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import os
import tempfile
import gradio as gr
from langchain_community.vectorstores import FAISS
from langchain_groq import ChatGroq
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_core.runnables import RunnablePassthrough
from langchain.document_loaders import PyPDFLoader
from langchain import hub
# Set API key (Replace with your actual key)
os.environ["GROQ_API_KEY"] = "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"
# Initialize LLM and Embeddings
llm = ChatGroq(model="llama3-8b-8192")
model_name = "BAAI/bge-small-en"
hf_embeddings = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs={'device': 'cpu'},
encode_kwargs={'normalize_embeddings': True}
)
# Function to process PDF
def process_pdf(file):
if file is None:
return "Please upload a PDF file."
# Save PDF temporarily
with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
temp_file.write(file)
temp_file_path = temp_file.name
# Load and process PDF
loader = PyPDFLoader(temp_file_path)
docs = loader.load()
# Split text
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
splits = text_splitter.split_documents(docs)
# Create FAISS vector store
vectorstore = FAISS.from_documents(documents=splits, embedding=hf_embeddings)
retriever = vectorstore.as_retriever()
# Load RAG prompt
prompt = hub.pull("rlm/rag-prompt")
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)
# RAG Chain
global rag_chain
rag_chain = (
{"context": retriever | format_docs, "question": RunnablePassthrough()}
| prompt
| llm
)
return "PDF processed successfully! Now ask questions."
# Function to answer queries
def ask_question(query):
if "rag_chain" not in globals():
return "Please upload and process a PDF first."
response = rag_chain.invoke(query).content
return response
# Gradio UI
with gr.Blocks() as demo:
gr.Markdown("# 📄 PDF Chatbot with RAG")
gr.Markdown("Upload a PDF and ask questions!")
pdf_input = gr.File(label="Upload PDF", type="binary")
process_button = gr.Button("Process PDF")
output_message = gr.Textbox(label="Status", interactive=False)
query_input = gr.Textbox(label="Ask a Question")
submit_button = gr.Button("Submit")
response_output = gr.Textbox(label="AI Response")
process_button.click(process_pdf, inputs=pdf_input, outputs=output_message)
submit_button.click(ask_question, inputs=query_input, outputs=response_output)
demo.launch() |