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import gradio as gr
from langchain_community.document_loaders import PyPDFLoader
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import FAISS
from langchain_groq import ChatGroq
from langchain_core.prompts import ChatPromptTemplate
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from dotenv import load_dotenv
import os
# Load environment variables
load_dotenv()
groq_api_key = os.getenv("GROQ_API_KEY")
# Initialize embedding model and LLM
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
llm = ChatGroq(model_name="Llama3-8b-8192", groq_api_key=groq_api_key)
# Prompt template
prompt = ChatPromptTemplate.from_messages([
("system", "You are an assistant for question-answering tasks."
"Answer the question in detail."
"You are an assistant for question-answering tasks."
"act as a Q/A chatbot."
"answer concise and detailed."
"\n\n"
"{context}"
),
("human", "{input}")
])
# Store the chain globally
rag_chain = None
# Upload and process PDF
def handle_upload(pdf_file):
global rag_chain
try:
loader = PyPDFLoader(pdf_file.name)
docs = loader.load()
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=30)
split_docs = splitter.split_documents(docs)
vectorstore = FAISS.from_documents(split_docs, embeddings)
retriever = vectorstore.as_retriever()
qa_chain = create_stuff_documents_chain(llm, prompt)
rag_chain = create_retrieval_chain(retriever, qa_chain)
return "✅ PDF uploaded. Ask your question below."
except Exception as e:
return f"❌ Error: {str(e)}"
# Answer user question
def ask_question(question):
if not rag_chain:
return "⚠️ Please upload a PDF first."
try:
result = rag_chain.invoke({"input": question})
return result["answer"]
except Exception as e:
return f"❌ Error generating answer: {str(e)}"
# Gradio UI
with gr.Blocks() as demo:
gr.Markdown("## 🤖 Simple PDF Q&A ChatBot")
with gr.Row():
pdf_file = gr.File(label="📄 Upload PDF", file_types=[".pdf"])
upload_btn = gr.Button("Upload")
upload_status = gr.Textbox(label="Status", interactive=False)
question = gr.Textbox(label="Ask a Question", placeholder="e.g., What is this paper about?")
answer = gr.Textbox(label="Answer", lines=10)
upload_btn.click(handle_upload, inputs=[pdf_file], outputs=[upload_status])
question.submit(ask_question, inputs=question, outputs=answer)
# Run the app
demo.launch()
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