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import gradio as gr
import pandas as pd
from jiwer import wer
import re
import os
REGEX_YAML_BLOCK = re.compile(r"---[\n\r]+([\S\s]*?)[\n\r]+---[\n\r]")
def parse_readme(filepath):
if not os.path.exists(filepath):
return "No README.md found."
with open(filepath, "r") as f:
text = f.read()
match = REGEX_YAML_BLOCK.search(text)
if match:
text = text[match.end():]
return text
def get_wer(df: pd.DataFrame):
print(df.keys())
preds = df.iloc[:, 0].tolist()
truths = df.iloc[:, 1].tolist()
print(truths, preds, type(truths))
err = wer(truths, preds)
return err
def compute(input_df: pd.DataFrame = None, input_file: str = None):
if input_df is not None and not input_df.empty and input_file is None:
print("in df")
if not (input_df.values == "").any():
print("in df but empty string")
return get_wer(input_df)
elif input_file and (input_df.values == "").any():
print("in file")
file_df = pd.read_csv(input_file.name)
print(file_df)
return get_wer(file_df)
else:
print("in error")
raise ValueError("Please don't provide both DataFrame and file.")
description = """
To calculate WER:
* Type the `prediction` and the `truth` in the respective columns in the below calculator.
* You can insert multiple predictions and truths by clicking on the `New row` button.
* To calculate the WER after inserting all the texts, click on `Submit`.
OR
* Upload a CSV file with the columns being `prediction` and `truth`.
* The first row of the file is supposed to have the column names.
* The sentences should be enclosed within `""` and the prediction and truth need to be separated by `,`.
* Find an example file [here](https://huggingface.co/spaces/neuralspace/wer_calculator/resolve/main/example.csv).
* To calculate the WER after uploading the CSV file, click on `Submit`.
NOTE: Pleasd don't use both the methods at once.
"""
demo = gr.Interface(
fn=compute,
inputs=[
gr.components.Dataframe(
headers=["prediction", "truth"],
col_count=2,
row_count=1,
label="Input"
),
gr.File(
file_count='single',
file_types=['.csv'],
label="CSV File"
)
],
outputs=gr.components.Textbox(label="WER"),
description=description,
title="WER Calculator",
article=parse_readme("README.md")
)
demo.launch()
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