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import os
import fitz # PyMuPDF for parsing PDF
import streamlit as st
from sentence_transformers import SentenceTransformer, util
# Load a pre-trained SentenceTransformer model
model_name = "paraphrase-MiniLM-L6-v2" # Can change this to a different model if needed
model = SentenceTransformer(model_name)
# Function to extract text from a PDF file
def extract_text_from_pdf(pdf_path):
text = ""
with fitz.open(pdf_path) as pdf_document:
for page_num in range(pdf_document.page_count):
page = pdf_document.load_page(page_num)
text += page.get_text()
return text
# Function to perform semantic search
def semantic_search(query, documents, top_k=5):
query_embedding = model.encode(query, convert_to_tensor=True)
# Convert the list of documents to embeddings
document_embeddings = model.encode(documents, convert_to_tensor=True)
# Compute cosine similarity scores of query with documents
cosine_scores = util.pytorch_cos_sim(query_embedding, document_embeddings)
# Sort the results in decreasing order
results = []
for idx in range(len(cosine_scores)):
results.append((documents[idx], cosine_scores[idx].item()))
results = sorted(results, key=lambda x: x[1], reverse=True)
return results[:top_k]
def main():
st.title("Semantic Search on PDF Documents")
query = st.text_input("Enter your query:")
pdf_file = st.file_uploader("Upload a PDF file:", type=["pdf"])
if st.button("Search"):
if pdf_file:
pdf_path = os.path.join("uploads", pdf_file.name)
with open(pdf_path, "wb") as f:
f.write(pdf_file.read())
pdf_text = extract_text_from_pdf(pdf_path)
search_results = semantic_search(query, [pdf_text])
os.remove(pdf_path) # Delete the uploaded file after processing
st.write(f"Search results for query: '{query}'")
for i, (result, score) in enumerate(search_results, start=1):
st.write(f"{i}. Score: {score:.2f}")
st.write(result)
if __name__ == "__main__":
main()