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Update appStore/keyword_search.py
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appStore/keyword_search.py
CHANGED
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@@ -8,24 +8,19 @@ import scripts.clean as clean
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#import needed libraries
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import seaborn as sns
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from pandas import DataFrame
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from sentence_transformers import SentenceTransformer, CrossEncoder, util
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# from keybert import KeyBERT
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from transformers import pipeline
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import matplotlib.pyplot as plt
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import numpy as np
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import streamlit as st
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import pandas as pd
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from rank_bm25 import BM25Okapi
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from sklearn.feature_extraction import _stop_words
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import string
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from tqdm.autonotebook import tqdm
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import numpy as np
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import tempfile
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import sqlite3
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#Haystack Components
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@st.cache(hash_funcs={"builtins.SwigPyObject": lambda _: None},allow_output_mutation=True)
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def start_haystack(temp.name, file):
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document_store = InMemoryDocumentStore()
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documents = pre.load_document(temp.name, file)
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@@ -36,7 +31,6 @@ def start_haystack(temp.name, file):
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pipeline = ExtractiveQAPipeline(reader, retriever)
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return pipeline
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def ask_question(question):
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prediction = pipeline.run(query=question, params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}})
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results = []
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#import needed libraries
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import seaborn as sns
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from pandas import DataFrame
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import matplotlib.pyplot as plt
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import numpy as np
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import streamlit as st
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import pandas as pd
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from sklearn.feature_extraction import _stop_words
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import string
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from tqdm.autonotebook import tqdm
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import numpy as np
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#Haystack Components
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@st.cache(hash_funcs={"builtins.SwigPyObject": lambda _: None},allow_output_mutation=True)
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+
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def start_haystack(temp.name, file):
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document_store = InMemoryDocumentStore()
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documents = pre.load_document(temp.name, file)
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pipeline = ExtractiveQAPipeline(reader, retriever)
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return pipeline
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def ask_question(question):
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prediction = pipeline.run(query=question, params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}})
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results = []
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