How to Build a Simple Chatbot with a Pre-trained LLM
💬 How to Build a Simple Chatbot with a Pre-trained LLM
This guide walks you through the steps using Python, OpenAI API, or Hugging Face Transformers.
🧰 Tools You'll Need
Option A: Use OpenAI API (easiest)
Python
openai Python package
OpenAI API key
Option B: Use Hugging Face Transformers (runs locally)
Python
transformers and torch packages
A pre-trained LLM from Hugging Face
🛠️ Step-by-Step (Option A – Using OpenAI API)
🔹 Step 1: Install the OpenAI SDK
pip install openai
🔹 Step 2: Get Your API Key
Sign up at https://platform.openai.com/
Get your API key from the API Keys page
🔹 Step 3: Write a Simple Python Chatbot
import openai
# Set your OpenAI API key
openai.api_key = "your-api-key-here"
def chat_with_gpt(prompt):
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo", # or gpt-4 if available
messages=[
{"role": "system", "content": "You are a helpful chatbot."},
{"role": "user", "content": prompt}
]
)
return response['choices'][0]['message']['content']
# Example usage
while True:
user_input = input("You: ")
if user_input.lower() in ["exit", "quit"]:
break
reply = chat_with_gpt(user_input)
print("Bot:", reply)
✅ Done! You now have a working chatbot using GPT.
🛠️ Step-by-Step (Option B – Using Hugging Face Locally)
🔹 Step 1: Install Required Libraries
pip install transformers torch
🔹 Step 2: Load a Pre-trained Model
from transformers import pipeline
# Load a conversational pipeline
chatbot = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.1")
# Chat loop
while True:
user_input = input("You: ")
if user_input.lower() in ["exit", "quit"]:
break
response = chatbot(user_input, max_new_tokens=100, do_sample=True)
print("Bot:", response[0]['generated_text'])
📝 Note: Large models like Mistral, LLaMA, or Falcon require a good GPU. You can use Google Colab or Hugging Face Spaces if you don’t have local hardware.
🌐 Optional: Add a Web Interface with Streamlit
pip install streamlit
# save as chatbot_app.py
import openai
import streamlit as st
openai.api_key = "your-api-key-here"
st.title("💬 Simple Chatbot")
user_input = st.text_input("You:")
if user_input:
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are a helpful chatbot."},
{"role": "user", "content": user_input}
]
)
st.text_area("Bot:", response['choices'][0]['message']['content'], height=200)
Run it:
streamlit run chatbot_app.py
🧠 Tips for Customizing Your Chatbot
Change the system prompt to give it a personality:
“You are a sarcastic chatbot.”
“You are a personal tutor for 10th grade math.”
Add memory by saving previous interactions in the messages list.
Add context by feeding in user profile data or past queries.
🚀 Bonus: Try Pre-trained Chatbots on Hugging Face
No code needed — just use these:
Hugging Face Chat UI
Try models like mistralai/Mistral-7B-Instruct, google/flan-t5-xl, or meta-llama/Llama-2-7b-chat
📦 Summary
Approach Best For Pros Cons
OpenAI API Quick & easy chatbot Fast setup, reliable results Requires internet & API key
Hugging Face + Transformers Running locally Free after setup, customizable Requires more compute
Streamlit UI Simple web app Interactive front-end Extra steps to deploy
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