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import random
import pandas as pd
ques_patch = pd.read_csv('./utils/question_list.csv', header=None)
ques_patch = ques_patch[0].tolist()
ques_wsi = pd.read_csv('./utils/question_wsi_list.csv', header=None)
ques_wsi = ques_wsi[0].tolist()
def patch_formatting_itp(examples, tokenizer):
question = random.choice(ques_patch)
answer = examples["txt"]
text = f"<|Question|>{question}" + f"<|Answer|>{answer}{tokenizer.eos_token}"
# text = f"<DES> {answer}{tokenizer.eos_token}"
examples["text"] = text
return examples
def patch_formatting_vqap(examples, tokenizer):
question = examples["question"]
answer = examples["answer"]
question = question.replace("<image>\n", "")
question = question.replace("<image>", "")
text = f"<|Question|>{question}" + f"<|Answer|>{answer}{tokenizer.eos_token}"
examples["text"] = text
return examples
# CNX-PathLLM/MultiConversation
# [{'from': 'human', 'value': 'What are the key features of this image that suggest chronic pancreatitis?'},
# {'from': 'gpt', 'value': 'The presence of duct dilatation, fibrosis, and pancreatic tissue necrosis are indicative of chronic pancreatitis.}]
def patch_formatting_vmc(examples, tokenizer): # image conversations
conversation = examples["conversations"]
conversation = ast.literal_eval(conversation)
text = ""
for sentence in conversation:
sentence['value'] = sentence['value'].replace("<image>\n", "")
sentence['value'] = sentence['value'].replace("<image>", "")
if sentence['from'] == 'human':
text += f"<|Question|>{sentence['value']}"
elif sentence['from'] == 'gpt':
text += f"<|Answer|>{sentence['value']}{tokenizer.eos_token}"
examples["text"] = text
return examples
def patch_formatting_ytb(examples, tokenizer):
text = examples['conversations'].replace("<image>\n", "").replace("<image>", "")
question = ast.literal_eval(text[1:-1].split('\n')[0])['value'].replace("\n", "")
answer = ast.literal_eval(text[1:-1].split('\n')[1])['value'].replace("\n", "")
text = f"<|Question|>{question}" + f"<|Answer|>{answer}{tokenizer.eos_token}"
# text = f"<DES>{answer}{tokenizer.eos_token}"
examples["text"] = text
return examples
def wsi_formatting_des(examples, tokenizer):
question = random.choice(ques_wsi)
answer = examples["description"]
text = f"<|Question|>{question}" + f"<|Answer|>{answer}{tokenizer.eos_token}"
examples["text"] = text
examples["text_input"] = question
return examples
def wsi_formatting_qa_open(examples, tokenizer):
question = examples["question"]
answer = examples["answer"]
text = f"<|Question|>{question}" + f"<|Answer|>{answer}{tokenizer.eos_token}"
examples["text"] = text
examples["text_input"] = question
return examples
def wsi_formatting_qa_close(examples, tokenizer, prompt_tag=False):
question = examples["question"]
answer = examples["answer"]
if answer.lower() in ['yes', 'no']:
prompt = f" Please provide only the answer (either Yes or No) for the following statement. Do not include any explanations or additional text. Just give Yes or No."
else:
prompt = f" Please provide only the answer (for example, A. [Answer Text], B. [Answer Text], etc.) for the following question. Do not include any explanations or additional text. Just give the letter followed by the corresponding answer."
if prompt_tag:
text = f"<|Question|>{question}<|Prompt|>{prompt}" + f"<|Answer|>{answer}{tokenizer.eos_token}"
else:
text = f"<|Question|>{question}" + f"<|Answer|>{answer}{tokenizer.eos_token}"
examples["text"] = text
examples["text_input"] = question
return examples
def wsi_formatting_des_test(examples, tokenizer):
question = random.choice(ques_wsi)
answer = examples["description"]
text = f"<|Question|>{question}" + f"<|Answer|>"
examples["text"] = text
examples["answer"] = answer
examples["question"] = question
examples["text_input"] = question
return examples
def wsi_formatting_qa_open_test(examples, tokenizer):
question = examples["question"]
answer = examples["answer"]
text = f"<|Question|>{question}" + f"<|Answer|>"
examples["text"] = text
examples["text_input"] = question
return examples
def wsi_formatting_qa_close_test(examples, tokenizer, prompt_tag=True):
question = examples["question"]
answer = examples["answer"]
if answer.lower() in ['yes', 'no']:
prompt = f" Please provide only the answer (either Yes or No) for the following statement. Do not include any explanations or additional text. Just give Yes or No."
else:
prompt = f" Please provide only the answer (for example, A. [Answer Text], B. [Answer Text], etc.) for the following question. Do not include any explanations or additional text. Just give the letter followed by the corresponding answer."
if prompt_tag:
text = f"<|Question|>{question}<|Prompt|>{prompt}" + f"<|Answer|>"
else:
text = f"<|Question|>{question}" + f"<|Answer|>"
examples["text"] = text
examples["text_input"] = question
return examples |