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# import json
# input_file = "/mnt/hwfile/ai4chem/hao/data_processing/SceMQA-main/chem/chem_multiple_choice.json"
# output_file = "/mnt/hwfile/ai4chem/hao/data_processing/SceMQA-main/test_chem_multiple_choice.jsonl"
# base_image_path = "/mnt/hwfile/ai4chem/hao/data_processing/SceMQA-main/Multiple_Choice/Chemistry/"
# # Read the input JSON file
# with open(input_file, 'r') as infile:
# data = json.load(infile)
# # Process each entry and write to the new JSONL file
# with open(output_file, 'w') as outfile:
# for idx, entry in enumerate(data, start=1):
# new_entry = {
# "id": str(idx),
# "images": [base_image_path + entry["ImagePath"] + ".png"],
# "conversations": [
# {"from": "human", "value": "<image>\n" + entry["Question"]},
# {"from": "gpt", "value": entry["Answer (final answer highlighted)"]}
# ]
# }
# outfile.write(json.dumps(new_entry, ensure_ascii=False) + "\n")
# print(f"Processing complete. The new JSONL file is saved at {output_file}")
# import json
# import os
# input_file = "/mnt/hwfile/ai4chem/hao/data_processing/SceMQA-main/chem/chem_multiple_choice.json"
# output_file = "/mnt/hwfile/ai4chem/hao/data_processing/SceMQA-main/test_chem_multiple_choice.jsonl"
# base_image_path = "/mnt/hwfile/ai4chem/hao/data_processing/SceMQA-main/Multiple_Choice/"
# # Read the input JSON file
# with open(input_file, 'r') as infile:
# data = json.load(infile)
# # Process each entry and write to the new JSONL file
# with open(output_file, 'w') as outfile:
# for idx, entry in enumerate(data, start=1):
# image_path_png = base_image_path + entry["ImagePath"] + ".png"
# image_path_jpg = base_image_path + entry["ImagePath"] + ".jpg"
# # Check if the .png file exists, otherwise use the .jpg file
# if not os.path.exists(image_path_png):
# image_path = image_path_jpg
# else:
# image_path = image_path_png
# new_entry = {
# "id": str(idx),
# "images": [image_path],
# "conversations": [
# {"from": "human", "value": "<image>\n" + entry["Question"]},
# {"from": "gpt", "value": entry["Answer (final answer highlighted)"][0]}
# ]
# }
# outfile.write(json.dumps(new_entry, ensure_ascii=False) + "\n")
# print(f"Processing complete. The new JSONL file is saved at {output_file}")
import json
import os
import re
input_file = "/mnt/hwfile/ai4chem/hao/data_processing/SceMQA-main/chem/chem_free_response.json"
output_file = "/mnt/hwfile/ai4chem/hao/data_processing/SceMQA-main/test_chem_free_response.jsonl"
base_image_path = "/mnt/hwfile/ai4chem/hao/data_processing/SceMQA-main/Free_Response/"
# Read the input JSON file
with open(input_file, 'r') as infile:
data = json.load(infile)
# Process each entry and write to the new JSONL file
with open(output_file, 'w') as outfile:
for idx, entry in enumerate(data, start=1):
image_path_png = base_image_path + entry["ImagePath"] + ".png"
image_path_jpg = base_image_path + entry["ImagePath"] + ".jpg"
# Check if the .png file exists, otherwise use the .jpg file
if not os.path.exists(image_path_png):
image_path = image_path_jpg
else:
image_path = image_path_png
# Extract the content inside answer{???} from the Answer attribute
match = re.search(r'answer\{(.*?)\}', entry["Answer (final answer highlighted)"], re.IGNORECASE)
answer_content = match.group(1) if match else ""
new_entry = {
"id": str(idx),
"images": [image_path],
"conversations": [
{"from": "human", "value": "<image>\n" + entry["Question"]},
{"from": "gpt", "value": answer_content}
]
}
outfile.write(json.dumps(new_entry, ensure_ascii=False) + "\n")
print(f"Processing complete. The new JSONL file is saved at {output_file}") |