demo_dpr.py

import copy

import streamlit as st
import pandas as pd
from sentence_transformers import SentenceTransformer, util
from sentence_transformers.cross_encoder import CrossEncoder
from st_aggrid import GridOptionsBuilder, AgGrid
import pickle
import torch
from transformers import AutoTokenizer, AutoModel
from pathlib import Path
import base64
import io

st.set_page_config(layout="wide")

DATAFRAME_FILE_ORIGINAL = 'policyQA_original.csv'
DATAFRAME_FILE_BSBS = 'basecamp.csv'

selectbox_selections = {
    'Retrieve - Rerank (with fine-tuned cross-encoder)': 1,
    'Dense Passage Retrieval':2,
    'Retrieve - Rerank':4
}
imagebox_selections = {
    'Retrieve - Rerank (with fine-tuned cross-encoder)': 'Retrieve-rerank-trained-cross-encoder.png',
    'Dense Passage Retrieval': 'DPR_pipeline.png',
    'Retrieve - Rerank': 'retrieve-rerank.png'
}

def retrieve_rerank(question):
    # Semantic Search (Retrieve)
    question_embedding = bi_encoder.encode(question, convert_to_tensor=True)
    hits = util.semantic_search(question_embedding, context_embeddings, top_k=100)
    if len(hits) == 0:
        return []
    hits = hits[0]
    # Rerank - score all retrieved passages with cross-encoder
    cross_inp = [[question, contexes[hit['corpus_id']]] for hit in hits]
    cross_scores = cross_encoder.predict(cross_inp)

    # Sort results by the cross-encoder scores
    for idx in range(len(cross_scores)):
        hits[idx]['cross-score'] = cross_scores[idx]

    # Output of top-5 hits from re-ranker
    hits = sorted(hits, key=lambda x: x['cross-score'], reverse=True)
    top_5_contexes = []
    top_5_scores = []
    for hit in hits[0:20]:
        top_5_contexes.append(contexes[hit['corpus_id']])
        top_5_scores.append(hit['cross-score'])
    return top_5_contexes, top_5_scores

class CPU_Unpickler(pickle.Unpickler):
    def find_class(self, module, name):
        if module == 'torch.storage' and name == '_load_from_bytes':
            return lambda b: torch.load(io.BytesIO(b), map_location='cpu')
        else: return super().find_class(module, name)

@st.cache(show_spinner=False, allow_output_mutation=True)
def load_paragraphs(path):
    with open(path, "rb") as fIn:
        cache_data = pickle.load(fIn)
        corpus_sentences = cache_data['contexes']
        corpus_embeddings = cache_data['embeddings']

    return corpus_embeddings, corpus_sentences

@st.cache(show_spinner=False)
def load_dataframes():
 #   data_original = pd.read_csv(DATAFRAME_FILE_ORIGINAL, index_col=0, sep='|')
    data_bsbs = pd.read_csv(DATAFRAME_FILE_BSBS, index_col=0, sep='|')
    data_bsbs.drop('context_id', axis=1, inplace=True)
#    data_original = data_original.sample(frac=1).reset_index(drop=True)
    data_bsbs = data_bsbs.sample(frac=1).reset_index(drop=True)
    return data_bsbs

def dot_product(question_output, context_output):
    mat1 = torch.squeeze(question_output, 0)
    mat2 = torch.squeeze(context_output, 0)
    result = torch.dot(mat1, mat2)
    return result

def search_pipeline(question, search_method):
    if search_method == 1: #Retrieve - rerank with fine-tuned cross encoder
        return retrieve_rerank_with_trained_cross_encoder(question)
    if search_method == 2:
        return custom_dpr_pipeline(question) # DPR only
    if search_method == 4:
        return retrieve_rerank(question)

def mean_pooling(token_embeddings, mask):
    token_embeddings = token_embeddings.masked_fill(~mask[..., None].bool(), 0.)
    sentence_embeddings = token_embeddings.sum(dim=1) / mask.sum(dim=1)[..., None]
    return sentence_embeddings

def custom_dpr_pipeline(question):
    #paragraphs
    tokenized_question = question_tokenizer(question, padding=True, truncation=True, return_tensors="pt")
    question_embedding = dpr_trained.model.question_model(**tokenized_question)
    question_embedding = mean_pooling(question_embedding[0], tokenized_question['attention_mask'])
  #  question_embedding = question_embedding['pooler_output']
    results_list = []
    for i,context_embedding in enumerate(dpr_context_embeddings):
        score = dot_product(question_embedding, context_embedding)
        results_list.append(score.detach().cpu())

    hits = sorted(range(len(results_list)), key=lambda i: results_list[i], reverse=True)
    top_5_contexes = []
    top_5_scores = []
    for j in hits[0:5]:
        top_5_contexes.append(dpr_contexes[j])
        top_5_scores.append(results_list[j])
    return top_5_contexes, top_5_scores

def retrieve(question):
    # Semantic Search (Retrieve)
    question_embedding = bi_encoder.encode(question, convert_to_tensor=True)
    hits = util.semantic_search(question_embedding, context_embeddings, top_k=100)
    if len(hits) == 0:
        return []
    hits = hits[0]
    return hits

def retrieve_rerank_with_trained_cross_encoder(question):
    hits = retrieve(question)
    cross_inp = [(question, contexes[hit['corpus_id']]) for hit in hits]
    cross_scores = trained_cross_encoder.predict(cross_inp)
    # Sort results by the cross-encoder scores
    for idx in range(len(cross_scores)):
        hits[idx]['cross-score'] = cross_scores[idx]

    # Output of top-5 hits from re-ranker
    hits = sorted(hits, key=lambda x: x['cross-score'], reverse=True)
    top_5_contexes = []
    top_5_scores = []
    for hit in hits[0:5]:
        top_5_contexes.append(contexes[hit['corpus_id']])
        top_5_scores.append(hit['cross-score'])
    return top_5_contexes, top_5_scores


def interactive_table(dataframe):
    gb = GridOptionsBuilder.from_dataframe(dataframe)
    gb.configure_pagination(paginationAutoPageSize=True)
    gb.configure_side_bar()
    gb.configure_selection('single', rowMultiSelectWithClick=True,
                           groupSelectsChildren="Group checkbox select children")  # Enable multi-row selection
    gridOptions = gb.build()
    grid_response = AgGrid(
        dataframe,
        gridOptions=gridOptions,
        data_return_mode='AS_INPUT',
        update_mode='SELECTION_CHANGED',
        enable_enterprise_modules=False,
        fit_columns_on_grid_load=False,
        theme='streamlit',  # Add theme color to the table
        height=350,
        width='100%',
        reload_data=False
    )
    return grid_response

def img_to_bytes(img_path):
    img_bytes = Path(img_path).read_bytes()
    encoded = base64.b64encode(img_bytes).decode()
    return encoded

def qa_main_widgetsv2():
    st.title("Question Answering Demo")
    st.markdown("""---""")
    option = st.selectbox("Select a search method:", list(selectbox_selections.keys()))
    header_html = "<center> <img src='data:image/png;base64,{}' class='img-fluid' width='60%', height='40%'> </center>".format(
        img_to_bytes(imagebox_selections[option])
    )
    st.markdown(
        header_html, unsafe_allow_html=True,
    )
    st.markdown("""---""")
    col1, col3 = st.columns([1, 1])
    with col1:
        form = st.form(key='first_form')
        question = form.text_area("What is your question?:", height=200)
        submit = form.form_submit_button('Submit')
        if "form_submit" not in st.session_state:
            st.session_state.form_submit = False
        if submit:
            st.session_state.form_submit = True
        if st.session_state.form_submit and question != '':
            with st.spinner(text='Related context search in progress..'):
                top_5_contexes, top_5_scores = search_pipeline(question.strip(), selectbox_selections[option])
            if len(top_5_contexes) == 0:
                st.error("Related context not found!")
                st.session_state.form_submit = False
            else:
                for i, context in enumerate(top_5_contexes):
                    st.markdown(f"## Related Context - {i + 1} (score: {top_5_scores[i]:.2f})")
                    st.markdown(context)
                    st.markdown("""---""")
    with col3:
        st.markdown("## Our Questions")
        grid_response = interactive_table(dataframe_bsbs)
        data2 = grid_response['selected_rows']
        if "grid_click_2" not in st.session_state:
            st.session_state.grid_click_2 = False
        if len(data2) > 0:
            st.session_state.grid_click_2 = True
        if st.session_state.grid_click_2:
            selection = data2[0]
            #   st.markdown("## Context & Answer:")
            st.markdown("### Context:")
            st.write(selection['context'])
            st.markdown("### Question:")
            st.write(selection['question'])
            st.markdown("### Answer:")
            st.write(selection['answer'])
            st.session_state.grid_click_2 = False

@st.cache(show_spinner=False, allow_output_mutation = True)
def load_models(dpr_model_path, auth_token, cross_encoder_model_path):

    dpr_trained = AutoModel.from_pretrained(dpr_model_path, use_auth_token=auth_token,
                                            trust_remote_code=True)
    bi_encoder = SentenceTransformer('multi-qa-MiniLM-L6-cos-v1')
    cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
    bi_encoder.max_seq_length = 500
    trained_cross_encoder = CrossEncoder(cross_encoder_model_path)
    question_tokenizer = AutoTokenizer.from_pretrained('facebook/contriever-msmarco')
    return dpr_trained, bi_encoder, cross_encoder, trained_cross_encoder, question_tokenizer

context_embeddings, contexes = load_paragraphs('st-context-embeddings.pkl')
dpr_context_embeddings, dpr_contexes = load_paragraphs('basecamp-dpr-contriever-embeddings.pkl')
dataframe_bsbs = load_dataframes()
dpr_trained, bi_encoder, cross_encoder, trained_cross_encoder, question_tokenizer = copy.deepcopy(load_models(st.secrets["DPR_MODEL_PATH"], st.secrets["AUTH_TOKEN"], st.secrets["CROSS_ENCODER_MODEL_PATH"]))
qa_main_widgetsv2()

#if __name__ == '__main__':
#    top_5_contexes, top_5_scores = search_pipeline('What are the benefits of 37Signals Visa Card?', 1)