Create app.py

app.py

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+from typing import List
+import pandas as pd
+from transformers import AutoTokenizer, AutoModel
+import torch
+from langchain_community.document_loaders import PyPDFLoader
+from IPython.display import display
+import os
+os.system('apt-get install poppler-utils')
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+import streamlit as st
+
+class PDFProcessor:
+    """
+    Class for processing PDF files to extract text content.
+    """
+    def extract_text_from_pdfs(self, file_paths: List[str]) -> List[str]:
+        """
+        Extract text content from a list of PDF files.
+
+        Args:
+            file_paths (List[str]): A list of file paths to the PDF documents.
+
+        Returns:
+            List[str]: A list of text content extracted from the PDF documents.
+        """
+        texts = []
+        for file_path in file_paths:
+            try:
+                loader = PyPDFLoader(file_path)
+                pages = loader.load_and_split()
+
+                for page in pages:
+                    if isinstance(page.page_content, bytes):
+                        text = page.page_content.decode('utf-8', errors='ignore')
+                    elif isinstance(page.page_content, str):
+                        text = page.page_content
+                    else:
+                        print(f"Unexpected type: {type(page.page_content)}")
+                        continue
+                    texts.append(text)
+            except Exception as e:
+                print(f"Failed to process {file_path}: {e}")
+
+        return texts
+
+class EmbeddingsProcessor:
+    """
+    Class for processing text to obtain embeddings using a transformer model.
+    """
+    def __init__(self, model_name: str):
+        """
+        Initialize the EmbeddingsProcessor with a pre-trained model.
+
+        Args:
+            model_name (str): The name of the pre-trained model to use for generating embeddings.
+        """
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.model = AutoModel.from_pretrained(model_name).to('cuda')
+
+    def get_embeddings(self, texts: List[str]) -> np.ndarray:
+        """
+        Generate embeddings for a list of texts.
+
+        Args:
+            texts (List[str]): A list of text strings for which to generate embeddings.
+
+        Returns:
+            np.ndarray: A NumPy array of embeddings for the provided texts.
+        """
+        encoded_input = self.tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
+        encoded_input = {k: v.to('cuda') for k, v in encoded_input.items()}
+        model_output = self.model(**encoded_input)
+        return model_output.last_hidden_state.mean(dim=1).detach().cpu().numpy()
+
+def compute_similarity(template_embeddings: np.ndarray, contract_embeddings: np.ndarray) -> np.ndarray:
+    """
+    Compute cosine similarity between template and contract embeddings.
+
+    Args:
+        template_embeddings (np.ndarray): A NumPy array of template embeddings.
+        contract_embeddings (np.ndarray): A NumPy array of contract embeddings.
+
+    Returns:
+        np.ndarray: A NumPy array of similarity scores between contracts and templates.
+    """
+    return cosine_similarity(contract_embeddings, template_embeddings)
+
+def clear_folder(path):
+    if not os.path.exists(path):
+        os.makedirs(path)  # Create the directory if it doesn't exist
+    for file in os.listdir(path):
+        file_path = os.path.join(path, file)
+        try:
+            if os.path.isfile(file_path):
+                os.unlink(file_path)
+        except Exception as e:
+            print(f"Failed to delete {file_path}: {e}")
+
+def save_uploaded_file(uploaded_file, path):
+    try:
+        with open(os.path.join(path, uploaded_file.name), "wb") as f:
+            f.write(uploaded_file.getbuffer())
+        return True
+    except:
+        return False
+
+# Streamlit UI
+st.title('PDF Similarity Checker')
+
+col1, col2 = st.columns(2)
+
+# Clear the templates and contracts folders before uploading new files
+templates_folder = './templates'
+contracts_folder = './contracts'
+
+clear_folder(templates_folder)
+clear_folder(contracts_folder)
+
+with col1:
+    st.header("Upload Templates")
+    uploaded_files_templates = st.file_uploader("Choose PDF files", accept_multiple_files=True, type=['pdf'])
+    os.makedirs(templates_folder, exist_ok=True)
+    for uploaded_file in uploaded_files_templates:
+        if save_uploaded_file(uploaded_file, templates_folder):
+            st.write(f"Saved: {uploaded_file.name}")
+
+with col2:
+    st.header("Upload Contracts")
+    uploaded_files_contracts = st.file_uploader("Choose PDF files", key="contracts", accept_multiple_files=True, type=['pdf'])
+    os.makedirs(contracts_folder, exist_ok=True)
+    for uploaded_file in uploaded_files_contracts:
+        if save_uploaded_file(uploaded_file, contracts_folder):
+            st.write(f"Saved: {uploaded_file.name}")
+
+model_name = st.selectbox("Select Model", ['sentence-transformers/multi-qa-mpnet-base-dot-v1'], index=0)
+
+if st.button("Compute Similarities"):
+    pdf_processor = PDFProcessor()
+    embedding_processor = EmbeddingsProcessor(model_name)
+
+    # Process templates
+    template_files = [os.path.join(templates_folder, f) for f in os.listdir(templates_folder)]
+    template_texts = [pdf_processor.extract_text_from_pdfs([f])[0] for f in template_files if pdf_processor.extract_text_from_pdfs([f])]
+    template_embeddings = embedding_processor.get_embeddings(template_texts)
+
+    # Process contracts
+    contract_files = [os.path.join(contracts_folder, f) for f in os.listdir(contracts_folder)]
+    contract_texts = [pdf_processor.extract_text_from_pdfs([f])[0] for f in contract_files if pdf_processor.extract_text_from_pdfs([f])]
+    contract_embeddings = embedding_processor.get_embeddings(contract_texts)
+
+    # Compute similarities
+    similarities = compute_similarity(template_embeddings, contract_embeddings)
+
+    # Display results in a table format
+    similarity_data = []
+    for i, contract_file in enumerate(contract_files):
+        row = [i + 1, os.path.basename(contract_file)]  # SI No and contract file name
+        for j in range(len(template_files)):
+            if j < similarities.shape[1] and i < similarities.shape[0]:  # Check if indices are within bounds
+                row.append(f"{similarities[i, j] * 100:.2f}%")  # Format as percentage
+            else:
+                row.append("N/A")  # Handle out-of-bounds indices gracefully
+        similarity_data.append(row)
+
+    # Create a DataFrame for the table
+    columns = ["SI No", "Contract"] + [os.path.basename(template_files[j]) for j in range(len(template_files))]
+    similarity_df = pd.DataFrame(similarity_data, columns=columns)
+
+    # Display maximize option
+    if st.checkbox("Maximize Table View"):
+        st.write("Similarity Scores Table (Maximized):")
+        st.dataframe(similarity_df)  # Maximized view
+    else:
+        st.write("Similarity Scores Table:")
+        st.table(similarity_df.style.hide(axis="index"))  # Normal view
+
+    # Download option
+    csv = similarity_df.to_csv(index=False).encode('utf-8')
+    st.download_button(
+        label="Download Similarity Table as CSV",
+        data=csv,
+        file_name='similarity_scores.csv',
+        mime='text/csv',
+    )