import csv
import os
import pandas as pd
from scipy.io import loadmat
import json
import re
def _read_files(directory):
files = []
print(f"Traversing the directory：{directory}")
for root, dirs, files_in_dir in os.walk(directory):
print(f"Current directory：{root}")
for file in files_in_dir:
file_path = os.path.join(root, file)
print(f"Find file：{file_path}")
try:
size = os.path.getsize(file_path)
print(f"File size：{size} byte")
if size < 3 * 1024 * 1024:
if file.endswith('.txt') or file.endswith('.md'):
try:
with open(file_path, 'r', encoding='utf-8') as f:
file_content = f.read()
except UnicodeDecodeError:
with open(file_path, 'r', encoding='latin-1') as f:
file_content = f.read()
files.append({"file_path": file_path, "content": file_content})
print(f"read {file} complete, length：{len(file_content)}")
elif file.endswith('.docx'):
import docx
doc = docx.Document(file_path)
file_content = "\n".join([para.text for para in doc.paragraphs])
files.append({"file_path": file_path, "content": file_content})
print(f"read {file} complete")
elif file.endswith('.pdf'):
import pdfplumber
with pdfplumber.open(file_path) as pdf:
file_content = "\n".join([page.extract_text() or "" for page in pdf.pages])
files.append({"file_path": file_path, "content": file_content})
print(f"read {file} complete")
elif file.endswith('.csv'):
with open(file_path, 'r', encoding='utf-8') as f:
reader = csv.reader(f)
file_content = "\n".join([",".join(row) for row in reader])
files.append({"file_path": file_path, "content": file_content})
print(f"read {file} complete")
elif file.endswith('.xls') or file.endswith('.xlsx'):
df = pd.read_excel(file_path)
file_content = df.to_csv(index=False)
files.append({"file_path": file_path, "content": file_content})
print(f"read {file} complete")
elif file.endswith('.mat'):
try:
mat_data = loadmat(file_path)
file_content = str(mat_data)
files.append({"file_path": file_path, "content": file_content})
print(f"read {file} complete")
except Exception as e:
print(f"Error processing.mat file {file_path} :{e}")
elif 'readme' in file.lower() or 'annotation' in file.lower() or 'record' in file.lower():
try:
with open(file_path, 'r', encoding='utf-8') as f:
file_content = f.read()
except UnicodeDecodeError:
with open(file_path, 'r', encoding='latin-1') as f:
file_content = f.read()
files.append({"file_path": file_path, "content": file_content})
print(f"read {file} complete, length：{len(file_content)}")
except Exception as e:
print(f"Error processing.mat file {file_path}：{e}")
return files
def _filter_files_with_gpt(files: str, client_paras: dict, client_type: str, completion_para: dict):
"""
Analyzes the given files using a GPT model and extracts JSON information containing
sampling rate and channel names.
Some large models may return mixed text; therefore, this function includes fault-tolerant
logic to parse the response correctly.
Args:
files (List[Dict[str, Any]]): A list of file information dictionaries, each containing:
- "file_path" (str): Path to the file.
- "content" (str): File content.
client_paras (Dict[str, Any]): Parameters required for initializing the GPT client.
client_type (str): Specifies the type of GPT client to use. Supported types:
- "AzureOpenAI"
- "OpenAI"
completion_para (Dict[str, Any]): Parameters for the GPT model completion request.
Returns:
List[Tuple[Dict[str, Any], Dict[str, Any]]]: A list of tuples where each tuple contains:
- The original file information.
- Extracted sampling rate and channel names in JSON format.
"""
def parse_json_with_fallback(response_text: str):
"""
Extracts and parses a JSON structure from the given GPT response text.
The function attempts to retrieve a valid JSON object using the following steps:
1. Prioritizes JSON code blocks enclosed by ```json ... ``` markers.
2. If not found or parsing fails, attempts to extract JSON from the first occurrence
of '{' to the last occurrence of '}'.
3. If parsing still fails, raises a ValueError.
Args:
response_text (str): The text response from the GPT model.
Returns:
Dict[str, Any]: Extracted JSON data.
Raises:
ValueError: If no valid JSON can be extracted.
"""
# Step 1: Attempt to extract JSON from ```json ... ``` blocks
code_blocks = re.findall(r'```json\s*(.*?)\s*```', response_text, re.DOTALL)
if code_blocks:
for block in code_blocks:
block = block.strip()
try:
return json.loads(block)
except Exception:
continue # Try the next JSON block if parsing fails
# Step 2: If no JSON block found, attempt to extract from the entire response
text = response_text.strip()
start_index = text.find('{')
end_index = text.rfind('}')
if start_index != -1 and end_index != -1 and start_index < end_index:
possible_json = text[start_index:end_index + 1]
try:
return json.loads(possible_json)
except Exception:
pass
# Step 3: If all attempts fail, raise an error
raise ValueError("No valid JSON could be extracted from the GPT response.")
processed_files = []
try:
if client_type == "AzureOpenAI":
from openai import AzureOpenAI
client = AzureOpenAI(**client_paras)
elif client_type == "OpenAI":
from openai import OpenAI
client = OpenAI(**client_paras)
else:
raise ValueError("Unsupported client_type. Supported types are 'AzureOpenAI' and 'OpenAI'.")
for file_info in files:
try:
file_path = file_info["file_path"]
content = file_info["content"]
print(f"Files being processed: {file_path}")
response = client.chat.completions.create(
**completion_para,
response_format={"type": "json_object"},
messages=[
{
"role": "system",
"content": (
"You are a highly capable assistant. Your task is to analyze the provided files and "
"extract the **sampling rates** and **channel names** in a well-structured JSON format. "
"The output should follow this structure:\n\n"
"{\n"
' "sampling_rate": <number or null>,\n'
' "channels": [<channel_name_1>, <channel_name_2>, ...] or null\n'
"}\n\n"
"If either sampling rates or channel names are not available in the file, return `null` for the corresponding field. "
"Ensure the response strictly follows the specified JSON format without additional text or explanations." ),
},
{
"role": "user",
"content": json.dumps({
"file_path": file_path,
"content": content,
"format": "json"
})
}
]
)
print(f"LLM response: {response}")
response_text = response.choices[0].message.content
decision = parse_json_with_fallback(response_text)
processed_files.append((file_info, decision))
print(f"Parsing LLM response: {decision}")
except Exception as e:
print(f"Error LLM response: {e}")
continue
return processed_files
except Exception as e:
print(f"Error using GPT to filter files: {e}")
return processed_files
def _resolve_sampling_rate_conflict(sampling_rate_list):
if not sampling_rate_list:
return None, "Sample rate information not found"
print("Multiple different sample rate information was detected：")
for i, (file_info, sampling_rate) in enumerate(sampling_rate_list, 1):
print(f"{i}: From file {file_info['file_path']}")
print(f" Sampling rate: {sampling_rate}")
print(f"{len(sampling_rate_list) + 1}: Using no sample rate information")
chosen_index = None
while chosen_index not in range(1, len(sampling_rate_list) + 2):
try:
user_input = input(f"Please select a number for the sample rate information (input format is '1'）: ")
chosen_index = int(user_input)
except ValueError:
print("Invalid selection, please re-enter.")
if chosen_index == len(sampling_rate_list) + 1:
return None, "Sample rate data is discarded"
else:
return sampling_rate_list[chosen_index - 1][1], None
def _resolve_channel_names_conflict(channel_info_list):
if not channel_info_list:
return [], "No channel name information found"
print("Several different channel name information was detected:")
for i, (file_info, channel_names) in enumerate(channel_info_list, 1):
print(f"{i}: From file {file_info['file_path']}")
print(f" Channel names: {channel_names}")
print(f"{len(channel_info_list) + 1}: Using no channel names information")
chosen_index = None
while chosen_index not in range(1, len(channel_info_list) + 2):
try:
user_input = input(f"Please select a number for the channel name information (input format is '1'): ")
chosen_index = int(user_input)
except ValueError:
print("Invalid selection, please re-enter.")
if chosen_index == len(channel_info_list) + 1:
return [], "The channel name data was discarded."
else:
return channel_info_list[chosen_index - 1][1], None
[docs]
def llm_description_file_parser(directory: str, client_type: str, client_paras: dict, completion_para: dict):
"""
Parse files in a specified directory to extract sampling rate and channel information using a Large Language Model
(LLM) API.
This function traverses a directory to read various file formats. It extracts sampling rates and channel names
from the files using an LLM API (e.g., GPT-4), and processes the extracted information based on user inputs to
resolve conflicts.
Parameters
----------
directory : str
The directory path where the files are stored for processing. Generally speaking, it will be the root directory
of the dataset
client_type : str
The type of LLM client to use (e.g., "AzureOpenAI", "OpenAI").
client_paras : dict
A dictionary containing the parameters needed to initialize the LLM API client. Please refer to OpenAI
documentation.
Returns
-------
dict: A dictionary containing the parsed sampling rate and channel information.
Returns an error message if no files are selected or if all data is discarded due to conflicts.
Raises
------
ValueError: If no files are selected for further analysis or if there are conflicts in the extracted data.
Examples
--------
>>> directory = 'path/to/description/directory'
>>> client_paras = {"api_key": "your_api_key", "api_version": "2023-03-15-preview"}
>>> client_type = "AzureOpenAI"
>>> result = llm_description_file_parser(directory, client_paras, client_type)
>>> print("The end result:", json.dumps(result, indent=4, ensure_ascii=False))
Contributor
-----------
Jingyi Ding (Jingyi.Ding21@student.xjtlu.edu.cn), on 2024-07-26.
EEGUnity Team modified it on 2025-02-23
"""
files = _read_files(directory)
processed_files = _filter_files_with_gpt(files, client_paras=client_paras, client_type=client_type, completion_para=completion_para)
if not processed_files:
return {"error": "No files were selected for further analysis"}
sampling_rate_list = []
channel_info_list = []
channel_keys = ["channel_names", "channels", "channel name", "names of channel"]
sampling_rate_keys = ["sampling_rate", "sampling rates", "sample rate", "samplingrate"]
for file_info, decision in processed_files:
for key in sampling_rate_keys:
if key in decision:
sampling_rate_list.append((file_info, decision[key]))
break
for key in channel_keys:
if key in decision:
channel_info_list.append((file_info, decision[key]))
break
selected_info = {"sampling_rate": None, "channels": []}
selected_sampling_rate, sampling_rate_msg = _resolve_sampling_rate_conflict(sampling_rate_list)
if sampling_rate_msg:
print(f" Sampling rate data: {sampling_rate_msg}")
selected_info["sampling_rate"] = None
else:
selected_info["sampling_rate"] = selected_sampling_rate
selected_channel_names, channel_names_msg = _resolve_channel_names_conflict(channel_info_list)
if channel_names_msg:
print(f" Channel name data: {channel_names_msg}")
selected_info["channels"] = []
else:
selected_info["channels"] = selected_channel_names
if selected_info["sampling_rate"] is None and not selected_info["channels"]:
return {"error": "All data is discarded"}
return {"sampling_rate": selected_info["sampling_rate"], "channels": selected_info["channels"]}
