app.py

import streamlit as st
from langchain.chat_models import ChatOpenAI
from langchain.schema import AIMessage, HumanMessage
from langchain.memory import ConversationBufferMemory

# ---------------------------
# Streamlit UI Config
# ---------------------------
st.set_page_config(page_title="AI Data Science Tutor", layout="wide")
st.title("📊 AI Conversational Data Science Tutor")

# ---------------------------
# Sidebar - Settings
# ---------------------------
st.sidebar.header("⚙️ Settings")

mode = st.sidebar.radio(
    "Choose Tutor Mode:",
    ("Dummy Tutor (No API Key)", "OpenAI Tutor (API Key Required)")
)

if mode == "OpenAI Tutor (API Key Required)":
    openai_api_key = st.sidebar.text_input("Enter your OpenAI API Key:", type="password")
else:
    openai_api_key = None

# ---------------------------
# Initialize Memory
# ---------------------------
if "memory" not in st.session_state:
    st.session_state.memory = ConversationBufferMemory(return_messages=True)

if "chat_history" not in st.session_state:
    st.session_state.chat_history = []

# ---------------------------
# Dummy Tutor Response Logic
# ---------------------------
def dummy_tutor_response(user_query):
    q = user_query.lower()
    if "regression" in q:
        return "📊 Regression is a supervised ML technique used to predict continuous values."
    elif "classification" in q:
        return "🔍 Classification predicts categorical labels, e.g., spam vs not spam."
    elif "neural" in q or "deep learning" in q:
        return "🧠 Neural Networks consist of layers of neurons that learn patterns from data."
    elif "pca" in q:
        return "📉 PCA reduces dimensions while preserving variance."
    elif "accuracy" in q or "precision" in q or "recall" in q:
        return "✅ Accuracy = correct predictions / total. For imbalanced data, use precision, recall, or F1-score."
    elif "clustering" in q:
        return "📌 Clustering groups similar points without labels (unsupervised learning)."
    elif "overfitting" in q:
        return "⚠️ Overfitting means the model memorizes data instead of generalizing."
    else:
        return f"🤔 I didn’t fully get that. Can you rephrase your Data Science question? (You asked: {user_query})"

# ---------------------------
# Get AI Tutor Response
# ---------------------------
def get_tutor_response(user_query):
    if mode == "Dummy Tutor (No API Key)":
        return dummy_tutor_response(user_query)

    if mode == "OpenAI Tutor (API Key Required)" and openai_api_key:
        llm = ChatOpenAI(
            model="gpt-4o-mini",  # You can also try "gpt-4o" or "gpt-4"
            openai_api_key=openai_api_key,
            temperature=0.5
        )

        # Add user message to memory
        st.session_state.memory.chat_memory.add_user_message(user_query)

        # Generate response
        response = llm(st.session_state.memory.chat_memory.messages)

        # Add AI message to memory
        st.session_state.memory.chat_memory.add_ai_message(response.content)

        return response.content

    return "⚠️ Please provide your OpenAI API key in the sidebar."

# ---------------------------
# Chat UI
# ---------------------------
user_query = st.chat_input("Ask me a Data Science question...")

if user_query:
    response = get_tutor_response(user_query)
    st.session_state.chat_history.append(("You", user_query))
    st.session_state.chat_history.append(("Tutor", response))

# Display Chat History
for sender, msg in st.session_state.chat_history:
    if sender == "You":
        st.markdown(f"**👩‍💻 {sender}:** {msg}")
    else:
        st.markdown(f"**🤖 {sender}:** {msg}")