AI Chatbot with Gemma 2B & Streamlit in Python 🔥 | Build Your Own LLM App!
Want to build your own private AI chatbot using open-source tools? In this video, you’ll learn how to create a simple conversational chatbot using Gemma 2B LLM with Streamlit in Python.
🧠 Gemma 2B is a lightweight, open LLM by Google, perfect for local/offline AI apps.
💻 We use Streamlit to build an intuitive web interface for real-time chat.
🚀 What You’ll Learn:
How to set up Gemma 2B locally
Building a clean chat UI using Streamlit
Sending prompts and receiving responses from the LLM
Running everything offline without API calls
🛠️ Tech Stack:
Python
Gemma 2B LLM (via Ollama or other loaders)
Streamlit (for frontend UI)
LangChain (optional prompt management)
🎯 Perfect for:
AI/ML enthusiasts
Developers exploring local LLMs
Anyone looking to create their own private chatbot
👍 Like | 💬 Comment | 🔔 Subscribe
#GemmaLLM #AIChatbot #StreamlitApp #PythonAI #OfflineLLM #Gemma2B #LocalAI #opensourceai
Source code:
import streamlit as st
import requests
OLLAMA_URL = "http://localhost:11434/api/chat"
MODEL = "gemma:2b" # Use the memory-friendly Gemma 2B model
st.set_page_config(page_title="Gemma Chatbot", page_icon="🤖")
st.title("🤖 AI Chatbot using Gemma 2B (Offline with Ollama)")
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Input box
user_input = st.chat_input("Say something...")
if user_input:
# Display user message
st.chat_message("user").markdown(user_input)
st.session_state.messages.append({"role": "user", "content": user_input})
# Prepare Ollama API payload
payload = {
"model": MODEL,
"messages": st.session_state.messages,
"stream": False
}
try:
response = requests.post(OLLAMA_URL, json=payload)
response.raise_for_status()
reply = response.json()["message"]["content"]
# Display assistant reply
st.chat_message("assistant").markdown(reply)
st.session_state.messages.append({"role": "assistant", "content": reply})
except Exception as e:
st.error(f"Failed to get response from Ollama: {e}")
Видео AI Chatbot with Gemma 2B & Streamlit in Python 🔥 | Build Your Own LLM App! канала Shaileshkumar Singh
🧠 Gemma 2B is a lightweight, open LLM by Google, perfect for local/offline AI apps.
💻 We use Streamlit to build an intuitive web interface for real-time chat.
🚀 What You’ll Learn:
How to set up Gemma 2B locally
Building a clean chat UI using Streamlit
Sending prompts and receiving responses from the LLM
Running everything offline without API calls
🛠️ Tech Stack:
Python
Gemma 2B LLM (via Ollama or other loaders)
Streamlit (for frontend UI)
LangChain (optional prompt management)
🎯 Perfect for:
AI/ML enthusiasts
Developers exploring local LLMs
Anyone looking to create their own private chatbot
👍 Like | 💬 Comment | 🔔 Subscribe
#GemmaLLM #AIChatbot #StreamlitApp #PythonAI #OfflineLLM #Gemma2B #LocalAI #opensourceai
Source code:
import streamlit as st
import requests
OLLAMA_URL = "http://localhost:11434/api/chat"
MODEL = "gemma:2b" # Use the memory-friendly Gemma 2B model
st.set_page_config(page_title="Gemma Chatbot", page_icon="🤖")
st.title("🤖 AI Chatbot using Gemma 2B (Offline with Ollama)")
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Input box
user_input = st.chat_input("Say something...")
if user_input:
# Display user message
st.chat_message("user").markdown(user_input)
st.session_state.messages.append({"role": "user", "content": user_input})
# Prepare Ollama API payload
payload = {
"model": MODEL,
"messages": st.session_state.messages,
"stream": False
}
try:
response = requests.post(OLLAMA_URL, json=payload)
response.raise_for_status()
reply = response.json()["message"]["content"]
# Display assistant reply
st.chat_message("assistant").markdown(reply)
st.session_state.messages.append({"role": "assistant", "content": reply})
except Exception as e:
st.error(f"Failed to get response from Ollama: {e}")
Видео AI Chatbot with Gemma 2B & Streamlit in Python 🔥 | Build Your Own LLM App! канала Shaileshkumar Singh
gemma 2b gemma llm ai chatbot gemma 2b chatbot build chatbot python streamlit chatbot offline ai chatbot local llm private ai chatbot open source llm streamlit gemma gemma llm tutorial chatbot with streamlit gemma 2b streamlit python ai app local chatbot app gemma chatbot tutorial llm chatbot ai with streamlit gemma 2b tutorial
Комментарии отсутствуют
Информация о видео
3 июля 2025 г. 10:30:28
00:09:04
Другие видео канала