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#ref: https://www.youtube.com/watch?v=3ZDVmzlM6Nc
import os
import chromadb
import streamlit as st
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_chroma import Chroma
from langchain_groq import ChatGroq
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from PyPDF2 import PdfReader
from groq import Groq
# Clear ChromaDB cache to fix tenant issue
chromadb.api.client.SharedSystemClient.clear_system_cache()
# Ensure required environment variables are set
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
if not GROQ_API_KEY:
st.error("GROQ_API_KEY is not set. Please configure it in environment variables.")
st.stop()
# Initialize Groq Client for transcription and LLM
groq_client = Groq(api_key=GROQ_API_KEY)
llm = ChatGroq(model="llama-3.1-70b-versatile", temperature=0, groq_api_key=GROQ_API_KEY)
# Function to process PDFs and set up the vectorstore
def process_and_store_pdfs(uploaded_files):
texts = []
for uploaded_file in uploaded_files:
reader = PdfReader(uploaded_file)
for page in reader.pages:
texts.append(page.extract_text())
embeddings = HuggingFaceEmbeddings()
vectorstore = Chroma.from_texts(texts, embedding=embeddings, persist_directory="vector_db_dir")
return vectorstore
# Function to set up the chat chain
def chat_chain(vectorstore):
retriever = vectorstore.as_retriever()
memory = ConversationBufferMemory(output_key="answer", memory_key="chat_history", return_messages=True)
chain = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=retriever,
chain_type="stuff",
memory=memory,
verbose=True,
return_source_documents=True
)
return chain
# Function to transcribe audio using Groq's Whisper
def transcribe_audio(file_path):
"""Transcribe audio using Groq's Whisper model."""
with open(file_path, "rb") as file:
transcription = groq_client.audio.transcriptions.create(
file=(file_path, file.read()),
model="distil-whisper-large-v3-en",
response_format="json",
language="en"
)
# Access the text attribute
return transcription.text
# Streamlit UI
st.title("Chat with PDFs via Audio 🎙️📚")
uploaded_files = st.file_uploader("Upload PDF Files", accept_multiple_files=True, type=["pdf"])
if uploaded_files:
vectorstore = process_and_store_pdfs(uploaded_files)
chain = chat_chain(vectorstore)
st.success("PDFs processed! Ready to chat.")
input_method = st.radio("Choose Input Method", ["Text Input", "Audio File Upload"])
# Text Input Mode
if input_method == "Text Input":
query = st.text_input("Ask your question:")
if query:
with st.spinner("Thinking..."):
response = chain({"question": query})["answer"]
st.write(f"**Response:** {response}")
# Audio Input Mode (File Upload)
elif input_method == "Audio File Upload":
uploaded_audio = st.file_uploader("Upload an audio file (.wav, .mp3)", type=["wav", "mp3"])
if uploaded_audio:
audio_file_path = "uploaded_audio.wav"
with open(audio_file_path, "wb") as f:
f.write(uploaded_audio.read())
st.audio(audio_file_path, format="audio/wav")
st.write("Transcribing audio...")
transcription = transcribe_audio(audio_file_path)
st.write(f"**You said:** {transcription}")
with st.spinner("Generating response..."):
response = chain({"question": transcription})["answer"]
st.write(f"**Response:** {response}")
else:
st.info("Please upload PDF files to start chatting.")
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