Delete demov2.py

demov2.py

@@ -1,304 +0,0 @@
-import copy
-import streamlit as st
-import json
-import pandas as pd
-import tokenizers
-from sentence_transformers import SentenceTransformer, CrossEncoder, util
-from transformers import pipeline
-from st_aggrid import GridOptionsBuilder, AgGrid
-import pickle
-import torch
-from transformers import RobertaTokenizer, RobertaForSequenceClassification
-import spacy
-import regex
-from typing import List
-from torch.autograd import Variable
-
-st.set_page_config(layout="wide")
-
-DATAFRAME_FILE_ORIGINAL = 'policyQA_original.csv'
-DATAFRAME_FILE_BSBS = 'policyQA_bsbs_sentence.csv'
-
-
-@st.experimental_singleton(suppress_st_warning=True, show_spinner=False)
-def cross_encoder_init():
-    cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
-    return cross_encoder
-
-
-@st.experimental_singleton(suppress_st_warning=True, show_spinner=False)
-def bi_encoder_init():
-    bi_encoder = SentenceTransformer('multi-qa-MiniLM-L6-cos-v1')
-    bi_encoder.max_seq_length = 500  # Truncate long passages to 256 tokens
-    return bi_encoder
-
-
-@st.experimental_singleton(suppress_st_warning=True, show_spinner=False)
-def nlp_init(auth_token, private_model_name):
-    return pipeline('question-answering', model=private_model_name, tokenizer=private_model_name,
-                    use_auth_token=auth_token,
-                    revision="main")
-
-
-@st.experimental_singleton(suppress_st_warning=True, show_spinner=False)
-def nlp_pipeline_hf():
-    model_name = "deepset/roberta-base-squad2"
-    return pipeline('question-answering', model=model_name, tokenizer=model_name)
-
-
-@st.experimental_singleton(suppress_st_warning=True, show_spinner=False)
-def nlp_pipeline_sentence_based(auth_token, private_model_name):
-    tokenizer = RobertaTokenizer.from_pretrained(private_model_name, use_auth_token=auth_token)
-    model = RobertaForSequenceClassification.from_pretrained(private_model_name, use_auth_token=auth_token)
-    return tokenizer, model
-
-
-@st.cache(hash_funcs={tokenizers.Tokenizer: lambda _: None, tokenizers.AddedToken: lambda _: None,
-                      regex.Pattern: lambda _: None}, show_spinner=False)
-def load_models_sentence_based(auth_token, private_model_name, private_model_name_base):
-    bi_encoder = bi_encoder_init()
-    cross_encoder = cross_encoder_init()
-    # OLD MODEL
-    # nlp = nlp_init(auth_token, private_model_name)
-    # nlp_hf = nlp_pipeline_hf()
-    policy_qa_tokenizer, policy_qa_model = nlp_pipeline_sentence_based(auth_token, private_model_name)
-    asnq_tokenizer, asnq_model = nlp_pipeline_sentence_based(auth_token, private_model_name_base)
-
-    return bi_encoder, cross_encoder, policy_qa_tokenizer, policy_qa_model, asnq_tokenizer, asnq_model
-
-
-@st.cache(hash_funcs={tokenizers.Tokenizer: lambda _: None, tokenizers.AddedToken: lambda _: None}, show_spinner=False)
-def load_models(auth_token, private_model_name):
-    bi_encoder = bi_encoder_init()
-    cross_encoder = cross_encoder_init()
-    nlp = nlp_init(auth_token, private_model_name)
-    nlp_hf = nlp_pipeline_hf()
-
-    return bi_encoder, cross_encoder, nlp, nlp_hf
-
-
-def context():
-    bi_encoder = SentenceTransformer('multi-qa-MiniLM-L6-cos-v1', device='cpu')
-    with open("/home/secilsen/PycharmProjects/SquadOperations/contexes.json", 'r', encoding='utf-8') as f:
-        paragraphs = json.load(f)
-        paragraphs = paragraphs['contexes']
-    with open('context-embeddings.pkl', "wb") as fIn:
-        context_embeddings = bi_encoder.encode(paragraphs, convert_to_tensor=True, show_progress_bar=True)
-        pickle.dump({'contexes': paragraphs, 'embeddings': context_embeddings}, fIn)
-
-
-@st.cache(show_spinner=False)
-def load_paragraphs():
-    with open('context-embeddings.pkl', "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_original = data_original.sample(frac=1).reset_index(drop=True)
-    data_bsbs = data_bsbs.sample(frac=1).reset_index(drop=True)
-    return data_original, data_bsbs
-
-
-def search(question, corpus_embeddings, contexes, bi_encoder, cross_encoder):
-    # Semantic Search (Retrieve)
-    question_embedding = bi_encoder.encode(question, convert_to_tensor=True)
-    hits = util.semantic_search(question_embedding, corpus_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
-
-
-def paragraph_embeddings():
-    paragraphs = load_paragraphs()
-    context_embeddings = bi_encoder.encode(paragraphs, convert_to_tensor=True, show_progress_bar=True)
-    return context_embeddings, paragraphs
-
-
-def retrieve_rerank_pipeline(question, context_embeddings, paragraphs, bi_encoder, cross_encoder):
-    top_5_contexes, top_5_scores = search(question, context_embeddings, paragraphs, bi_encoder, cross_encoder)
-    return top_5_contexes, top_5_scores
-
-
-def qa_pipeline(question, context, nlp):
-    return nlp({'question': question.strip(), 'context': context})
-
-
-def qa_pipeline_sentence(question, context, model, tokenizer):
-    sentences_doc = spacy_nlp(context)
-    candidate_sentences = []
-    for sentence in sentences_doc.sents:
-        tokenized = tokenizer(f"<s> {question} </s> {sentence.text} </s>", padding=True, truncation=True, return_tensors='pt')
-        output = model(**tokenized)
-        soft_outputs = torch.nn.functional.sigmoid(output[0])
-        t = Variable(torch.Tensor([0.2]))  # threshold
-        out = (soft_outputs[0] > t) * 1
-        out = out.flatten().cpu().detach().numpy()
-     #   res = torch.argmax(out, dim=-1)
-        print(out[1])
-        if out[1] == 1:
-            prob = soft_outputs[:, 1].flatten().cpu().detach().numpy()
-            candidate_sentences.append(dict(sentence=sentence,
-                                            prob=prob[0]))
-    print(candidate_sentences)
-    candidate_sentences = sorted(candidate_sentences, key=lambda x: x['prob'], reverse=True)
-    return candidate_sentences
-
-
-def candidate_sentence_controller(sentences):
-    if sentences is None or len(sentences) == 0:
-        return ""
-    if len(sentences) == 1:
-        return sentences[0]
-    return sentences
-
-
-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 qa_main_widgetsv2():
-    st.title("Question Answering Demo")
-    col1, col2, col3 = st.columns([2, 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 = retrieve_rerank_pipeline(question.strip(), context_embeddings,
-                                                                        paragraphs, bi_encoder,
-                                                                        cross_encoder)
-            if len(top_5_contexes) == 0:
-                st.error("Related context not found!")
-                st.session_state.form_submit = False
-            else:
-                with st.spinner(text='Now answering your question..'):
-                    for i, context in enumerate(top_5_contexes):
-                        #  answer_trained = qa_pipeline(question, context, nlp)
-                        # answer_base = qa_pipeline(question, context, nlp_hf)
-                        answer_trained = qa_pipeline_sentence(question, context, policy_qa_model, policy_qa_tokenizer)
-                        answer_base = qa_pipeline_sentence(question, context, asnq_model, asnq_tokenizer)
-                        st.markdown(f"## Related Context - {i + 1} (score: {top_5_scores[i]:.2f})")
-                        st.markdown(context)
-                        st.markdown("## Answer (trained):")
-                        if answer_trained is None:
-                            st.markdown("")
-                        elif isinstance(answer_trained, List):
-                            for i,answer in enumerate(answer_trained):
-                                st.markdown(f"### Answer Option {i+1} with prob. {answer['prob']:.4f}")
-                                st.markdown(answer['sentence'])
-                        else:
-                            st.markdown(answer_trained)
-                        # st.markdown(answer_trained['answer'])
-                        st.markdown("## Answer (roberta-base-asnq):")
-                        if answer_base is None:
-                            st.markdown("")
-                        elif isinstance(answer_base, List):
-                            for i,answer in enumerate(answer_base):
-                                st.markdown(f"### Answer Option {i + 1} with prob. {answer['prob']:.4f}")
-                                st.markdown(answer['sentence'])
-                        else:
-                            st.markdown(answer_base)
-                        st.markdown("""---""")
-
-    with col2:
-        st.markdown("## Original Questions")
-        grid_response = interactive_table(dataframe_original)
-        data1 = grid_response['selected_rows']
-        if "grid_click_1" not in st.session_state:
-            st.session_state.grid_click_1 = False
-        if len(data1) > 0:
-            st.session_state.grid_click_1 = True
-        if st.session_state.grid_click_1:
-            selection = data1[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_1 = False
-    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
-
-
-def load():
-    context_embeddings, paragraphs = load_paragraphs()
-    dataframe_original, dataframe_bsbs = load_dataframes()
-    spacy_nlp = spacy.load('en_core_web_sm')
-    # bi_encoder, cross_encoder, nlp, nlp_hf = copy.deepcopy(load(st.secrets["AUTH_TOKEN"], st.secrets["MODEL_NAME"]))
-    bi_encoder, cross_encoder, policy_qa_tokenizer, policy_qa_model, asnq_tokenizer, asnq_model \
-        = copy.deepcopy(
-        load_models_sentence_based(st.secrets["AUTH_TOKEN"], st.secrets["MODEL_NAME"], st.secrets["MODEL_NAME_BASE"]))
-    return context_embeddings, paragraphs, dataframe_original, dataframe_bsbs, bi_encoder, cross_encoder, policy_qa_tokenizer, policy_qa_model, asnq_tokenizer, asnq_model, spacy_nlp
-
-
-#   save_dataframe()
-# context_embeddings, paragraphs, dataframe_original, dataframe_bsbs, bi_encoder, cross_encoder, nlp, nlp_hf = load()
-context_embeddings, paragraphs, dataframe_original, dataframe_bsbs, bi_encoder, cross_encoder, policy_qa_tokenizer, policy_qa_model, asnq_tokenizer, asnq_model, spacy_nlp = load()
-qa_main_widgetsv2()
-
-# if __name__ == '__main__':
-#    context()