thiru10/Coding_Assistant

1import streamlit as st2import os3import shutil4from dotenv import load_dotenv5from langchain_groq import ChatGroq6from langchain_community.embeddings import SentenceTransformerEmbeddings7from langchain_community.vectorstores import FAISS8from langchain.chains import RetrievalQA9from ingest import main as ingest_data10 11# --- Configuration ---12BASE_DIR = os.path.dirname(os.path.abspath(__file__))13load_dotenv(dotenv_path=os.path.join(BASE_DIR, ".env"))14PERSIST_DIR = os.path.join(BASE_DIR, "db")15 16# --- Caching Functions ---17 18from langchain.prompts import PromptTemplate19 20# --- Prompt Engineering ---21prompt_template = """""22    You are Python programmer, assisting a user in coding choosing algorithms explaining concepts as a assistant and tutor, confident, and concise.23    24    25    You said:26    You are PyTutor, an expert Python programmer and teaching assistant.27    Your role is to help users understand, design, and debug Python code confidently and clearly.28    29    Guidelines:30    31    Be concise, precise, and technically accurate.32    33    Always explain the reasoning behind your code or solution.34    35    When relevant, compare alternative approaches and explain why one is preferred.36    37    Use simple language when explaining complex algorithms.38    39    Prioritize clarity, correctness, and performance in code examples.40    41    Provide step-by-step explanations for concepts or algorithms.42    43    When teaching, use small, runnable Python snippets.44    45    Avoid unnecessary verbosity or overgeneralization — keep answers focused and confident.46    47    Always assume the user is learning Python actively and wants to understand, not just copy.48    49    Tone:50    51    Confident, concise, and instructive — like a skilled mentor guiding a student through real code.52    Context:53    {context}54    55    Question: {question}56    57    Interview Answer:58"""""59QA_CHAIN_PROMPT = PromptTemplate.from_template(prompt_template)60 61# --- Caching Functions ---62 63@st.cache_resource(show_spinner="Connecting to LLM...")64def llm_pipeline():65    """Initializes the Groq LLM pipeline."""66    token = os.getenv("GROQ_API_KEY")67    if not token:68        st.error("GROQ_API_KEY is not set. Please add it to your .env file.")69        st.stop()70 71    try:72        llm = ChatGroq(73            groq_api_key=token,74            model_name="llama-3.1-8b-instant",75            temperature=0.4,76            max_tokens=102477        )78        return llm79    except Exception as e:80        st.error(f"Failed to initialize Groq LLM: {e}")81        st.stop()82 83@st.cache_resource(show_spinner="Loading Knowledge Base...")84def qa_llm(_llm):85    """Initializes the RetrievalQA chain."""86    try:87        embeddings = SentenceTransformerEmbeddings(88            model_name="all-MiniLM-L6-v2",89            model_kwargs={"device": "cpu"}90        )91        92        if not os.path.exists(PERSIST_DIR):93            st.warning("Knowledge base not found. Please build it first.")94            return None95 96        db = FAISS.load_local(PERSIST_DIR, embeddings, allow_dangerous_deserialization=True)97        retriever = db.as_retriever(search_kwargs={'k': 5})98        99        qa = RetrievalQA.from_chain_type(100            llm=_llm,101            chain_type="stuff",102            retriever=retriever,103            return_source_documents=True,104            chain_type_kwargs={"prompt": QA_CHAIN_PROMPT}105        )106        return qa107    except Exception as e:108        st.error(f"Error initializing QA pipeline: {e}")109        return None110 111# --- Main App Logic ---112 113def main():114    st.set_page_config(page_title="Assistant", layout="centered")115    st.markdown("<h1 style='text-align:center;color:blue;'>Coding Assistant  🤖</h1>", unsafe_allow_html=True)116    st.markdown("### Ask me anything about Programming ")117 118    # --- Sidebar for Actions ---119    with st.sidebar:120        st.markdown("## Actions")121        if st.button("Build/Update Knowledge Base"):122            with st.spinner("Ingesting data from all sources... This may take a moment."):123                ingest_data() # Call the main function from ingest.py124            st.success("Knowledge Base is up to date!")125            st.cache_resource.clear() # Clear cache to reload QA chain126            st.rerun()127 128        st.markdown("---_")129        st.markdown("**Sources:**")130        st.markdown("- PDF(s) in `docs/` folder")131 132    # --- Main Chat Interface ---133    if not os.path.exists(PERSIST_DIR):134        st.info("Welcome! Please build the knowledge base using the button in the sidebar to get started.")135        st.stop()136 137    llm = llm_pipeline()138    qa = qa_llm(llm)139 140    if qa is None:141        st.warning("The QA system is not available. Please build the knowledge base.")142        st.stop()143 144    # Pre-defined questions145    example_prompts = [146    "Explain the difference between time and space complexity with examples.",147    "Write an optimized Python solution for the Two Sum problem and explain your approach.",148    "Can you walk me through how to solve a problem using dynamic programming?",149    "Show me how to implement binary search and analyze its complexity.",150    "Compare BFS and DFS in terms of use cases and efficiency.",151    "What are the most common sorting algorithms used in interviews?",152    "Explain how to detect a cycle in a linked list using Floyd’s algorithm.",153    "Give me the Python code for merging two sorted arrays efficiently.",154    "Walk me through solving a problem with recursion and then optimizing it with memoization.",155    "How do I explain my approach to an interviewer for a graph traversal problem?"156    ]157 158    cols = st.columns(2)159    for i, prompt in enumerate(example_prompts):160        if cols[i % 2].button(prompt):161            st.session_state["last_input"] = prompt162 163    # User input164    user_input = st.text_input("Your question:", key="user_input")165    user_question = st.session_state.pop("last_input", None) or user_input166 167    if user_question:168        with st.spinner("Thinking..."):169            try:170                response = qa({"query": user_question})171                answer = response.get("result", "No answer found.")172                173                st.markdown(f"**You:** {user_question}")174                st.markdown(f"**Assistant:** {answer}")175 176                # Display source documents177                with st.expander("See sources"):178                    for doc in response.get("source_documents", []):179                        st.info(f"**Source:** `{os.path.basename(doc.metadata.get('source', 'N/A'))}`")180                        st.text(doc.page_content[:300] + "...")181            except Exception as e:182                st.error(f"An error occurred: {e}")183 184if __name__ == "__main__":185    main()186