Download comparing_size/utils.py from aggr8/Percept-V: direct link, hf CLI and curl.
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- Download file 3.09 kB
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https://huggingface.co/datasets/aggr8/Percept-V/resolve/main/comparing_size/utils.py
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hf download hf://datasets/aggr8/Percept-V/comparing_size/utils.py
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curl -L -o utils.py https://huggingface.co/datasets/aggr8/Percept-V/resolve/main/comparing_size/utils.py
3.09 kB
| import re | |
| def output_from_text(text): | |
| #extract the number in the last line of text | |
| try: | |
| lines = text.strip().split("\n") | |
| lines = list(filter(None, lines)) | |
| last_line = list(filter(None, lines[-1].split(" "))) | |
| #last_line = [x.lower() for x in last_line] | |
| answer = [] | |
| for x in last_line: | |
| word = re.sub(r'[^a-zA-Z]', '', x.lower()) | |
| if word == 'blue' or word == 'green': | |
| answer.append(word) | |
| if answer != []: | |
| return { | |
| "OUTPUT": answer, | |
| "ERROR": None | |
| } | |
| else: | |
| return{ | |
| "OUTPUT": None, | |
| "ERROR": 'Does not give answer in the correct format' | |
| } | |
| except Exception as e: | |
| return { | |
| "OUTPUT": None, | |
| "ERROR": str(e) | |
| } | |
| """import re | |
| import json | |
| import os | |
| def output_from_text(response): | |
| # Define a regular expression pattern to match 'Row Number: Color' | |
| try: | |
| pattern = r"ANSWER:\s*(.*)" | |
| # Find all matches in the response | |
| match = re.search(pattern, response) | |
| if match: | |
| row_colors = match.group(1).strip(" \n\t*").split(" ") | |
| # strip whitespacce and stars from all elements in the list | |
| row_colors = [color.strip("*") for color in row_colors] | |
| # # Convert the matches into a dictionary | |
| # row_colors = [color for row, color in matches] | |
| return { | |
| "OUTPUT":{ | |
| "Gold_output": row_colors, | |
| "num_objects": len(row_colors) | |
| }, | |
| "ERROR": None | |
| } | |
| else: | |
| return { | |
| "OUTPUT": None, | |
| "ERROR": "Output not found" | |
| } | |
| except Exception as e: | |
| return { | |
| "OUTPUT": None, | |
| "ERROR": str(e) | |
| } | |
| def gold_to_output(i): | |
| gold_output = None | |
| try: | |
| #get directory of this file | |
| dir_of_file = os.path.dirname(os.path.realpath(__file__)) | |
| #open the data file | |
| with open(dir_of_file + "\data.json") as f: | |
| data = json.load(f) | |
| row = data[i] | |
| gold_output = row['Gold_output'] | |
| output_prompt = "ANSWER: " | |
| for i , col in enumerate(gold_output): | |
| output_prompt += f"{col} " | |
| except Exception as e: | |
| return { | |
| "OUTPUT": None, | |
| "ERROR": str(e) | |
| } | |
| return { | |
| "OUTPUT": output_prompt, | |
| "ERROR": None | |
| } | |
| """ | |
| # import json | |
| # with open("./answer_vertexai.json", "r") as f: | |
| # data = json.load(f) | |
| # # print(data[1]) | |
| # for i in data: | |
| # output_text = i["gpt_response"] | |
| # i["Output"] = output_from_text(output_text)["OUTPUT"] | |
| # i["ERROR"] = output_from_text(output_text)["ERROR"] | |
| # # remove OUTPU/ key | |
| # # i.pop("OUTPUT") | |
| # with open("./answer_vertexai.json", "w") as f: | |
| # json.dump(data, f, indent=4) | |
| # print("Output saved to answer.json") |