + answer + """ | |
| **Example 1:** How many 'r's are in strawberry? 3 | |
| **Example 2:** What is the opposite of black? White | |
| **Example 3:** What is the biggest city in California? Los Angeles | |
| **Example 4:** What is the superlative of good? Best | |
| **Example 5:** What are the first 10 numbers in the Fibonacci sequence? 0, 1, 1, 2, 3, 5, 8, 13, 21, 34 | |
| **Final answer:** | |
| """ | |
| client = OpenAI() | |
| completion = client.chat.completions.create( | |
| messages=[{"role": "user", "content": [{"type": "text", "text": prompt_template}]}], | |
| model=model | |
| ) | |
| return completion.choices[0].message.content |