Create app.py

app.py

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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
+
+# Load the tokenizer and model for sentence embeddings
+@st.cache_resource
+def load_model():
+    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
+    return tokenizer, model, sentence_model
+
+# Extract text from a PDF file
+def extract_text_from_pdf(pdf_file):
+    pdf_reader = PyPDF2.PdfReader(pdf_file)
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+    return text
+
+# Extract text from a Word document
+def extract_text_from_word(docx_file):
+    doc = Document(docx_file)
+    text = ""
+    for paragraph in doc.paragraphs:
+        text += paragraph.text + "\n"
+    return text
+
+# Compare sentences for similarity
+def compare_sentences(doc1_sentences, doc2_sentences, sentence_model):
+    similar_sentences = []
+    for i, sent1 in enumerate(doc1_sentences):
+        best_match = None
+        best_score = 0
+        for j, sent2 in enumerate(doc2_sentences):
+            score = util.pytorch_cos_sim(sentence_model.encode(sent1), sentence_model.encode(sent2)).item()
+            if score > best_score:  # Higher similarity score
+                best_score = score
+                best_match = (i, j, score, sent1, sent2)
+        if best_match and best_score > 0.6:  # Threshold for similarity
+            similar_sentences.append(best_match)
+    return similar_sentences
+
+# Streamlit UI
+def main():
+    st.title("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
+        text1 = extract_text_from_pdf(uploaded_file1) if uploaded_file1.name.endswith(".pdf") else extract_text_from_word(uploaded_file1)
+        text2 = extract_text_from_pdf(uploaded_file2) if uploaded_file2.name.endswith(".pdf") else extract_text_from_word(uploaded_file2)
+
+        # Split text into sentences
+        doc1_sentences = text1.split('. ')
+        doc2_sentences = text2.split('. ')
+
+        # Load model
+        tokenizer, model, sentence_model = load_model()
+
+        # Perform sentence comparison
+        similar_sentences = compare_sentences(doc1_sentences, doc2_sentences, sentence_model)
+
+        # Display results
+        st.header("Comparative Analysis Results")
+        if 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()