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2dbee83 c123d6e 2dbee83 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | import streamlit as st
import pickle
from sentence_transformers import CrossEncoder
with open('eiga_docs.pkl', 'rb') as file:
documents=pickle.load( file)
#with open('index.pkl', 'rb') as file:
# index=pickle.load( file)
#documents =[ i["title"]+" "+i["text"] for i in documentt]
#bi_encoder = SentenceTransformer('all-mpnet-base-v2',device="cpu",)
# Load a cross-encoder model for re-ranking
cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
# Streamlit app
#st.title("Information Retrieval System")
#query = st.text_input("Enter your query:")
#if query:
# Encode the query using the bi-encoder
#query_embedding = bi_encoder.encode([query])
# Retrieve top-k documents using FAISS
#k = 30 # Number of documents to retrieve
#D, I = index.search(query_embedding, k)
#Collect the retrieved documents
#retrieved_docs = [documents[i] for i in I[0]]
#Re-rank the retrieved documents using the cross-encoder
#cross_inp = [[query, doc["title"]+" "+doc["text"]] for doc in documents]
#scores = cross_encoder.predict(cross_inp)
# Sort the documents by score
#sorted_docs = [documents[doc] for _, doc in sorted(zip(scores, range(len(documents))), reverse=True)][:10]
# Display the results
#st.write("Top documents:")
#for i, doc in enumerate(sorted_docs):
# st.write(f"Result {i+1}. Title: {doc['title']}")
# st.write(f"Text : {doc['text']}")
# st.write(f"Lien de telechargment : {doc['doc_url']}")
# st.markdown("""---""")
# Function to display results for a given page
def display_results(sorted_docs, page_number, results_per_page):
start_index = (page_number - 1) * results_per_page
end_index = min(page_number * results_per_page, len(sorted_docs))
for i, doc in enumerate(sorted_docs[start_index:end_index], start=start_index):
st.write(f"**Result {i+1}.** Title: {doc['title']}")
st.write(f"Text : {doc['text']}")
st.write(f"Link : {doc['doc_url']}")
st.markdown("""---""")
# Function to perform search and return sorted documents
def perform_search(query):
if query:
cross_inp = [[query, doc["title"] + " " + doc["text"]] for doc in documents]
scores = cross_encoder.predict(cross_inp)
# Sort the documents by score
sorted_docs = [documents[doc] for _, doc in sorted(zip(scores, range(len(documents))), reverse=True)]
return sorted_docs
else:
return []
# Main Streamlit app
def main():
st.sidebar.title("Pagination")
results_per_page = st.sidebar.number_input("Results per page", min_value=1, max_value=10, value=5)
total_pages = (len(documents) + results_per_page - 1) // results_per_page
page_number = st.sidebar.number_input("Page Number", min_value=1, max_value=total_pages, value=1)
st.sidebar.write(f"Current Page Number: {page_number}")
st.sidebar.write( f"Totale Pages :{total_pages}")
#print(results_per_page,total_pages,page_number)
st.title("Information Retrieval System")
query = st.text_input("Enter your question:")
if st.button("Search") or query:
sorted_docs = perform_search(query)
st.session_state.sorted_docs = sorted_docs
else:
sorted_docs = st.session_state.get("sorted_docs", [])
if sorted_docs:
display_results(st.session_state.sorted_docs, page_number, results_per_page)
st.write(f"Current Page Number: {page_number}")
# Run the app
if __name__ == "__main__":
main() |