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()