text_summariser / chatfuncs /helper_functions.py
seanpedrickcase's picture
Dockerfile now loads models to local folder. Can use custom output folder. requrirements for GPU-enabled summarisation now in separate file to hopefully avoid HF space issues.
3809dc8
import os
import re
import pandas as pd
import gradio as gr
import os
import shutil
import os
import shutil
import getpass
import gzip
import pickle
def get_or_create_env_var(var_name, default_value):
# Get the environment variable if it exists
value = os.environ.get(var_name)
# If it doesn't exist, set it to the default value
if value is None:
os.environ[var_name] = default_value
value = default_value
return value
# Retrieving or setting output folder
env_var_name = 'GRADIO_OUTPUT_FOLDER'
default_value = 'output/'
output_folder = get_or_create_env_var(env_var_name, default_value)
print(f'The value of {env_var_name} is {output_folder}')
def ensure_output_folder_exists(output_folder):
"""Checks if the output folder exists, creates it if not."""
folder_name = output_folder
if not os.path.exists(folder_name):
# Create the folder if it doesn't exist
os.makedirs(folder_name)
print(f"Created the output folder:", folder_name)
else:
print(f"The output folder already exists:", folder_name)
# Attempt to delete content of gradio temp folder
def get_temp_folder_path():
username = getpass.getuser()
return os.path.join('C:\\Users', username, 'AppData\\Local\\Temp\\gradio')
def empty_folder(directory_path):
if not os.path.exists(directory_path):
#print(f"The directory {directory_path} does not exist. No temporary files from previous app use found to delete.")
return
for filename in os.listdir(directory_path):
file_path = os.path.join(directory_path, filename)
try:
if os.path.isfile(file_path) or os.path.islink(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception as e:
#print(f'Failed to delete {file_path}. Reason: {e}')
print('')
def get_file_path_end(file_path):
# First, get the basename of the file (e.g., "example.txt" from "/path/to/example.txt")
basename = os.path.basename(file_path)
# Then, split the basename and its extension and return only the basename without the extension
filename_without_extension, _ = os.path.splitext(basename)
#print(filename_without_extension)
return filename_without_extension
def get_file_path_end_with_ext(file_path):
match = re.search(r'(.*[\/\\])?(.+)$', file_path)
filename_end = match.group(2) if match else ''
return filename_end
def detect_file_type(filename):
"""Detect the file type based on its extension."""
if (filename.endswith('.csv')) | (filename.endswith('.csv.gz')) | (filename.endswith('.zip')):
return 'csv'
elif filename.endswith('.xlsx'):
return 'xlsx'
elif filename.endswith('.parquet'):
return 'parquet'
elif filename.endswith('.pkl.gz'):
return 'pkl.gz'
else:
raise ValueError("Unsupported file type.")
def read_file(filename):
"""Read the file based on its detected type."""
file_type = detect_file_type(filename)
print("Loading in file")
if file_type == 'csv':
file = pd.read_csv(filename, low_memory=False).reset_index(drop=True).drop(["index", "Unnamed: 0"], axis=1, errors="ignore")
elif file_type == 'xlsx':
file = pd.read_excel(filename).reset_index(drop=True).drop(["index", "Unnamed: 0"], axis=1, errors="ignore")
elif file_type == 'parquet':
file = pd.read_parquet(filename).reset_index(drop=True).drop(["index", "Unnamed: 0"], axis=1, errors="ignore")
elif file_type == 'pkl.gz':
with gzip.open(filename, 'rb') as file:
file = pickle.load(file)
#file = pd.read_pickle(filename)
print("File load complete")
return file
def put_columns_in_df(in_file, in_bm25_column):
'''
When file is loaded, update the column dropdown choices and change 'clean data' dropdown option to 'no'.
'''
file_list = [string.name for string in in_file]
#print(file_list)
data_file_names = [string for string in file_list]
data_file_name = data_file_names[0]
new_choices = []
concat_choices = []
df = read_file(data_file_name)
new_choices = list(df.columns)
concat_choices.extend(new_choices)
return gr.Dropdown(choices=concat_choices), df
def put_columns_in_join_df(in_file, in_bm25_column):
'''
When file is loaded, update the column dropdown choices and change 'clean data' dropdown option to 'no'.
'''
print("in_bm25_column")
new_choices = []
concat_choices = []
df = read_file(in_file.name)
new_choices = list(df.columns)
print(new_choices)
concat_choices.extend(new_choices)
return gr.Dropdown(choices=concat_choices)
def dummy_function(gradio_component):
"""
A dummy function that exists just so that dropdown updates work correctly.
"""
return None
def display_info(info_component):
gr.Info(info_component)