Build Your Own AI Chatbot & Automate Your Life With Python and Streamlit

Are you tired of using the same chatbots that feel lifeless and generic? Imagine building your own intelligent assistant—customized, branded, and fully controllable. Whether you’re a tech enthusiast, a developer, or just someone curious, this guide walks you through creating a custom chatbot and automating daily tasks like sending WhatsApp messages to your loved ones.

Welcome to KNCMAP Technologies’ guide to the future—a simple, effective way to kickstart your journey with Python, Streamlit, and OpenRouter AI.

Step-by-Step: Create Your Own Chatbot

Step 1: Prepare Your Development Environment

Use PyCharm or any Python IDE.
Create a new file, e.g., Streamlit.py.

Step 2: Paste the Code



import streamlit as st
import requests
import os
import shelve
from dotenv import load_dotenv

load_dotenv()

st.title("KNCMAP-TECHNOLOGIES")

USER_AVATAR = ""
BOT_AVATAR = ""
API_KEY = os.getenv("KNCMAP_API_KEY")
MODEL_NAME = "mistralai/mistral-7b-instruct"

def load_chat_history():
    with shelve.open("chat_history") as db:
        return db.get("messages", [])

def save_chat_history(messages):
    with shelve.open("chat_history") as db:
        db["messages"] = messages

if "messages" not in st.session_state:
    st.session_state.messages = load_chat_history()

with st.sidebar:
    if st.button("Delete Chat History"):
        st.session_state.messages = []
        save_chat_history([])

for message in st.session_state.messages:
    avatar = USER_AVATAR if message["role"] == "user" else BOT_AVATAR
    with st.chat_message(message["role"], avatar=avatar):
        st.markdown(message["content"])

if prompt := st.chat_input("How can I help?"):
    st.session_state.messages.append({"role": "user", "content": prompt})
    with st.chat_message("user", avatar=USER_AVATAR):
        st.markdown(prompt)

    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }
    body = {
        "model": MODEL_NAME,
        "messages": st.session_state.messages
    }

    try:
        response = requests.post(
            "https://openrouter.ai/api/v1/chat/completions",
            headers=headers,
            json=body
        )
        result = response.json()
        answer = result['choices'][0]['message']['content']
        with st.chat_message("assistant", avatar=BOT_AVATAR):
            st.markdown(answer)
        st.session_state.messages.append({"role": "assistant", "content": answer})
        save_chat_history(st.session_state.messages)
    except Exception as e:
        with st.chat_message("assistant", avatar=BOT_AVATAR):
            st.markdown(f"❌ Error: {str(e)}")

import streamlit as st

import requests

import os

import shelve

from dotenv import load_dotenv

load_dotenv()

st.title("KNCMAP-TECHNOLOGIES")

USER_AVATAR = ""

BOT_AVATAR = ""

API_KEY = os.getenv("KNCMAP_API_KEY")

MODEL_NAME = "mistralai/mistral-7b-instruct"

def load_chat_history():

with shelve.open("chat_history") as db:

return db.get("messages", [])

def save_chat_history(messages):

with shelve.open("chat_history") as db:

db["messages"] = messages

if "messages" not in st.session_state:

st.session_state.messages = load_chat_history()

with st.sidebar:

if st.button("Delete Chat History"):

st.session_state.messages = []

save_chat_history([])

for message in st.session_state.messages:

avatar = USER_AVATAR if message["role"] == "user" else BOT_AVATAR

with st.chat_message(message["role"], avatar=avatar):

st.markdown(message["content"])

if prompt := st.chat_input("How can I help?"):

st.session_state.messages.append({"role": "user", "content": prompt})

with st.chat_message("user", avatar=USER_AVATAR):

st.markdown(prompt)

headers = {

"Authorization": f"Bearer {API_KEY}",

"Content-Type": "application/json"

}

body = {

"model": MODEL_NAME,

"messages": st.session_state.messages

}

try:

response = requests.post(

"https://openrouter.ai/api/v1/chat/completions",

headers=headers,

json=body

)

result = response.json()

answer = result['choices'][0]['message']['content']

with st.chat_message("assistant", avatar=BOT_AVATAR):

st.markdown(answer)

st.session_state.messages.append({"role": "assistant", "content": answer})

save_chat_history(st.session_state.messages)

except Exception as e:

with st.chat_message("assistant", avatar=BOT_AVATAR):

st.markdown(f"❌ Error: {str(e)}")

Step 3: Set Up Your `.env` File

Create a .env file in the same directory and add your API key from OpenRouter.ai:



KNCMAP_API_KEY=sk-your-key

KNCMAP_API_KEY=sk-your-key

Step 4: Install Dependencies

In your terminal:



pip install streamlit python-dotenv

pip install streamlit python-dotenv

Step 5: Run Your Bot



streamlit run Streamlit.py

streamlit run Streamlit.py

Your chatbot is now live in your browser!

Modify the Chatbot for Real-Life Use Cases

1. Stock Trading for Grandpa

Want to make your grandfather happy by automating stock insights?

Integrate financial APIs like Yahoo Finance or Alpha Vantage.
Modify the prompt to retrieve stock advice or daily summaries.
Use scheduled tasks to fetch morning reports using schedule or APScheduler.

2. Compete in Gucci’s Fashion Design Challenge

Help your father outshine the competition:

Train your model on fashion datasets.
Use the chatbot to brainstorm fashion themes and generate new ideas.
Integrate image generation with tools like DALL·E or Replicate to create design concepts.

3. Automate Sweet Messages to Your Partner

Nothing says love like a well-timed message. Automate WhatsApp love notes using Python:

Tools You’ll Need:

pywhatkit for sending WhatsApp messages
schedule or APScheduler for time-based automation

Sample Script:



import pywhatkit
import schedule
import time

def send_morning_message():
    pywhatkit.sendwhatmsg("+254712345678", "Good morning, love! ", 5, 0)

def send_day_checkin():
    pywhatkit.sendwhatmsg("+254712345678", "Hope your day is going well! ", 12, 0)

def send_evening_message():
    pywhatkit.sendwhatmsg("+254712345678", "Good night, my heart ❤️", 20, 0)

schedule.every().day.at("05:00").do(send_morning_message)
schedule.every().day.at("12:00").do(send_day_checkin)
schedule.every().day.at("20:00").do(send_evening_message)

while True:
    schedule.run_pending()
    time.sleep(60)

import pywhatkit

import schedule

import time

def send_morning_message():

pywhatkit.sendwhatmsg("+254712345678", "Good morning, love! ", 5, 0)

def send_day_checkin():

pywhatkit.sendwhatmsg("+254712345678", "Hope your day is going well! ", 12, 0)

def send_evening_message():

pywhatkit.sendwhatmsg("+254712345678", "Good night, my heart ❤️", 20, 0)

schedule.every().day.at("05:00").do(send_morning_message)

schedule.every().day.at("12:00").do(send_day_checkin)

schedule.every().day.at("20:00").do(send_evening_message)

while True:

schedule.run_pending()

time.sleep(60)

Conclusion

With just a few lines of code, you’ve built a chatbot, customized it, and even used it for heartfelt communication or competitive advantage. At KNCMAP Technologies, we believe in blending simplicity with impact—and now it’s your turn.

Create. Customize. Automate. ❤️