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import os
from dotenv import load_dotenv
import fitz # PyMuPDF
from docx import Document
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
import pickle
from langchain_community.llms import HuggingFaceEndpoint
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
import gradio as gr
# Load environment variables from .env
load_dotenv()
# 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")
print(f"API Token: {api_token[:5]}...")
# Initialize the HuggingFace LLM
llm = HuggingFaceEndpoint(
endpoint_url="https://api-inference.huggingface.co/models/gpt2",
model_kwargs={"api_key": api_token}
)
# Initialize the HuggingFace embedding
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 = pickle.load(f)
else:
# Create a new FAISS index if it doesn't exist
index = faiss.IndexFlatL2(embedding_model.get_sentence_embedding_dimension())
with open(index_path, "wb") as f:
pickle.dump(index, f)
# 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
def process_and_query(text):
# Process the text and update FAISS index (similar to the previous code)
sentences = text.split("\n")
embeddings = embedding_model.encode(sentences)
index.add(np.array(embeddings))
# Search the FAISS index
query_embedding = embedding_model.encode([text])
D, I = index.search(np.array(query_embedding), k=5)
top_documents = []
for idx in I[0]:
if idx != -1: # Ensure that a valid index is found
top_documents.append(f"Document {idx}")
# Generate response using LLM (optional)
# You can replace this with your desired LLM interaction logic
response = llm.run(inputs=text, max_length=100, temperature=0.7)["generated_text"]
return {"top_documents": top_documents, "response": response}
# Define the Gradio interface
interface = gr.Interface(
fn=process_and_query,
inputs="textbox",
outputs=["list", "text"],
title="Chatbot with Text Processing and Retrieval",
description="Upload a document (PDF or Word) or enter text to process. The chatbot will retrieve relevant documents and generate a response (optional).",
)
# Launch the Gradio interface
interface.launch()
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