Build a Chatbot in 50 Lines

"""
chatbot.py — a 50-line conversational chatbot with memory.

Run:  python chatbot.py
Env:  export ANTHROPIC_API_KEY=sk-ant-...
"""
import os
import anthropic

client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

# This list is the entire "memory" of the chatbot.
conversation_history: list[dict] = []

SYSTEM_PROMPT = (
    "You are a helpful AI tutor specializing in machine learning. "
    "Be concise, friendly, and clear. If a question is ambiguous, ask "
    "one clarifying question before answering. Use plain language."
)


def chat(user_message: str) -> str:
    """Send a message and get a response, maintaining conversation history."""
    conversation_history.append({"role": "user", "content": user_message})

    # Send the ENTIRE conversation history every time.
    # Trim old messages if we get too long (keep last 20 exchanges = 40 messages).
    response = client.messages.create(
        model="claude-opus-4-5",
        max_tokens=1024,
        system=SYSTEM_PROMPT,
        messages=conversation_history[-40:],
    )

    assistant_message = response.content[0].text
    conversation_history.append({"role": "assistant", "content": assistant_message})
    return assistant_message


if __name__ == "__main__":
    print("Chatbot ready! Type 'quit' to exit, 'reset' to clear history.\n")
    while True:
        user_input = input("You: ").strip()
        if not user_input:
            continue
        if user_input.lower() == "quit":
            print("Goodbye!")
            break
        if user_input.lower() == "reset":
            conversation_history.clear()
            print("Conversation reset.\n")
            continue
        print(f"AI: {chat(user_input)}\n")

# Study buddy
SYSTEM_PROMPT = (
    "You are a patient study buddy helping with ML. Always check the "
    "user's understanding with one question before moving on to the next topic."
)

# Cooking assistant
SYSTEM_PROMPT = (
    "You are an expert chef. Give practical cooking advice. Always suggest "
    "substitutions for ingredients the user may not have."
)

# Senior code reviewer
SYSTEM_PROMPT = (
    "You are a senior software engineer doing code review. Be direct, "
    "specific, and constructive. Flag security, performance, and "
    "readability issues separately."
)

# Interview coach
SYSTEM_PROMPT = (
    "You are an AI interview coach for ML engineering roles. Ask ONE "
    "technical question at a time. After the user answers, give specific "
    "feedback (what was strong, what was missing) before the next question."
)

def chat_stream(user_message: str) -> str:
    """Send a message and stream the response token-by-token."""
    conversation_history.append({"role": "user", "content": user_message})

    full_response = ""
    with client.messages.stream(
        model="claude-opus-4-5",
        max_tokens=1024,
        system=SYSTEM_PROMPT,
        messages=conversation_history[-40:],
    ) as stream:
        for text in stream.text_stream:
            print(text, end="", flush=True)
            full_response += text
    print()  # newline at end

    conversation_history.append({"role": "assistant", "content": full_response})
    return full_response

# app.py — Streamlit web chatbot. Save next to chatbot.py.
import streamlit as st
import anthropic
import os

client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

st.set_page_config(page_title="ML Tutor", page_icon="🤖")
st.title("ML Tutor — built on Claude")

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

# Render history
for msg in st.session_state.messages:
    with st.chat_message(msg["role"]):
        st.markdown(msg["content"])

# Take a new message
if prompt := st.chat_input("Ask anything about ML..."):
    st.session_state.messages.append({"role": "user", "content": prompt})
    with st.chat_message("user"):
        st.markdown(prompt)

    with st.chat_message("assistant"):
        placeholder = st.empty()
        full = ""
        with client.messages.stream(
            model="claude-opus-4-5",
            max_tokens=1024,
            system="You are a helpful ML tutor. Be concise and clear.",
            messages=st.session_state.messages[-40:],
        ) as stream:
            for text in stream.text_stream:
                full += text
                placeholder.markdown(full + "▌")
            placeholder.markdown(full)
    st.session_state.messages.append({"role": "assistant", "content": full})