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| from distutils.command.upload import upload | |
| import pandas as pd | |
| import streamlit as st | |
| from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering | |
| from transformers import pipeline | |
| def load_data(file): | |
| df = pd.read_csv(file, encoding='utf-8', nrows=50) | |
| return df | |
| def load_pipeline(model_cp, tokenizer_cp): | |
| return pipeline("question-answering", model=model_cp, tokenizer=tokenizer_cp) | |
| def choose_model(): | |
| with st.sidebar: | |
| st.write("# Model Selection") | |
| model_cp = st.selectbox('Select model for inference', | |
| ('deepset/roberta-base-squad2', | |
| 'aidan-o-brien/recipe-improver')) | |
| # If not my model > model_cp = tokenizer_cp, else > albert tokenizer | |
| if model_cp == "aidan-o-brien/recipe-improver": | |
| return model_cp, "albert-base-v2" | |
| else: | |
| return model_cp, model_cp | |
| def choose_postprocessing(): | |
| with st.sidebar: | |
| st.write('# Postprocessing') | |
| postprocessing = st.selectbox('Select postprocessing method', | |
| ('no postprocessing', | |
| 'remove substrings', | |
| 'iteration')) | |
| return postprocessing | |
| def remove_substring(resp): | |
| """Function filters postprocessed model output to remove answers that | |
| are substrings of another answer.""" | |
| answers = [answer['answer'] for answer in resp] | |
| answers.sort(key=lambda s: len(s), reverse=True) | |
| filtered_answers = [] | |
| for s in answers: | |
| if not any([s in o for o in filtered_answers]): | |
| filtered_answers.append(s) | |
| filtered_resp = list(filter(lambda r: r['answer'] in filtered_answers, resp)) | |
| return filtered_resp | |
| def iterate(resp, context): | |
| # Remove substring answers | |
| filtered_resp = remove_substring(resp) | |
| # Remove best answer from original context | |
| best_answer = filtered_resp[0]['answer'] | |
| new_context = context.replace(best_answer, "") | |
| return new_context, filtered_resp | |
| def remove_substring_iter(pipeline, question, context): | |
| # Create answers placeholder | |
| ret_resp = [] | |
| # Loop through five times, removing best and re-running | |
| for _ in range(5): | |
| resp = pipeline(question, context, top_k=5, | |
| handle_impossible_answer=True) | |
| # Update context | |
| context, filtered_resp = iterate(resp, context) | |
| # If best score not above threshold, quit | |
| if filtered_resp[0]['score'] < 1e-2: | |
| break | |
| ret_resp.append(filtered_resp[0]) | |
| return ret_resp | |
| # Page config | |
| title = "Recipe Improver" | |
| icon = "🍣" | |
| st.set_page_config(page_title=title, page_icon=icon) | |
| st.title(title) | |
| # Choose model and postprocessing procedure | |
| model_cp, tokenizer_cp = choose_model() | |
| postprocessing = choose_postprocessing() | |
| # Load model and tokenizer | |
| question_answer = load_pipeline(model_cp, tokenizer_cp) | |
| st.write("Model and tokenizer successfully loaded.") | |
| # Upload csv - format with expander for aesthetics | |
| with st.expander("Upload csv file"): | |
| uploaded_file = st.file_uploader("Choose a csv file", type="csv", key='file_uploader') | |
| # If file is uploaded, run inference - QA pipelines can't run batch | |
| if uploaded_file is not None: | |
| df = load_data(uploaded_file) | |
| # Ask user for index to try out | |
| user_idx = st.text_input(f'Enter index (max {df.shape[0] - 1}', "0") | |
| # Only run rest of script if user provides index | |
| if user_idx is not None: | |
| first_example = df['review'][int(user_idx)] | |
| question = "How was the recipe improved?" | |
| if postprocessing == "no postprocessing": | |
| resp = question_answer(question=question, | |
| context=first_example, | |
| top_k=5) | |
| elif postprocessing == "remove substrings": | |
| resp = question_answer(question=question, | |
| context=first_example, | |
| top_k=5) | |
| resp = remove_substring(resp) | |
| elif postprocessing == "iteration": | |
| resp = remove_substring_iter(question_answer, | |
| question, | |
| first_example) | |
| # Present results | |
| st.markdown(f""" | |
| # Results | |
| The review provided was: | |
| {first_example} | |
| The question asked was: | |
| {question} | |
| The answers were: | |
| {resp} | |
| """) |