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import os
import streamlit as st
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.llms import HuggingFaceHub
from langchain.schema import Document
import requests
from io import BytesIO
import fitz # PyMuPDF
from dotenv import load_dotenv
# Set device based on GPU availability
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load environment variables from .env file
load_dotenv()
# Hugging Face API token should now be loaded from the .env file
# Explicitly set the Hugging Face API token from the environment variable
os.environ["HUGGINGFACEHUB_API_TOKEN"] = os.getenv("HUGGINGFACE_API_TOKEN")
# Load embeddings with Hugging Face API
embedding_model = "sentence-transformers/all-MiniLM-L6-v2"
embeddings = HuggingFaceEmbeddings(model_name=embedding_model) # Removed api_key parameter
# Set up the text generation model using Hugging Face Hub
model_name = "google/flan-t5-small" # Use a smaller model to reduce response time and cost
llm = HuggingFaceHub(repo_id=model_name, huggingfacehub_api_token=os.getenv("HUGGINGFACEHUB_API_TOKEN"), model_kwargs={"max_length": 256, "temperature": 0.7})
# Streamlit interface
def main():
st.title("Chat with Multiple PDFs")
st.write("Upload PDF files and chat with them.")
# File uploader
uploaded_files = st.file_uploader("Upload PDF Files", accept_multiple_files=True, type=["pdf"])
if uploaded_files:
# Load PDF documents
documents = []
for uploaded_file in uploaded_files:
pdf_content = BytesIO(uploaded_file.read())
doc = fitz.open(stream=pdf_content, filetype="pdf") # Open PDF with PyMuPDF
text = ""
for page in doc:
text += page.get_text() # Extract text from each page
doc.close()
# Create Document instance with page content
documents.append(Document(page_content=text, metadata={"file_name": uploaded_file.name}))
# Split documents into manageable chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
chunks = text_splitter.split_documents(documents)
# Embed document chunks into vector store
vector_store = FAISS.from_documents(chunks, embeddings)
# User query input
st.write("You can now start chatting with your PDFs!")
user_input = st.text_input("Ask a question:")
if user_input:
# Perform similarity search on the vector store
docs = vector_store.similarity_search(user_input, k=3)
# Concatenate retrieved docs into a single prompt
prompt = "\n".join([doc.page_content for doc in docs]) + "\n\n" + user_input
# Generate response using the Hugging Face API
try:
response = llm(prompt)
st.write(response)
except requests.exceptions.RequestException as e:
st.error(f"Error connecting to Hugging Face API: {e}")
if __name__ == "__main__":
main()
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