app.py

import streamlit as st
import pandas as pd
import numpy as np
import torch
import evaluate

from datasets import load_dataset
from evaluate import load as load_metric
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from sklearn.metrics import accuracy_score, f1_score
from tqdm.auto import tqdm
from torch.utils.data import DataLoader

select = st.selectbox('Which model would you like to evaluate?',
	('Bart', 'mBart'))

def get_datasets():
	if select == 'Bart':
		all_datasets = ["Communication Networks: unseen questions", "Communication Networks: unseen answers"]
	if select == 'mBart':
		all_datasets = ["Micro Job: unseen questions", "Micro Job: unseen answers", "Legal Domain: unseen questions", "Legal Domain: unseen answers"]
	return all_datasets

all_datasets = get_datasets()

def get_split(dataset_name):
	if dataset_name == "Communication Networks: unseen questions":
		split = load_dataset("Short-Answer-Feedback/saf_communication_networks_english", split="test_unseen_questions")
	if dataset_name == "Communication Networks: unseen answers":
		split = load_dataset("Short-Answer-Feedback/saf_communication_networks_english", split="test_unseen_answers")
	if dataset_name == "Micro Job: unseen questions":
		split = load_dataset("Short-Answer-Feedback/saf_micro_job_german", split="test_unseen_questions")
	if dataset_name == "Micro Job: unseen answers":
		split = load_dataset("Short-Answer-Feedback/saf_micro_job_german", split="test_unseen_answers")
	if dataset_name	== "Legal Domain: unseen questions":
		split = load_dataset("Short-Answer-Feedback/saf_legal_domain_german", split="test_unseen_questions")
	if dataset_name	== "Legal Domain: unseen answers":
		split = load_dataset("Short-Answer-Feedback/saf_legal_domain_german", split="test_unseen_answers")
	return split

def get_model(datasetname):
	if datasetname == "Communication Networks: unseen questions" or datasetname == "Communication Networks: unseen answers":
		model = "Short-Answer-Feedback/bart-finetuned-saf-communication-networks"
	if datasetname == "Micro Job: unseen questions" or datasetname == "Micro Job: unseen answers":
		model = "Short-Answer-Feedback/mbart-finetuned-saf-micro-job"
	if datasetname == "Legal Domain: unseen questions" or datasetname == "Legal Domain: unseen answers":
		model = "Short-Answer-Feedback/mbart-finetuned-saf-legal-domain"
	return model

def get_tokenizer(datasetname):
	if datasetname == "Communication Networks: unseen questions" or datasetname == "Communication Networks: unseen answers":
		tokenizer = "Short-Answer-Feedback/bart-finetuned-saf-communication-networks"
	if datasetname == "Micro Job: unseen questions" or datasetname == "Micro Job: unseen answers":
		tokenizer = "Short-Answer-Feedback/mbart-finetuned-saf-micro-job"
	if datasetname == "Legal Domain: unseen questions" or datasetname == "Legal Domain: unseen answers":
		tokenizer = "Short-Answer-Feedback/mbart-finetuned-saf-legal-domain"
	return tokenizer 

sacrebleu = load_metric('sacrebleu')
rouge = load_metric('rouge')
meteor = load_metric('meteor')
bertscore = load_metric('bertscore')

MAX_INPUT_LENGTH = 256
MAX_TARGET_LENGTH = 128

def preprocess_function(examples):    
    """
    Preprocess entries of the given dataset

    Params:
        examples (Dataset): dataset to be preprocessed
    Returns:
        model_inputs (BatchEncoding): tokenized dataset entries
    """
    inputs, targets = [], []
    for i in range(len(examples['question'])):
        inputs.append(f"Antwort: {examples['provided_answer'][i]} Lösung: {examples['reference_answer'][i]} Frage: {examples['question'][i]}")
        targets.append(f"{examples['verification_feedback'][i]} Feedback: {examples['answer_feedback'][i]}")

    # apply tokenization to inputs and labels
    model_inputs = tokenizer(inputs, max_length=MAX_INPUT_LENGTH, padding='max_length', truncation=True)
    labels = tokenizer(text_target=targets, max_length=MAX_TARGET_LENGTH, padding='max_length', truncation=True)

    model_inputs['labels'] = labels['input_ids']

    return model_inputs


def flatten_list(l):
    """
    Utility function to convert a list of lists into a flattened list
    Params:
        l (list of lists): list to be flattened
    Returns:
        A flattened list with the elements of the original list
    """
    return [item for sublist in l for item in sublist]


def extract_feedback(predictions):
    """
    Utility function to extract the feedback from the predictions of the model
    Params:
        predictions (list): complete model predictions
    Returns:
        feedback (list): extracted feedback from the model's predictions
    """
    feedback = []
    # iterate through predictions and try to extract predicted feedback
    for pred in predictions:
        try:
            fb = pred.split(':', 1)[1]
        except IndexError:
            try:
                if pred.lower().startswith('partially correct'):
                    fb = pred.split(' ', 1)[2]
                else:
                    fb = pred.split(' ', 1)[1]
            except IndexError:
                fb = pred
        feedback.append(fb.strip())
    
    return feedback


def extract_labels(predictions):
    """
    Utility function to extract the labels from the predictions of the model
    Params:
        predictions (list): complete model predictions
    Returns:
        feedback (list): extracted labels from the model's predictions
    """
    labels = []
    for pred in predictions:
        if pred.lower().startswith('correct'):
            label = 'Correct'
        elif pred.lower().startswith('partially correct'):
            label = 'Partially correct'
        elif pred.lower().startswith('incorrect'):
            label = 'Incorrect'
        else:
            label = 'Unknown label'
        labels.append(label)
    
    return labels


def get_predictions_labels(model, dataloader):
    """
    Evaluate model on the given dataset

    Params:
        model (PreTrainedModel): seq2seq model
        dataloader (torch Dataloader): dataloader of the dataset to be used for evaluation
    Returns:
        results (dict): dictionary with the computed evaluation metrics
        predictions (list): list of the decoded predictions of the model
    """
    decoded_preds, decoded_labels = [], []

    model.eval()
    # iterate through batchs in the dataloader
    for batch in tqdm(dataloader):
        with torch.no_grad():
            batch = {k: v.to(device) for k, v in batch.items()}
            # generate tokens from batch
            generated_tokens = model.generate(
                batch['input_ids'],
                attention_mask=batch['attention_mask'],
                max_length=MAX_TARGET_LENGTH
            )
            # get golden labels from batch
            labels_batch = batch['labels']
            
            # decode model predictions and golden labels
            decoded_preds_batch = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
            decoded_labels_batch = tokenizer.batch_decode(labels_batch, skip_special_tokens=True)

            decoded_preds.append(decoded_preds_batch)
            decoded_labels.append(decoded_labels_batch)

    # convert predictions and golden labels into flattened lists
    predictions = flatten_list(decoded_preds)
    labels = flatten_list(decoded_labels)

    return predictions, labels





def load_data():
    df = pd.DataFrame(columns=['Model', 'Dataset', 'SacreBLEU', 'ROUGE-2', 'METEOR', 'BERTScore', 'Accuracy', 'Weighted F1', 'Macro F1'])
    for ds in all_datasets:
        split = get_split(ds)
        model = AutoModelForSeq2SeqLM.from_pretrained(get_model(ds))
        tokenizer = AutoTokenizer.from_pretrained(get_tokenizer(ds))

        processed_dataset = split.map(
            preprocess_function,
            batched=True,
            remove_columns=split.column_names
        )
        processed_dataset.set_format('torch')

        dataloader = DataLoader(processed_dataset, batch_size=4)

        predictions, labels = get_predictions_labels(model, dataloader)

        predicted_feedback = extract_feedback(predictions)
        predicted_labels = extract_labels(predictions)

        reference_feedback = [x.split('Feedback:', 1)[1].strip() for x in labels]
        reference_labels = [x.split('Feedback:', 1)[0].strip() for x in labels]

        rouge_score = rouge.compute(predictions=predicted_feedback, references=reference_feedback)['rouge2']
        bleu_score = sacrebleu.compute(predictions=predicted_feedback, references=[[x] for x in reference_feedback])['score']
        meteor_score = meteor.compute(predictions=predicted_feedback, references=reference_feedback)['meteor']
        bert_score = bertscore.compute(predictions=predicted_feedback, references=reference_feedback, lang='de', model_type='bert-base-multilingual-cased', rescale_with_baseline=True)

        reference_labels_np = np.array(reference_labels)
        accuracy_value = accuracy_score(reference_labels_np, predicted_labels)
        f1_weighted_value = f1_score(reference_labels_np, predicted_labels, average='weighted')
        f1_macro_value = f1_score(reference_labels_np, predicted_labels, average='macro', labels=['Incorrect', 'Partially correct', 'Correct'])

        new_row_data = {"Model": get_model(ds), "Dataset": ds, "SacreBLEU": bleu_score, "ROUGE-2": rouge_score, "METEOR": meteor_score, "BERTScore": bert_score, "Accuracy": accuracy_value, "Weighted F1": f1_weighted_value, "Macro F1": f1_macro_value}
        new_row = pd.DataFrame(new_row_data)
        
        df = pd.concat([df, new_row])
    return df

dataframe = load_data()

st.dataframe(dataframe)