Building a Q&A Application with Streamlit and the Gemini API

 

Have you ever wanted to create your own interactive Q&A application powered by AI? Look no further! This blog post will guide you through building a simple application using Streamlit and Google's Generative AI platform, specifically the Gemini API.

What is Streamlit?

Streamlit is an open-source Python library that allows you to rapidly create web apps. Streamlit boasts a simple syntax, making it easy to craft user interfaces with minimal coding.

What is the Gemini API?

The Gemini API is part of Google's Generative AI suite, offering access to powerful large language models. These models can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.

Building the Q&A App

Let's break down the provided code and understand how it works:

1. Importing Libraries:

   • dotenv: This library helps load environment variables from a .env file, a secure way to store your API key.
   • streamlit as st: This line imports Streamlit and assigns it the alias st for convenience.
   • os: This library provides access to the operating system, allowing us to use environment variables.
   • google.generativeai as genai: This imports the generative AI library from Google.

2. API Key Configuration:

   • load_dotenv(): This line loads environment variables from the .env file.
   • genai.configure(api_key=os.getenv("GOOGLE_API_KEY")): This configures the Gemini API using your API key, which should be stored securely in the .env file.

3. Loading the Gemini Model:

   • model=genai.GenerativeModel("gemini-pro"): This line creates a generative model instance using the "gemini-pro" model. You can explore other models available in the API documentation.

4. get_gemini_response Function:

   • This function takes a question as input and uses the loaded model to generate a response using the generate_content method.
   • The function then returns the text portion of the generated response.

5. Streamlit App Initialization:

   • st.set_page_config(page_title="Q&A Demo"): This line sets the title of your web app displayed in the browser tab.
   • st.header("Gemini Application"): This creates a heading for your app.

6. User Input and Button:

   • input=st.text_input("Input: ",key="input"): This line creates a text input box where users can type their questions. The key argument ensures Streamlit remembers the state of the input field.
   • submit=st.button("Ask the question"): This line creates a button that users can click to trigger the response generation.

7. Responding to User Input:

   • The if submit: block checks if the button is clicked.
   • If clicked, it calls the get_gemini_response function with the user's question as input.
   • The response from the function is then displayed below a subheading using st.write.

Running the App

1. Save the code as a Python file (e.g., app.py).
2. Make sure you have Streamlit and the google-generativeai library installed (pip install streamlit google-generativeai).
3. Create a .env file in the same directory and add your API key as GOOGLE_API_KEY="YOUR_API_KEY" (replace with your actual key).
4. Run the script using streamlit run app.py.

Deployment (Optional)

While Streamlit allows you to run the app locally, you can deploy it to a cloud platform for wider accessibility. Streamlit offers deployment options, or you can explore cloud providers like Google Cloud Platform or Amazon Web Services.

Conclusion

This code provides a basic example of building a Q&A application with Streamlit and the Gemini API. With further development, you can customize the app by:

• Adding more functionalities like handling different user queries.
• Styling the user interface using Streamlit's theming options.
• Integrating error handling for a more robust user experience.

By leveraging Streamlit and the Gemini API, you can create interactive AI-powered applications with minimal coding effort. This paves the way for exciting possibilities in web development!

To complement this tutorial, you can experiment with the code yourself! I've uploaded the complete code to GitHub here. This repository provides a clear structure and all the necessary files to get your Q&A application running. Feel free to clone the repository, follow the instructions in the README, and start interacting with your own AI-powered question answerer!