app.py

import os
from groq import Groq
import torch
import transformers
from transformers import pipeline
from langchain_groq import ChatGroq
from langchain.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceBgeEmbeddings
from langchain.prompts import PromptTemplate
import streamlit as st

GROQ_API_KEY = os.getenv('GROQ_API_KEY')
if not GROQ_API_KEY:
    raise ValueError("GROQ_API_KEY environment variable is not set.")

# Initialize Groq Client
groq_client = Groq(api_key=GROQ_API_KEY)

# Configure Llama 3.2 LLM with Groq
def configure_groq_llm(
    model_name="llama-3.3-70b-versatile",
    temperature=0.7,
    max_tokens=2048
):
    return ChatGroq(
        groq_api_key=GROQ_API_KEY,
        model_name=model_name,
        temperature=temperature,
        max_tokens=max_tokens
    )

# Embedding Configuration
def get_embeddings(model_name="BAAI/bge-base-en"):
    encode_kwargs = {'normalize_embeddings': True}
    return HuggingFaceBgeEmbeddings(
        model_name=model_name,
        encode_kwargs=encode_kwargs
    )

# Prompt Template
def create_llama_prompt():
    template = """
    Use the following context to answer the question:
    Context: {context}
    Question: {question}
    Helpful Answer:"""
    
    return PromptTemplate(
        template=template, 
        input_variables=["context", "question"]
    )

# Initialize Components
embeddings = get_embeddings()
llm = configure_groq_llm()
vectordb = Chroma(persist_directory='db', embedding_function=embeddings)
retriever = vectordb.as_retriever(search_kwargs={"k": 5})
prompt = create_llama_prompt()

# QA Chain Configuration
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=retriever,
    chain_type_kwargs={"prompt": prompt},
    return_source_documents=True
)

# Streamlit Interface
def groq_nlp_chatbot():
    st.title("Groq Llama 3.2 Chatbot")
    
    user_input = st.text_input("Your Question:")
    if user_input:
        try:
            response = qa_chain(user_input)
            st.text_area("Bot's Response:", response['result'])
        except Exception as e:
            st.error(f"Error processing request: {e}")

if __name__ == "__main__":
    groq_nlp_chatbot()