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from haystack.document_stores import InMemoryDocumentStore
import pandas as pd
import gradio as gr
df=pd.read_parquet('df.parquet')
candidats=pd.read_parquet('candidats.parquet')
document_store = InMemoryDocumentStore(use_bm25=True)
docs=df.drop_duplicates(subset=['fileclean']).rename(columns={'fileclean':'content'}).to_dict(orient='records')
document_store.write_documents(docs)
from haystack.nodes import BM25Retriever
retriever = BM25Retriever(document_store=document_store)
from haystack.pipelines import DocumentSearchPipeline
pipeline = DocumentSearchPipeline(retriever=retriever)
def semanticsearch(query):
result = pipeline.run(
query=query,
params={
"Retriever": {
"top_k": 10
}
},debug=False
)
results=[]
for document in result['documents']:
result=document.to_dict()
for c in ['content_type','embedding','id']:
result.pop(c)
results.append(result)
results=pd.DataFrame(results)
return results
demo = gr.Interface(
semanticsearch,
[
gr.Dropdown(candidats.sort_values(by='text').text.tolist()),
],
[gr.Dataframe()]
)
if __name__ == "__main__":
demo.launch() |