app.py

import os
import fitz
from docx import Document
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
import pickle
import gradio as gr
from typing import List
from langchain_community.llms import HuggingFaceEndpoint
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings

# Function to extract text from a PDF file
def extract_text_from_pdf(pdf_path):
    text = ""
    doc = fitz.open(pdf_path)
    for page_num in range(len(doc)):
        page = doc.load_page(page_num)
        text += page.get_text()
    return text

# Function to extract text from a Word document
def extract_text_from_docx(docx_path):
    doc = Document(docx_path)
    text = "\n".join([para.text for para in doc.paragraphs])
    return text

# Initialize the embedding model
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')

# Hugging Face API token
api_token = os.getenv('HUGGINGFACEHUB_API_TOKEN')
if not api_token:
    raise ValueError("HUGGINGFACEHUB_API_TOKEN environment variable is not set")

# Initialize the HuggingFace LLM
llm = HuggingFaceEndpoint(
    endpoint_url="https://api-inference.huggingface.co/models/gpt2",
    model_kwargs={"api_key": api_token}
)

# Initialize the HuggingFace embeddings
embedding = HuggingFaceEmbeddings()

# Load or create FAISS index
index_path = "faiss_index.pkl"
if os.path.exists(index_path):
    with open(index_path, "rb") as f:
        index, document_texts = pickle.load(f)
else:
    # Create a new FAISS index if it doesn't exist
    index = faiss.IndexFlatL2(embedding_model.get_sentence_embedding_dimension())
    document_texts = []
    with open(index_path, "wb") as f:
        pickle.dump((index, document_texts), f)

def upload_files(files):
    global index, document_texts
    for file in files:
        content = file.read()
        if file.name.endswith('.pdf'):
            with open("temp.pdf", "wb") as f:
                f.write(content)
            text = extract_text_from_pdf("temp.pdf")
        elif file.name.endswith('.docx'):
            with open("temp.docx", "wb") as f:
                f.write(content)
            text = extract_text_from_docx("temp.docx")
        else:
            return "Unsupported file format"

        # Process the text and update FAISS index
        sentences = text.split("\n")
        embeddings = embedding_model.encode(sentences)
        index.add(np.array(embeddings))
        document_texts.append(text)

    # Save the updated index and documents
    with open(index_path, "wb") as f:
        pickle.dump((index, document_texts), f)

    return "Files processed successfully"

def query_text(text):
    # Encode the query text
    query_embedding = embedding_model.encode([text])
    
    # Search the FAISS index
    D, I = index.search(np.array(query_embedding), k=5)
    
    top_documents = []
    for idx in I[0]:
        if idx != -1 and idx < len(document_texts):  # Ensure that a valid index is found
            top_documents.append(document_texts[idx])

    return top_documents

# Create Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("## Document Upload and Query System")
    
    with gr.Tab("Upload Files"):
        upload = gr.File(file_count="multiple", label="Upload PDF or DOCX files")
        upload_button = gr.Button("Upload")
        upload_output = gr.Textbox()
        upload_button.click(fn=upload_files, inputs=upload, outputs=upload_output)
    
    with gr.Tab("Query"):
        query = gr.Textbox(label="Enter your query")
        query_button = gr.Button("Search")
        query_output = gr.Textbox()
        query_button.click(fn=query_text, inputs=query, outputs=query_output)

demo.launch()