app.py

import inseq
import captum

import gradio as gr
import matplotlib.pyplot as plt


import torch
import os
# import nltk
import argparse
import random
import numpy as np

from argparse import Namespace
from tqdm.notebook import tqdm
from torch.utils.data import DataLoader
from functools import partial

import pandas as pd


model_es = "Helsinki-NLP/opus-mt-en-es"
model_fr = "Helsinki-NLP/opus-mt-en-fr"
model_zh = "Helsinki-NLP/opus-mt-en-zh"
model_sw = "Helsinki-NLP/opus-mt-en-sw"


model_es = inseq.load_model("Helsinki-NLP/opus-mt-en-es", "input_x_gradient")
model_fr = inseq.load_model("Helsinki-NLP/opus-mt-en-fr", "input_x_gradient")
model_zh = inseq.load_model("Helsinki-NLP/opus-mt-en-zh", "input_x_gradient")
model_sw = inseq.load_model("Helsinki-NLP/opus-mt-en-sw", "input_x_gradient")


def saliency_contrastive(input, ref, cont, inseq_model):

    # model = inseq.load_model(dict_models[inseq_model], "input_x_gradient")

    input = input if input != '' else 'example text'

    # out = model.attribute(
    # input_texts=input,
    # attribute_target=True,
    # step_scores=["probability"],
    # )


    out = dict_models[inseq_model].attribute(
        input, # Input
        ref, # Forced target
        attributed_fn="contrast_prob_diff",
        contrast_targets=cont, #contrastive
        attribute_target=True,
        # contrast_targets_alignments="auto",
        step_scores=["contrast_prob_diff"],
    )



    # Visualize the attributions and step scores
    html_out = out.show(return_html=True, display=False)
    return gr.Plot(value=html_out)


def saliency_inseq(input, inseq_model):

    # model = inseq.load_model(dict_models[inseq_model], "input_x_gradient")

    input = input if input != '' else 'example text'

    out = dict_models[inseq_model].attribute(
    input_texts=input,
    attribute_target=True,
    step_scores=["probability"],
    )
    text_out = out.info['generated_texts']

    # Visualize the attributions and step scores
    html_out = out.show(return_html=True, display=False)

    return [gr.Plot(value=html_out), text_out]



def model_generate(input, inseq_model):
    model = inseq.load_model(dict_models[inseq_model], "input_x_gradient")
    output = model.generate(input)

    # Visualize the attributions and step scores
    return output


dict_models = {
	'en-zh': model_zh,
	'en-fr': model_fr,
	'en-es': model_es,
	'en-sw': model_sw
}

saliency_examples = [
	"Peace of Mind: Protection for consumers.",
	"The sustainable development goals report: towards a rescue plan for people and planet",
	"We will leave no stone unturned to hold those responsible to account.",
	"The clock is now ticking on our work to finalise the remaining key legislative proposals presented by this Commission to ensure that citizens and businesses can reap the benefits of our policy actions.",
	"Pumpkins, squash and gourds, fresh or chilled, excluding courgettes",
	"The labour market participation of mothers with infants has even deteriorated over the past two decades, often impacting their career and incomes for years.",
]

contrastive_examples = [
["Peace of Mind: Protection for consumers.",
"Paz mental: protección de los consumidores",
"Paz de la mente: protección de los consumidores"],
["the slaughterer has finished his work.",
"l'abatteur a terminé son travail.",
"l'abatteuse a terminé son travail."],
['A fundamental shift is needed - in commitment, solidarity, financing and action - to put the world on a better path.',
 '需要在承诺、团结、筹资和行动方面进行根本转变,使世界走上更美好的道路。',
 '我们需要从根本上转变承诺、团结、资助和行动，使世界走上更美好的道路。',]
	]


#Load challenge set examples
df_challenge_set = pd.read_csv("challenge_sets.csv")
arr_challenge_set = df_challenge_set.values
arr_challenge_set = [[x[2], x[3], x[4], x[5]] for x in arr_challenge_set]



with gr.Blocks(theme=gr.themes.Default()) as demo:
	gr.Markdown("""
                # MAKE NMT Workshop \t `Inseq`
                """)


			
	with gr.Tab("Saliency"):
		gr.Markdown("""
		### Method: Input X Gradient
		The absolute value of these coefficients can be taken to represent feature importance.
		https://arxiv.org/abs/1312.6034
		""")
		with gr.Row():
			with gr.Column(scale=2):
				gr.Markdown(
					"""
					### Translation
					""")
				text_input = gr.Textbox(label="Source Text")
				text_output_box = gr.Textbox(label="Target Text")
			with gr.Column(scale=4):
				gr.Markdown(
					"""
					### If challenge is selected from the challenge set list bellow
					""")
				challenge_ex  = gr.Textbox(label="Challenge", interactive=False)
				category_minor  = gr.Textbox(label="category_minor", interactive=False)
				category_major  = gr.Textbox(label="category_major", interactive=False)

				with gr.Accordion("Challenge selection:"):
					gr.Examples(arr_challenge_set,[text_input, challenge_ex,category_minor,category_major], label="")
		# gr.Examples(saliency_examples, text_input)
		radio = gr.Radio(choices=['en-zh', 'en-es', 'en-fr', 'en-sw'], value="en-fr",  label= '', container=False)
		text_button = gr.Button("Translate")
		text_output = gr.HTML()

	with gr.Tab("Contrastive "):
		gr.Markdown("""
		### How is this feature X contributing to the prediction of A rather than B?
		https://aclanthology.org/2022.emnlp-main.14.pdf
		""")
		text_input_c = gr.Textbox(label="Source Text")
		text_input_ref = gr.Textbox(label="Reference Text (A)")
		text_input_cont = gr.Textbox(label="Contrastive Text (B)")
		gr.Examples(contrastive_examples,[text_input_c, text_input_ref, text_input_cont])
		text_output_c = gr.HTML()

		radio_c = gr.Radio(choices=['en-zh', 'en-es', 'en-fr', 'en-sw'], value="en-fr", label= '', container=False)
		text_button_c = gr.Button("Translate")

	text_button.click(fn=saliency_inseq, inputs=[text_input, radio], outputs=[text_output, text_output_box])
	text_button_c.click(fn=saliency_contrastive, inputs=[text_input_c, text_input_ref, text_input_cont, radio_c], outputs=text_output_c)



if __name__ == "__main__":   
    demo.launch()