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b06f609
1
Parent(s):
09ef786
Update app.py
Browse files
app.py
CHANGED
@@ -22,7 +22,7 @@ def semantic_search(query, documents, top_k=5):
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query_embedding = model.encode(query, convert_to_tensor=True)
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# Convert the list of documents to embeddings
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document_embeddings = model.encode(documents, convert_to_tensor=True)
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# Compute cosine similarity scores of query with documents
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cosine_scores = util.pytorch_cos_sim(query_embedding, document_embeddings)
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@@ -38,8 +38,8 @@ def semantic_search(query, documents, top_k=5):
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def main():
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st.title("Semantic Search on PDF Documents")
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query = st.text_input("Enter your query:")
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pdf_file = st.file_uploader("Upload a PDF file:", type=["pdf"])
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if st.button("Search"):
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if pdf_file:
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@@ -50,10 +50,7 @@ def main():
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# Extract text from the PDF along with page numbers
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pdf_text_with_pages = list(extract_text_from_pdf(pdf_path))
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pdf_text = [text for _, text in pdf_text_with_pages]
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search_results = semantic_search(query, pdf_text)
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os.remove(pdf_path) # Delete the uploaded file after processing
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st.write(f"Search results for query: '{query}'")
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@@ -64,4 +61,4 @@ def main():
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st.write(result_text)
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if __name__ == "__main__":
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main()
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query_embedding = model.encode(query, convert_to_tensor=True)
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# Convert the list of documents to embeddings
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document_embeddings = model.encode([text for _, text in documents], convert_to_tensor=True)
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# Compute cosine similarity scores of query with documents
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cosine_scores = util.pytorch_cos_sim(query_embedding, document_embeddings)
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def main():
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st.title("Semantic Search on PDF Documents")
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pdf_file = st.file_uploader("Upload a PDF file:", type=["pdf"])
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query = st.text_input("Enter your query:")
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if st.button("Search"):
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if pdf_file:
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# Extract text from the PDF along with page numbers
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pdf_text_with_pages = list(extract_text_from_pdf(pdf_path))
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search_results = semantic_search(query, pdf_text_with_pages)
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os.remove(pdf_path) # Delete the uploaded file after processing
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st.write(f"Search results for query: '{query}'")
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st.write(result_text)
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if __name__ == "__main__":
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main()
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