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| import streamlit as st | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| from sentence_transformers import SentenceTransformer, util | |
| import PyPDF2 | |
| from docx import Document | |
| import numpy as np | |
| # Load the tokenizer and model for sentence embeddings | |
| def load_model(): | |
| try: | |
| tokenizer = AutoTokenizer.from_pretrained("rakeshkiriyath/gpt2Medium_text_to_sql") | |
| model = AutoModelForCausalLM.from_pretrained("rakeshkiriyath/gpt2Medium_text_to_sql") | |
| sentence_model = SentenceTransformer('all-MiniLM-L6-v2') # Smaller, faster sentence embeddings model | |
| st.success("Model loaded successfully!") | |
| return tokenizer, model, sentence_model | |
| except Exception as e: | |
| st.error(f"Error loading models: {e}") | |
| return None, None, None | |
| # Extract text from a PDF file | |
| def extract_text_from_pdf(pdf_file): | |
| try: | |
| pdf_reader = PyPDF2.PdfReader(pdf_file) | |
| text = "" | |
| for page in pdf_reader.pages: | |
| text += page.extract_text() | |
| return text | |
| except Exception as e: | |
| st.error(f"Error reading PDF: {e}") | |
| return "" | |
| # Extract text from a Word document | |
| def extract_text_from_word(docx_file): | |
| try: | |
| doc = Document(docx_file) | |
| text = "" | |
| for paragraph in doc.paragraphs: | |
| text += paragraph.text + "\n" | |
| return text | |
| except Exception as e: | |
| st.error(f"Error reading Word document: {e}") | |
| return "" | |
| # Optimized comparison using embeddings and matrix operations | |
| def compare_sentences(doc1_sentences, doc2_sentences, sentence_model): | |
| # Encode all sentences in batches to get embeddings | |
| doc1_embeddings = sentence_model.encode(doc1_sentences, convert_to_tensor=True, batch_size=16) | |
| doc2_embeddings = sentence_model.encode(doc2_sentences, convert_to_tensor=True, batch_size=16) | |
| # Compute cosine similarity matrix between all pairs | |
| similarity_matrix = util.pytorch_cos_sim(doc1_embeddings, doc2_embeddings) | |
| # Extract pairs with similarity > threshold | |
| threshold = 0.6 # Adjust this for stricter or looser matching | |
| similar_sentences = [] | |
| for i, row in enumerate(similarity_matrix): | |
| for j, score in enumerate(row): | |
| if score >= threshold: | |
| similar_sentences.append((i, j, score.item(), doc1_sentences[i], doc2_sentences[j])) | |
| return similar_sentences | |
| # Streamlit UI | |
| def main(): | |
| st.title("Optimized Comparative Analysis of Two Documents") | |
| st.sidebar.header("Upload Files") | |
| # Upload files | |
| uploaded_file1 = st.sidebar.file_uploader("Upload the First Document (PDF/Word)", type=["pdf", "docx"]) | |
| uploaded_file2 = st.sidebar.file_uploader("Upload the Second Document (PDF/Word)", type=["pdf", "docx"]) | |
| if uploaded_file1 and uploaded_file2: | |
| # Extract text from the uploaded documents | |
| if uploaded_file1.name.endswith(".pdf"): | |
| text1 = extract_text_from_pdf(uploaded_file1) | |
| else: | |
| text1 = extract_text_from_word(uploaded_file1) | |
| if uploaded_file2.name.endswith(".pdf"): | |
| text2 = extract_text_from_pdf(uploaded_file2) | |
| else: | |
| text2 = extract_text_from_word(uploaded_file2) | |
| if not text1.strip(): | |
| st.error("The first document is empty or could not be read.") | |
| return | |
| if not text2.strip(): | |
| st.error("The second document is empty or could not be read.") | |
| return | |
| st.write("### Preview of Document 1:") | |
| st.text(text1[:500]) # Display a preview of Document 1 | |
| st.write("### Preview of Document 2:") | |
| st.text(text2[:500]) # Display a preview of Document 2 | |
| # Split text into sentences | |
| doc1_sentences = text1.split('. ') | |
| doc2_sentences = text2.split('. ') | |
| # Limit sentences for testing purposes (optional) | |
| doc1_sentences = doc1_sentences[:50] # Remove this line for full processing | |
| doc2_sentences = doc2_sentences[:50] # Remove this line for full processing | |
| # Load models | |
| tokenizer, model, sentence_model = load_model() | |
| if not sentence_model: | |
| st.error("Failed to load the sentence embedding model.") | |
| return | |
| # Perform sentence comparison | |
| st.info("Comparing sentences, this may take a moment...") | |
| similar_sentences = compare_sentences(doc1_sentences, doc2_sentences, sentence_model) | |
| # Display results | |
| st.header("Comparative Analysis Results") | |
| st.write(f"Number of sentences in Document 1: {len(doc1_sentences)}") | |
| st.write(f"Number of sentences in Document 2: {len(doc2_sentences)}") | |
| if similar_sentences: | |
| st.success(f"Found {len(similar_sentences)} similar sentences!") | |
| for match in similar_sentences: | |
| doc1_index, doc2_index, score, sent1, sent2 = match | |
| st.markdown(f"**Document 1 Sentence {doc1_index + 1}:** {sent1}") | |
| st.markdown(f"**Document 2 Sentence {doc2_index + 1}:** {sent2}") | |
| st.markdown(f"**Similarity Score:** {score:.2f}") | |
| st.markdown("---") | |
| else: | |
| st.info("No significantly similar sentences found.") | |
| else: | |
| st.warning("Please upload two documents to compare.") | |
| if __name__ == "__main__": | |
| main() | |