app.py

from datasets import load_dataset
from transformers import (
    DPRQuestionEncoder,
    DPRQuestionEncoderTokenizer,
    MT5ForConditionalGeneration,
    AutoTokenizer,
    AutoModelForCTC,
    Wav2Vec2Tokenizer,
)
from general_utils import (
    embed_questions,
    transcript,
    remove_chars_to_tts,
    parse_final_answer,
)
from typing import List
import gradio as gr
from article_app import article, description, examples
from haystack.nodes import DensePassageRetriever
from haystack.document_stores import InMemoryDocumentStore
import numpy as np
from sentence_transformers import SentenceTransformer, util, CrossEncoder

topk = 21
minchars = 200
min_snippet_length = 20
device = "cpu"
covidterms = ["covid19", "covid", "coronavirus", "covid-19", "sars-cov-2"]

models = {
    "wav2vec2-iic": {
        "processor": Wav2Vec2Tokenizer.from_pretrained(
            "IIC/wav2vec2-spanish-multilibrispeech"
        ),
        "model": AutoModelForCTC.from_pretrained(
            "IIC/wav2vec2-spanish-multilibrispeech"
        ),
    },
}


tts_es = gr.Interface.load("huggingface/facebook/tts_transformer-es-css10")


params_generate = {
    "min_length": 50,
    "max_length": 250,
    "do_sample": False,
    "early_stopping": True,
    "num_beams": 8,
    "temperature": 1.0,
    "top_k": None,
    "top_p": None,
    "no_repeat_ngram_size": 3,
    "num_return_sequences": 1,
}

dpr = DensePassageRetriever(
    document_store=InMemoryDocumentStore(),
    query_embedding_model="IIC/dpr-spanish-question_encoder-allqa-base",
    passage_embedding_model="IIC/dpr-spanish-passage_encoder-allqa-base",
    max_seq_len_query=64,
    max_seq_len_passage=256,
    batch_size=512,
    use_gpu=False,
)

mt5_tokenizer = AutoTokenizer.from_pretrained("IIC/mt5-base-lfqa-es")
mt5_lfqa = MT5ForConditionalGeneration.from_pretrained("IIC/mt5-base-lfqa-es")

similarity_model = SentenceTransformer(
    "distiluse-base-multilingual-cased", device="cpu"
)

crossencoder = CrossEncoder("IIC/roberta-base-bne-ranker", device="cpu")

dataset = load_dataset("IIC/spanish_biomedical_crawled_corpus", split="train")

dataset = dataset.filter(lambda example: len(example["text"]) > minchars)

dataset.load_faiss_index(
    "embeddings",
    "dpr_index_bio_newdpr.faiss",
)


def query_index(question: str):
    question_embedding = dpr.embed_queries([question])[0]
    scores, closest_passages = dataset.get_nearest_examples(
        "embeddings", question_embedding, k=topk
    )
    contexts = [
        closest_passages["text"][i] for i in range(len(closest_passages["text"]))
    ]# [:int(topk / 3)]
    return [
        context for context in contexts if len(context.split()) > min_snippet_length
    ]


def sort_on_similarity(question, contexts, include_rank: int = 5):
    question_encoded = similarity_model.encode([question])[0]
    ctxs_encoded = similarity_model.encode(contexts)
    sim_scores_ss = [
         util.cos_sim(question_encoded, ctx_encoded) for ctx_encoded in ctxs_encoded
    ]
    text_pairs = [[question, ctx] for ctx in contexts]
    similarity_scores = crossencoder.predict(text_pairs)
    similarity_scores = np.array(sim_scores_ss) * similarity_scores
    similarity_ranking_idx = np.flip(np.argsort(similarity_scores))
    return [contexts[idx] for idx in similarity_ranking_idx][:include_rank]


def create_context(contexts: List):
    return "<p>" + "<p>".join(contexts)


def create_model_input(question: str, context: str):
    return f"question: {question} context: {context}"


def generate_answer(model_input, update_params):
    model_input = mt5_tokenizer(
        model_input, truncation=True, padding=True, return_tensors="pt", max_length=1024
    )
    params_generate.update(update_params)
    answers_encoded = mt5_lfqa.generate(
        input_ids=model_input["input_ids"].to(device),
        attention_mask=model_input["attention_mask"].to(device),
        **params_generate,
    )
    answers = mt5_tokenizer.batch_decode(
        answers_encoded, skip_special_tokens=True, clean_up_tokenization_spaces=True
    )
    results = [{"generated_text": answer} for answer in answers]
    return results


def search_and_answer(
    question,
    audio_file,
    audio_array,
    min_length_answer,
    num_beams,
    no_repeat_ngram_size,
    temperature,
    max_answer_length,
    do_tts,
):
    update_params = {
        "min_length": min_length_answer,
        "max_length": max_answer_length,
        "num_beams": int(num_beams),
        "temperature": temperature,
        "no_repeat_ngram_size": no_repeat_ngram_size,
    }
    if not question:
        s2t_model = models["wav2vec2-iic"]["model"]
        s2t_processor = models["wav2vec2-iic"]["processor"]
        question = transcript(
            audio_file, audio_array, processor=s2t_processor, model=s2t_model
        )
        print(f"Transcripted question: *** {question} ****")
    if any([any([term in word.lower() for term in covidterms]) for word in question.split(" ")]):
        return "Del COVID no queremos saber ya más nada, lo sentimos, pregúntame sobre otra cosa :P ", "ni contexto ni contexta.", "audio_troll.flac"
    contexts = query_index(question)
    contexts = sort_on_similarity(question, contexts)
    context = create_context(contexts)
    model_input = create_model_input(question, context)
    answers = generate_answer(model_input, update_params)
    final_answer = answers[0]["generated_text"]
    if do_tts:
        audio_answer = tts_es(remove_chars_to_tts(final_answer))
    final_answer, documents = parse_final_answer(final_answer, contexts)
    return final_answer, documents, audio_answer if do_tts else "audio_troll.flac"


if __name__ == "__main__":
    gr.Interface(
        search_and_answer,
        inputs=[
            gr.inputs.Textbox(
                lines=2,
                label="Pregúntame sobre BioMedicina o temas relacionados. Puedes simplemente preguntarme aquí y darle al botón verde de abajo que pone Enviar.",
                placeholder="Escribe aquí tu pregunta",
                optional=True,
            ),
            gr.inputs.Audio(
                source="upload",
                type="filepath",
                label="Sube un audio con tu respuesta aquí si quieres.",
                optional=True,
            ),
            gr.inputs.Audio(
                source="microphone",
                type="numpy",
                label="Graba aquí un audio con tu pregunta.",
                optional=True,
            ),
            gr.inputs.Slider(
                minimum=10,
                maximum=200,
                default=50,
                label="Minimum size for the answer",
                step=1,
            ),
            gr.inputs.Slider(
                minimum=4, maximum=12, default=8, label="number of beams", step=1
            ),
            gr.inputs.Slider(
                minimum=2, maximum=5, default=3, label="no repeat n-gram size", step=1
            ),
            gr.inputs.Slider(
                minimum=0.8, maximum=2.0, default=1.0, label="temperature", step=0.1
            ),
            gr.inputs.Slider(
                minimum=220,
                maximum=360,
                default=250,
                label="maximum answer length",
                step=1,
            ),
            gr.inputs.Checkbox(
                default=False, label="Text to Speech", optional=True),
        ],
        outputs=[
            gr.outputs.HTML(
                label="Respuesta generada."
            ),
            gr.outputs.HTML(
                label="Documentos utilizados."
            ),
            gr.outputs.Audio(label="Respuesta en audio."),
        ],
        description=description,
        examples=examples,
        theme="grass",
        article=article,
        thumbnail="IIC_logoP.png",
        css="https://cdn.jsdelivr.net/npm/bootstrap@3.3.7/dist/css/bootstrap.min.css",
    ).launch()