Spaces:
Runtime error
Runtime error
| import streamlit as st | |
| import os | |
| from haystack import Pipeline | |
| from haystack.utils import fetch_archive_from_http, clean_wiki_text, convert_files_to_docs | |
| from haystack.schema import Answer | |
| from haystack.document_stores import InMemoryDocumentStore | |
| from haystack.pipelines import DocumentSearchPipeline, ExtractiveQAPipeline, GenerativeQAPipeline | |
| from haystack.nodes import (DensePassageRetriever, EmbeddingRetriever, FARMReader, | |
| OpenAIAnswerGenerator, Seq2SeqGenerator, | |
| TfidfRetriever) | |
| from haystack.nodes import RAGenerator | |
| import logging | |
| from markdown import markdown | |
| from annotated_text import annotation | |
| from PIL import Image | |
| logging.basicConfig(format="%(levelname)s - %(name)s - %(message)s", level=logging.WARNING) | |
| logging.getLogger("haystack").setLevel(logging.INFO) | |
| os.environ['TOKENIZERS_PARALLELISM'] = "false" | |
| MY_API_KEY = os.environ.get("MY_API_KEY") | |
| # Haystack Components | |
| # @st.cache_data | |
| def start_haystack(): | |
| # document_store = InMemoryDocumentStore() | |
| # For dense retriever | |
| document_store = InMemoryDocumentStore(embedding_dim=128) | |
| # For OPEN AI retriever | |
| # document_store = InMemoryDocumentStore(embedding_dim=1024) | |
| load_and_write_data(document_store) | |
| # retriever = TfidfRetriever(document_store=document_store) | |
| retriever = DensePassageRetriever( | |
| document_store=document_store, | |
| query_embedding_model="vblagoje/dpr-question_encoder-single-lfqa-wiki", | |
| passage_embedding_model="vblagoje/dpr-ctx_encoder-single-lfqa-wiki", | |
| ) | |
| # retriever = EmbeddingRetriever( | |
| # document_store=document_store, | |
| # embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1", | |
| # model_format="sentence_transformers", | |
| # ) | |
| document_store.update_embeddings(retriever) | |
| # OPEN AI | |
| # retriever = EmbeddingRetriever( | |
| # document_store=document_store, | |
| # batch_size=8, | |
| # embedding_model="ada", | |
| # api_key=MY_API_KEY, | |
| # max_seq_len=1024 | |
| # ) | |
| # document_store.update_embeddings(retriever) | |
| # reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True) | |
| # pipeline = ExtractiveQAPipeline(reader, retriever) | |
| generator = Seq2SeqGenerator(model_name_or_path="vblagoje/bart_lfqa") | |
| # generator = OpenAIAnswerGenerator( | |
| # api_key=MY_API_KEY, | |
| # model="text-davinci-003", | |
| # max_tokens=50, | |
| # presence_penalty=0.1, | |
| # frequency_penalty=0.1, | |
| # top_k=3, | |
| # temperature=0.9 | |
| # ) | |
| # pipe.add_node(component=retriever, name="Retriever", inputs=["Query"]) | |
| # pipe.add_node(component=generator, name="prompt_node", inputs=["Query"]) | |
| pipe = GenerativeQAPipeline(generator=generator, retriever=retriever) | |
| return pipe | |
| def load_and_write_data(document_store): | |
| doc_dir = './dao_data' | |
| docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, | |
| split_paragraphs=True) | |
| document_store.write_documents(docs) | |
| pipeline = start_haystack() | |
| def set_state_if_absent(key, value): | |
| if key not in st.session_state: | |
| st.session_state[key] = value | |
| set_state_if_absent("question", "What is the goal of VitaDAO?") | |
| set_state_if_absent("results", None) | |
| def reset_results(*args): | |
| st.session_state.results = None | |
| # Streamlit App | |
| image = Image.open('got-haystack.png') | |
| st.image(image) | |
| st.markdown(""" | |
| This QA demo uses a [Haystack Extractive QA Pipeline]( | |
| https://haystack.deepset.ai/components/ready-made-pipelines#extractiveqapipeline) with | |
| an [InMemoryDocumentStore](https://haystack.deepset.ai/components/document-store) which contains | |
| documents about Game of Thrones 👑 | |
| Go ahead and ask questions about the marvellous kingdom! | |
| """, unsafe_allow_html=True) | |
| question = st.text_input("", value=st.session_state.question, max_chars=100, | |
| on_change=reset_results) | |
| def ask_question(question): | |
| # prediction = pipeline.run(query=question, params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}}) | |
| prediction = pipeline.run(query=question, params={"Retriever": {"top_k": 10}, "Generator": {"top_k": 1}}) | |
| results = [] | |
| for answer in prediction["answers"]: | |
| answer = answer.to_dict() | |
| if answer["answer"]: | |
| print(answer) | |
| results.append( | |
| { | |
| "context": "..." + str(answer["context"]) + "...", | |
| "answer": answer["answer"], | |
| # "relevance": round(answer["score"] * 100, 2), | |
| # "offset_start_in_doc": answer["offsets_in_document"][0]["start"], | |
| } | |
| ) | |
| else: | |
| results.append( | |
| { | |
| "context": None, | |
| "answer": None, | |
| # "relevance": round(answer["score"] * 100, 2), | |
| } | |
| ) | |
| return results | |
| if question: | |
| with st.spinner("👑 Performing semantic search on royal scripts..."): | |
| try: | |
| msg = 'Asked ' + question | |
| logging.info(msg) | |
| st.session_state.results = ask_question(question) | |
| except Exception as e: | |
| logging.exception(e) | |
| if st.session_state.results: | |
| st.write('## Top Results') | |
| for count, result in enumerate(st.session_state.results): | |
| if result["answer"]: | |
| answer, context = result["answer"], result["context"] | |
| start_idx = context.find(answer) | |
| end_idx = start_idx + len(answer) | |
| st.write( | |
| markdown(context[:start_idx] + str( | |
| annotation(body=answer, label="ANSWER", background="#964448", | |
| color='#ffffff')) + context[end_idx:]), | |
| unsafe_allow_html=True, | |
| ) | |
| # st.markdown(f"**Relevance:** {result['relevance']}") | |
| else: | |
| st.info( | |
| "🤔 Haystack is unsure whether any of the documents contain an " | |
| "answer to your question. Try to reformulate it!" | |
| ) | |