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.
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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)