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844bbbd ea2d73a 0bbd893 844bbbd 0bbd893 844bbbd 0bbd893 d984a4d 844bbbd d984a4d 844bbbd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | import os
import streamlit as st
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.chains import RetrievalQA
from langchain_groq import ChatGroq # NEW import
# -------------------------------
# Sidebar for API key input
# -------------------------------
st.set_page_config(page_title="Groq PDF Chatbot")
st.title("📄 Chat with your PDF using Groq + LLaMA3")
api_key = st.sidebar.text_input("🔑 Enter your Groq API Key", type="password")
if not api_key:
st.warning("Please enter your Groq API key in the sidebar.")
st.stop()
os.environ["GROQ_API_KEY"] = api_key
# -------------------------------
# Load and split the PDF
# -------------------------------
pdf_path = "docs/acca.pdf" # Make sure this file is in your Space
loader = PyPDFLoader(pdf_path)
pages = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
docs = text_splitter.split_documents(pages)
# -------------------------------
# Vector store using FAISS
# -------------------------------
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
vectorstore = FAISS.from_documents(docs, embeddings)
# -------------------------------
# Groq LLM setup
# -------------------------------
llm = ChatGroq(
temperature=0.7,
model_name="llama3-8b-8192", # Make sure to use correct lowercase name
groq_api_key=api_key
)
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
retriever=vectorstore.as_retriever(),
return_source_documents=True
)
# -------------------------------
# User input and response
# -------------------------------
query = st.text_input("Ask a question based on the PDF:")
if query:
with st.spinner("Generating answer..."):
result = qa_chain.invoke(query)
st.subheader("📌 Answer")
st.write(result["result"])
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