Create app.py

app.py

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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"  # You 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()