YouTube Tutorials Playlist: JustCodeIt/Streamlit 101
In the first part of this tutorial, we explored the basics of creating a simple Streamlit chatbot that mirrors user input. We're taking a significant leap forward by integrating OpenAI's powerful language models to make our chatbot more interactive and intelligent. This guide will walk you through the process of enhancing your Streamlit chatbot with OpenAI, enabling it to provide dynamic responses to user queries.
Prerequisites
- Completion of Part 1 of this tutorial.
- An OpenAI API key. You can obtain one by creating an account on the OpenAI platform.
- I am familiar with handling API requests in Python.
Step 1: OpenAI API Setup
Before diving into the code, ensure you can access the OpenAI API by signing up on their platform and obtaining an API key. This key will allow your application to communicate with OpenAI's servers and use their language models.
Step 2: Updating Your App with OpenAI Integration
Modify Your Python Script
Open your app.py or the main script of your Streamlit application. We'll be making several additions to incorporate OpenAI's language models.
-
Import OpenAI Library
First, install the OpenAI Python package by running
pip install openaiin your terminal. Then, add the following import statements to your script:
import os
import streamlit as st
import openai
- Configure OpenAI API Key Securely store your OpenAI API key using environment variables or a configuration file. For simplicity, you can also directly assign it in your script (not recommended for production):
openai.api_key = 'your_api_key_here'
- Modify Chatbot Response Logic Replace the simple mirroring logic with a function that sends user input to OpenAI's language model and displays the model's response:
def get_openai_response(user_input):
"""
This function sends the user input to OpenAI's Chat API and returns the model's response.
"""
try:
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo", # Specify the model for chat applications
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": user_input},
]
)
# Extracting the text from the last response in the chat
if response.choices:
return response.choices[0].message['content'].strip()
else:
return "No response from the model."
except Exception as e:
return f"An error occurred: {str(e)}"
# Streamlit app layout
st.title("Your Advanced Streamlit Chatbot")
user_input = st.text_input("What would you like to ask?")
if st.button("Submit"):
if user_input:
chatbot_response = get_openai_response(user_input)
st.write(f"Chatbot: {chatbot_response}")
else:
st.write("Please enter a question or message to get a response.")
Step 3: Testing and Iteration
After integrating OpenAI, it's crucial to test your chatbot extensively. Experiment with different types of queries to see how well the chatbot responds. You may adjust the parameters in the ChatCompletion.create method to fine-tune the responses according to your needs.
Step 4: Further Enhancements
- Customize the Chatbot's Personality: Use the messages parameter to prepend a description of the chatbot's personality or knowledge base, making the interactions more engaging.
- Implement Session State for Contextual Conversations: Utilize Streamlit's session state to remember the context of the conversation, allowing for more coherent and context-aware responses.
- Add More Interactivity: Explore Streamlit's widgets and features to add functionalities like voice input, response options, or multimedia content.
Wrapping Up
By integrating OpenAI's language models, you've transformed your Streamlit chatbot into a dynamic and intelligent conversational agent. This guide has provided you with the foundation to build upon, and there's a vast potential for further enhancements and customization. Dive into the OpenAI documentation and Streamlit's community resources to explore new possibilities and take your chatbot to the next level.
TL;DR Section
If you're looking for a quick guide to creating an advanced Streamlit chatbot using OpenAI's GPT model, here's the condensed version. Make sure to replace your_api_key_here with your actual OpenAI API key.
Note: It's best practice to use environment variables or secure methods to handle your API key in a production environment.
import os
import streamlit as st
import openai
# Load your OpenAI API key from an environment variable
openai.api_key = os.getenv("OPENAI_API_KEY")
def get_openai_response(user_input):
"""
Sends user input to OpenAI's Chat API and returns the model's response.
"""
try:
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo", # Use the model suited for chat applications
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": user_input},
]
)
# Extract the text from the last response in the chat
return response.choices[0].message['content'].strip() if response.choices else "No response from the model."
except Exception as e:
return f"An error occurred: {str(e)}"
# Streamlit app layout
st.title("Your Advanced Streamlit Chatbot")
user_input = st.text_input("What would you like to ask?")
if st.button("Submit"):
chatbot_response = get_openai_response(user_input) if user_input else "Please enter a question or message to get a response."
st.write(f"Chatbot: {chatbot_response}")
Instructions to Run the App:
- Install Streamlit with
pip install streamlitif you haven't already. - Install the OpenAI package with
pip install openai. - Save the code snippet in a file named
app.py. - Set your OpenAI API key in an environment variable or replace
os.getenv("OPENAI_API_KEY")with your key in the code. - Run the app with
streamlit run app.pyin your terminal. - Interact with your chatbot in the browser window that opens.
This streamlined code snippet is all you need to start with an intelligent chatbot in Streamlit, powered by OpenAI's GPT model. Customize and enhance it to fit your needs and explore the vast capabilities of conversational AI.
Up Next: Semantic Search with Vector Databases, Embeddings, and PDF Processing - Part 3
In Part 3 of our tutorial series, we will explore the exciting world of semantic search by enhancing our Streamlit chatbot with advanced AI capabilities. We'll integrate a vector database to store and retrieve information efficiently, utilize embeddings to understand the semantic meaning behind texts and implement PDF processing to extend our chatbot's knowledge base.
Vector Databases and Embeddings
We'll explore how to use vector databases like Weaviate or Pinecone to store embeddings of documents, enabling our chatbot to perform semantic searches. This will allow the chatbot to understand the context and meaning behind user queries, providing more accurate and relevant responses.
PDF Processing
To further empower our chatbot, we'll implement PDF processing. This will enable the chatbot to ingest and understand content from PDF documents, making it possible to answer questions based on a vast array of literature and reference materials.
Building a Semantic Search Chatbot
By the end of Part 3, you'll have a sophisticated Streamlit chatbot capable of semantic search, providing users with information and answers drawn from a rich database of embedded texts and documents. Stay tuned for a deep dive into these advanced features that will take your chatbot to the next level.
P.S. Leave comments with things you would like me to cover next.
If you would like to support me or buy me a beer feel free to join my Patreon jamesbmour
Top comments (0)